| 1. |
- Pan, S, et al.
(författare)
-
A study on influential factors of occupant window-opening behavior in an office building in China
- 2018
-
Ingår i: Building and Environment. - : Elsevier BV. - 0360-1323 .- 1873-684X. ; 133, s. 41-50
-
Tidskriftsartikel (refereegranskat)abstract
- Occupants often perform many types of behavior in buildings to adjust the indoor thermal environment. In these types, opening/closing the windows, often regarded as window-opening behavior, is more commonly observed because of its convenience. It not only improves indoor air quality to satisfy occupants' requirement for indoor thermal comfort but also influences building energy consumption. To learn more about potential factors having effects on occupants' window-opening behavior, a field study was carried out in an office building within a university in Beijing. Window state (open/closed) for a total of 5 windows in 5 offices on the second floor in 285 days (9.5 months) were recorded daily. Potential factors, categorized as environmental and non-environmental ones, were subsequently identified with their impact on window-opening behavior through logistic regression and Pearson correlation approaches. The analytical results show that occupants' window-opening behavior is more strongly correlated to environmental factors, such as indoor and outdoor air temperatures, wind speed, relative humidity, outdoor FM2.5 concentrations, solar radiation, sunshine hours, in which air temperatures dominate the influence. While the non-environmental factors, i.e. seasonal change, time of day and personal preference, also affects the patterns of window-opening probability. This paper provides solid field data on occupant window opening behavior in China, with high resolutions and demonstrates the way in analyzing and predicting the probability of window-opening behavior. Its discussion into the potential impact factors shall be useful for further investigation of the relationship between building energy consumption and window-opening behavior.
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| 2. |
- Cai, Demin, et al.
(författare)
-
Epigenetic and SP1-mediated regulation is involved in the repression of galactokinase 1 gene in the liver of neonatal piglets born to betaine-supplemented sows
- 2017
-
Ingår i: European Journal of Nutrition. - : Springer Science and Business Media LLC. - 1436-6207 .- 1436-6215. ; 56:5, s. 1899-1909
-
Tidskriftsartikel (refereegranskat)abstract
- Purpose: In this study, we sought to investigate the effects of maternal betaine supplementation on the expression and regulation of GALK1 gene in the liver of neonatal piglets.Methods: Sixteen sows of two groups were fed control or betaine-supplemented diets (3 g/kg), respectively, throughout the pregnancy. Newborn piglets were individually weighed immediately after birth, and one male piglet close to mean body weight from the same litter was selected and killed before suckling. Serum samples of newborn piglets were analyzed for biochemical indexes, hormone and amino acid levels. Liver samples were analyzed for GALK1 expression by real-time PCR and western blotting, while GALK1 regulational mechanism was analyzed by methylated DNA immunoprecipitation, chromatin immunoprecipitation and microRNAs expression.Results: Betaine-exposed neonatal piglets had lower serum concentration of galactose, which was associated with significantly down-regulated hepatic GALK1 expression. The repression of GALK1 mRNA expression was associated with DNA hypermethylation and more enriched repression histone mark H3K27me3 on its promoter. Binding sites of SP1, GR and STAT3 were predicted on GALK1 promoter, and decreased SP1 protein content and lower SP1 binding to GALK1 promoter were detected in the liver of betaine-exposed piglets. Furthermore, the expression of miRNA-149 targeting GALK1 was up-regulated in the liver of betaine-exposed piglets, along with elevated miRNAs-processing enzymes Dicer and Ago2.Conclusions: Our results suggest that maternal dietary betaine supplementation during gestation suppresses GALK1 expression in the liver of neonatal piglets, which involves complex gene regulation mechanisms including DNA methylation, histone modification, miRNAs expression and SP1-mediated transcriptional modulation.
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| 3. |
- Carling, Kenneth, et al.
(författare)
-
An empirical test of the gravity p-median model
- 2012
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- A customer is presumed to gravitate to a facility by the distance to it and the attractiveness of it. However regarding the location of the facility, the presumption is that the customer opts for the shortest route to the nearest facility.This paradox was recently solved by the introduction of the gravity p-median model. The model is yet to be implemented and tested empirically. We implemented the model in an empirical problem of locating locksmiths, vehicle inspections, and retail stores ofv ehicle spare-parts, and we compared the solutions with those of the p-median model. We found the gravity p-median model to be of limited use for the problem of locating facilities as it either gives solutions similar to the p-median model, or it gives unstable solutions due to a non-concave objective function.
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| 4. |
- Carling, Kenneth, et al.
(författare)
-
Distance measure and the p-median problem in rural areas
- 2012
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The p-median model is used to locate P facilities to serve a geographically distributed population. Conventionally, it is assumed that the population patronize the nearest facility and that the distance between the resident and the facility may be measured by the Euclidean distance. Carling, Han, and Håkansson (2012) compared two network distances with the Euclidean in a rural region witha sparse, heterogeneous network and a non-symmetric distribution of thepopulation. For a coarse network and P small, they found, in contrast to the literature, the Euclidean distance to be problematic. In this paper we extend their work by use of a refined network and study systematically the case when P is of varying size (2-100 facilities). We find that the network distance give as gooda solution as the travel-time network. The Euclidean distance gives solutions some 2-7 per cent worse than the network distances, and the solutions deteriorate with increasing P. Our conclusions extend to intra-urban location problems.
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| 5. |
- Carling, Kenneth, et al.
(författare)
-
Distance measure and the p-median problem in rural areas
- 2015
-
Ingår i: Annals of Operations Research. - : Springer. - 0254-5330 .- 1572-9338. ; 226:1, s. 89-99
-
Tidskriftsartikel (refereegranskat)abstract
- The p-median model is used to locate P facilities to serve a geographically distributed population. Conventionally, it is assumed that the population patronize the nearest facility and that the distance between the resident and the facility may be measured by the Euclidean distance. Carling, Han, and Håkansson (2012) compared two network distances with the Euclidean in a rural region with a sparse, heterogeneous network and a non-symmetric distribution of the population. For a coarse network and P small, they found, in contrast to the literature, the Euclidean distance to be problematic. In this paper we extend their work by use of a refined network and study systematically the case when P is of varying size (1-100 facilities). We find that the network distance give as good a solution as the travel-time network. The Euclidean distance gives solutions some 4-10 per cent worse than the network distances, and the solutions tend to deteriorate with increasing P. Our conclusions extend to intra-urban location problems.
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| 6. |
- Carling, Kenneth, et al.
(författare)
-
Does Euclidean distance work well when the p-median model is applied in rural areas?
- 2012
-
Ingår i: Annals of Operations Research. - : Springer Science and Business Media LLC. - 0254-5330 .- 1572-9338. ; 201:1, s. 83-97
-
Tidskriftsartikel (refereegranskat)abstract
- The p-median model is used to locate P centers to serve a geographically distributed population. A cornerstone of such a model is the measure of distance between a service center and demand points, i.e. the location of the population (customers, pupils, patients, and so on). Evidence supports the current practice of using Euclidean distance. However, we find that the location of multiple hospitals in a rural region of Sweden with anon-symmetrically distributed population is quite sensitive to distance measure, and somewhat sensitive to spatial aggregation of demand points.
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| 7. |
- Carling, Kenneth, et al.
(författare)
-
GRASP and statistical bounds for heuristic solutions to combinatorial problems
- 2016
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The quality of a heuristic solution to a NP-hard combinatorial problem is hard to assess. A few studies have advocated and tested statistical bounds as a method for assessment. These studies indicate that statistical bounds are superior to the more widely known and used deterministic bounds. However, the previous studies have been limited to a few metaheuristics and combinatorial problems and, hence, the general performance of statistical bounds in combinatorial optimization remains an open question. This work complements the existing literature on statistical bounds by testing them on the metaheuristic Greedy Randomized Adaptive Search Procedures (GRASP) and four combinatorial problems. Our findings confirm previous results that statistical bounds are reliable for the p-median problem, while we note that they also seem reliable for the set covering problem. For the quadratic assignment problem, the statistical bounds has previously been found reliable when obtained from the Genetic algorithm whereas in this work they found less reliable. Finally, we provide statistical bounds to four 2-path network design problem instances for which the optimum is currently unknown.
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| 8. |
- Carling, Kenneth, et al.
(författare)
-
Measuring CO2 emissions induced by online and brick-and-mortar retailing
- 2014
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- We develop a method for empirically measuring the difference in carbon footprint between traditional and online retailing (“e-tailing”) from entry point to a geographical area to consumer residence. The method only requires data on the locations of brick-and-mortar stores, online delivery points, and residences of the region’s population, and on the goods transportation networks in the studied region. Such data are readily available in most countries, so the method is not country or region specific. The method has been evaluated using data from the Dalecarlia region in Sweden, and is shown to be robust to all assumptions made. In our empirical example, the results indicate that the average distance from consumer residence to a brick-and-mortar retailer is 48.54 km in the studied region, while the average distance to an online delivery point is 6.7 km. The results also indicate that e-tailing increases the average distance traveled from the regional entry point to the delivery point from 47.15 km for a brick-and-mortar store to 122.75 km for the online delivery points. However, as professional carriers transport the products in bulk to stores or online delivery points, which is more efficient than consumers’ transporting the products to their residences, the results indicate that consumers switching from traditional to e-tailing on average reduce their CO2 footprints by 84% when buying standard consumer electronics products.
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| 9. |
- Carling, Kenneth, et al.
(författare)
-
Measuring transport related CO2 emissions induced by online and brick-and-mortar retailing
- 2015
-
Ingår i: Transportation Research Part D. - : Elsevier BV. - 1361-9209 .- 1879-2340. ; 40, s. 28-42
-
Tidskriftsartikel (refereegranskat)abstract
- We develop a method for empirically measuring the difference in transport related carbon footprint between traditional and online retailing (“e-tailing”) from entry point to a geographical area to consumer residence. The method only requires data on the locations of brick-and-mortar stores, online delivery points, and residences of the region’s population, and on the goods transportation networks in the studied region. Such data are readily available in most countries. The method has been evaluated using data from the Dalecarlia region in Sweden, and is shown to be robust to all assumptions made. In our empirical example, the results indicate that the average distance from consumer residence to a brick-and-mortar retailer is 48.54 km in the studied region, while the average distance to an online delivery point is 6.7 km. The results also indicate that e-tailing increases the average distance traveled from the regional entry point to the delivery point from 47.15 km for a brick-and-mortar store to 122.75 km for the online delivery points. However, as professional carriers transport the products in bulk to stores or online delivery points, which is more efficient than consumers’ transporting the products to their residences, the results indicate that consumers switching from traditional to e-tailing on average reduce their transport CO2 footprints by 84% when buying standard consumer electronics products.
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| 10. |
- Carling, Kenneth, et al.
(författare)
-
Methodological issues in applying Location Models to Rural areas
- 2010
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Location Models are usedfor planning the location of multiple service centers in order to serve a geographicallydistributed population. A cornerstone of such models is the measure of distancebetween the service center and a set of demand points, viz, the location of thepopulation (customers, pupils, patients and so on). Theoretical as well asempirical evidence support the current practice of using the Euclidian distancein metropolitan areas. In this paper, we argue and provide empirical evidencethat such a measure is misleading once the Location Models are applied to ruralareas with heterogeneous transport networks. This paper stems from the problemof finding an optimal allocation of a pre-specified number of hospitals in alarge Swedish region with a low population density. We conclude that the Euclidianand the network distances based on a homogenous network (equal travel costs inthe whole network) give approximately the same optimums. However networkdistances calculated from a heterogeneous network (different travel costs indifferent parts of the network) give widely different optimums when the numberof hospitals increases. In terms ofaccessibility we find that the recent closure of hospitals and the in-optimallocation of the remaining ones has increased the average travel distance by 75%for the population. Finally, aggregation the population misplaces the hospitalsby on average 10 km.
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| 11. |
- Carling, Kenneth, et al.
(författare)
-
Testing the gravity p-median model empirically
- 2015
-
Ingår i: Operations Research Perspectives. - : Elsevier. - 2214-7160. ; 2:124
-
Tidskriftsartikel (refereegranskat)abstract
- Regarding the location of a facility, the presumption in the widely used p-median model is that the customer opts for the shortest route to the nearest facility. However, this assumption is problematic on free markets since the customer is presumed to gravitate to a facility by the distance to and the attractiveness of it. The recently introduced gravity p-median model offers an extension to the p-median model that account for this. The model is therefore potentially interesting, although it has not yet been implemented and tested empirically. In this paper, we have implemented the model in an empirical problem of locating vehicle inspections, locksmiths, and retail stores of vehicle spare-parts for the purpose of investigating its superiority to the p-median model. We found, however, the gravity p-median model to be of limited use for the problem of locating facilities as it either gives solutions similar to the p-median model, or it gives unstable solutions due to a non-concave objective function.
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| 12. |
- Carling, Kenneth, et al.
(författare)
-
Var ska sjukhusen ligga?
- 2013
-
Ingår i: Ekonomiska samfundets tidskrift. - 0013-3183 .- 2323-1378. ; :3, s. 165-171
-
Tidskriftsartikel (refereegranskat)abstract
- Denna artikel visar på en metod för att undersöka hur optimal befolkningens fysiska tillgänglighet till sjukvården är. Detta är relevant med tanke på den svenska storregionala omdaningen som säkerligen kommer provocera fram omprövningar av sjukhusens framtida placering.Med Dalarna som exempel fann vi att en ökning från dagens två till tre optimalt lokaliserade sjukhus skulle minska befolkningens genomsnittliga reseavstånd med 25 %.På basis av transportsektorns standardkalkyler för samhällsekonomisk effekter vid resande, samt av kostnader för drift av sjukvård sluter vi dessutom oss till att en komplettering av nuvarande två sjukhus i Dalarna med ett tredje vore samhällsekonomiskt effektivt.
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| 13. |
- Carlucci, S., et al.
(författare)
-
Modeling occupant behavior in buildings
- 2020
-
Ingår i: Building and Environment. - : Elsevier BV. - 0360-1323 .- 1873-684X. ; 174
-
Tidskriftsartikel (refereegranskat)
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| 14. |
|
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| 15. |
- Gu, Yaxiu, et al.
(författare)
-
Techno-economic analysis of a solar photovoltaic/thermal (PV/T) concentrator for building application in Sweden using Monte Carlo method
- 2018
-
Ingår i: Energy Conversion and Management. - : Elsevier BV. - 0196-8904 .- 1879-2227. ; 165, s. 8-24
-
Tidskriftsartikel (refereegranskat)abstract
- The solar energy share in Sweden will grow up significantly in next a few decades. Such transition offers not only great opportunity but also uncertainties for the emerging solar photovoltaic/thermal (PV/T) technologies. This paper therefore aims to conduct a techno-economic evaluation of a reference solar PV/T concentrator in Sweden for building application. An analytical model is developed based on the combinations of Monte Carlo simulation techniques and multi energy-balance/financial equations, which takes into account of the integrated uncertainties and risks of various variables. In the model, 11 essential input variables, i.e. average daily solar irradiance, electrical/thermal efficiency, prices of electricity/heating, operation & management (OM) cost, PV/T capital cost, debt to equity ratio, interest rate, discount rate, and inflation rate, are considered, while the economic evaluation metrics, such as levelized cost of energy (LCOE), net present value (NPV), and payback period (PP), are primarily assessed. According to the analytical results, the mean values of LCOE, NPV and PP of the reference PV/T connector are observed at 1.27 SEK/kW h (0.127 €/kW h), 18,812.55 SEK (1881.255 €) and 10 years during its 25 years lifespan, given the project size at 10.37 m2 and capital cost at 4482–5378 SEK/m2 (448.2–537.8 €/m2). The positive NPV indicates that the investment on the selected PV/T concentrator will be profitable as the projected earnings exceeds the anticipated costs, depending on the NPV decision rule. The sensitivity analysis and the parametric study illustrate that the economic performance of the reference PV/T concentrator in Sweden is mostly proportional to solar irradiance, debt to equity ratio and heating price, but disproportionate to capital cost and discount rate. Together with additional market analysis of PV/T technologies in Sweden, it is expected that this paper could clarify the economic situation of PV/T technologies in Sweden and provide a useful model for their further investment decisions, in order to achieve sustainable and low-carbon economics, with an expanded quantitative discussion of the real economic or policy scenarios that may lead to those outcomes.
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| 16. |
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| 17. |
- Han, Mengjie, 1985-, et al.
(författare)
-
A novel reinforcement learning method for improving occupant comfort via window opening and closing
- 2020
-
Ingår i: Sustainable cities and society. - : Elsevier BV. - 2210-6707. ; 61
-
Tidskriftsartikel (refereegranskat)abstract
- An occupant's window opening and closing behaviour can significantly influence the level of comfort in the indoor environment. Such behaviour is, however, complex to predict and control conventionally. This paper, therefore, proposes a novel reinforcement learning (RL) method for the advanced control of window opening and closing. The RL control aims at optimising the time point for window opening/closing through observing and learning from the environment. The theory of model-free RL control is developed with the objective of improving occupant comfort, which is applied to historical field measurement data taken from an office building in Beijing. Preliminary testing of RL control is conducted by evaluating the control method’s actions. The results show that the RL control strategy improves thermal and indoor air quality by more than 90 % when compared with the actual historically observed occupant data. This methodology establishes a prototype for optimally controlling window opening and closing behaviour. It can be further extended by including more environmental parameters and more objectives such as energy consumption. The model-free characteristic of RL avoids the disadvantage of implementing inaccurate or complex models for the environment, thereby enabling a great potential in the application of intelligent control for buildings.
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| 18. |
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| 19. |
- Han, Mengjie, et al.
(författare)
-
A review of reinforcement learning methodologies on control systems for building energy
- 2018
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The usage of energy directly leads to a great amount of consumption of the non-renewable fossil resources. Exploiting fossil resources energy can influence both climate and health via ineluctable emissions. Raising awareness, choosing alternative energy and developing energy efficient equipment contributes to reducing the demand for fossil resources energy, but the implementation of them usually takes a long time. Since building energy amounts to around one-third of global energy consumption, and systems in buildings, e.g. HVAC, can be intervened by individual building management, advanced and reliable control techniques for buildings are expected to have a substantial contribution to reducing global energy consumptions. Among those control techniques, the model-free, data-driven reinforcement learning method seems distinctive and applicable. The success of the reinforcement learning method in many artificial intelligence applications has brought us an explicit indication of implementing the method on building energy control. Fruitful algorithms complement each other and guarantee the quality of the optimisation. As a central brain of smart building automation systems, the control technique directly affects the performance of buildings. However, the examination of previous works based on reinforcement learning methodologies are not available and, moreover, how the algorithms can be developed is still vague. Therefore, this paper briefly analyses the empirical applications from the methodology point of view and proposes the future research direction.
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| 20. |
- Han, Mengjie, 1985-, et al.
(författare)
-
An Approach to Data Acquisition for Urban Building Energy Modeling Using a Gaussian Mixture Model and Expectation-Maximization Algorithm
- 2021
-
Ingår i: Buildings. - : MDPI. - 2075-5309. ; 11:1
-
Tidskriftsartikel (refereegranskat)abstract
- In recent years, a building’s energy performance is becoming uncertain because of factors such as climate change, the Covid-19 pandemic, stochastic occupant behavior and inefficient building control systems. Sufficient measurement data is essential to predict and manage a building’s performance levels. Assessing energy performance of buildings at an urban scale requires even larger data samples in order to perform an accurate analysis at an aggregated level. However, data are not only expensive, but it can also be a real challenge for communities to acquire large amounts of real energy data. This is despite the fact that inadequate knowledge of a full population will lead to biased learning and the failure to establish a data pipeline. Thus, this paper proposes a Gaussian mixture model (GMM) with an Expectation-Maximization (EM) algorithm that will produce synthetic building energy data. This method is tested on real datasets. The results show that the parameter estimates from the model are stable and close to the true values. The bivariate model gives better performance in classification accuracy. Synthetic data points generated by the models show a consistent representation of the real data. The approach developed here can be useful for building simulations and optimizations with spatio-temporal mapping.
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| 21. |
- Han, Mengjie, et al.
(författare)
-
Comparison and one-stop shopping after big-box retail entry : a spatial difference-in-difference analysis
- 2018
-
Ingår i: Journal of Retailing and Consumer Services. - : Elsevier BV. - 0969-6989 .- 1873-1384. ; 40, s. 175-187
-
Tidskriftsartikel (refereegranskat)abstract
- This paper empirically measures the potential spillover effects of big-box retail entry on the productivity of incumbent retailers in the entry regions, and investigates whether the effects differ depending on 1) if the entry is in a rural or urban area, and 2) if the incumbent retailers are within retail industries selling substitute or complement goods to those found in IKEA. To identify the IKEA-entry effect, a difference-in-difference model is suitable, but traditionally such estimators neglect the possibility that firms’ sales are determined by a process with spatially interactive responses. If ignored, these responses may cause biased estimates of the IKEA entry effect due to spatial heterogeneity of the treatment effect. One objective of this paper is thus to propose a spatial difference-in-difference estimator accounting for possible spatial spillover effects of IKEA entry. Particular emphasis is placed on the development of a suitable weight matrix accounting for the spatial links between firms, where we allow for local spatial interactions such that the outcome of observed units depends both on their own treatment as well as on the treatment of their neighbors. Our results show that for complementary goods retailers (or one-stop shopping retailers) in Haparanda and Kalmar, productivity increased by 35% and 18%, respectively, due to IKEA entry. No statistically significant effects were found for the entries in Karlstad and Gothenburg, indicating that it is mainly incumbents in smaller entry regions that benefit from IKEA entry. Also, for incumbent retailers selling substitute (or comparison shopping) goods no significant effects were found in any of the entry regions, indicating that it is mainly retailers selling complementary goods that benefit from IKEA entry. Finally, our results also show that ignoring the possibility of spatially correlated treatment effects in the regression models reduces the estimated impact of the IKEA entries in Haparanda and Kalmar on productivity in one-stop shopping retail firms with 3% and 0.1% points, respectively. © 2017 Elsevier Ltd
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| 22. |
- Han, Mengjie, 2013-
(författare)
-
Computational study of the step size parameter of the subgradient optimization method
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- The subgradient optimization method is a simple and flexible linear programming iterative algorithm. It is much simpler than Newton's method and can be applied to a wider variety of problems. It also converges when the objective function is non-differentiable. Since an efficient algorithm will not only produce a good solution but also take less computing time, we always prefer a simpler algorithm with high quality. In this study a series of step size parameters in the subgradient equation is studied. The performance is compared for a general piecewise function and a specific p-median problem. We examine how the quality of solution changes by setting five forms of step size parameter.
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| 23. |
- Han, Mengjie, 1985-, et al.
(författare)
-
Generating hourly electricity demand data for large-scale single-family buildings by a decomposition-recombination method
- 2022
-
Ingår i: Energy and Built Environment. - : Elsevier BV. - 2666-1233.
-
Tidskriftsartikel (refereegranskat)abstract
- Household electricity demand has substantial impacts on local grid operation, energy storage and the energy performance of buildings. Hourly demand data at district or urban level helps stakeholders understand the demand patterns from a granular time scale and provides robust evidence in energy management. However, such type of data is often expensive and time-consuming to collect, process and integrate. Decisions built upon smart meter data have to deal with challenges of privacy and security in the whole process. Incomplete data due to confidentiality concerns or system failure can further increase the difficulty of modeling and optimization. In addition, methods using historical data to make predictions can largely vary depending on data quality, local building environment, and dynamic factors. Considering these challenges, this paper proposes a statistical method to generate hourly electricity demand data for large-scale single-family buildings by decomposing time series data and recombining them into synthetics. The proposed method used public data to capture seasonality and the distribution of residuals that fulfill statistical characteristics. A reference building was used to provide empirical parameter settings and validations for the studied buildings. An illustrative case in a city of Sweden using only annual total demand was presented for deploying the proposed method. The results showed that the proposed method can mimic reality well and represent a high level of similarity to the real data. The average monthly error for the best month reached 15.9% and the best one was below 10% among 11 tested months. Less than 0.6% improper synthetic values were found in the studied region.
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| 24. |
- Han, Mengjie, 1985-, et al.
(författare)
-
Generating Hourly Electricity Demand Data for Large-Scale Single-Family Buildings by a Decomposition–Recombination Method
- 2023
-
Ingår i: Future Urban Energy System for Buildings. - Singapore : Springer Nature. - 9789819912216 - 9789819912223 ; , s. 331-354
-
Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Household electricity demand has substantial impacts on local grid operation, energy storage, and the energy performance of buildings. Hourly demand data at district or urban level helps stakeholders understand the demand patterns from a granular time scale and provides robust evidence in energy management. However, such type of data is often expensive and time-consuming to collect, process, and integrate. Decisions built upon smart meter data have to deal with challenges of privacy and security in the whole process. Incomplete data due to confidentiality concerns or system failure can further increase the difficulty of modeling and optimization. In addition, methods using historical data to make predictions can largely vary depending on data quality, local building environment, and dynamic factors. Considering these challenges, this chapter proposes a statistical method to generate hourly electricity demand data for large-scale single-family buildings by decomposing time series data and recombining them into synthetics. The proposed method used public data to capture seasonality and the distribution of residuals that fulfill statistical characteristics. A reference building was used to provide empirical parameter settings and validations for the studied buildings. An illustrative case in a city of Sweden using only annual total demand was presented for deploying the proposed method. The results showed that the proposed method can mimic reality well and represent a high level of similarity to the real data. The average monthly error for the best month reached 15.9% and the best one was below 10% among 11 tested months. Less than 0.6% improper synthetic values were found in the studied region. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.
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| 25. |
- Han, Mengjie, 1985-, et al.
(författare)
-
GRASP and Statistical Bounds for Heuristic Solutions to Combinatorial Problems
- 2019
-
Ingår i: International Journal of Management and Applied Science. - : Institute of Research and Journals (IRAJ). - 2394-7926. ; 5:8, s. 113-119
-
Tidskriftsartikel (refereegranskat)abstract
- The quality of a heuristic solution to a NP-hard combinatorial problem is hard to assess. A few studies have advocated and tested statistical bounds as a method for assessment. These studies indicate that statistical bounds are superior to the more widely known and used deterministic bounds. However, the previous studies have been limited to a few heuristics and combinatorial problems and, hence, the general performance of statistical bounds in combinatorial optimization remains an open question. This work complements the existing literature on statistical bounds by testing them on the metaheuristic Greedy Randomized Adaptive Search Procedures (GRASP) and four combinatorial problems. Our findings confirm previous results that statistical bounds are reliable for the p-median problem, while we note that they also seem reliable for the set covering problem. For the quadratic assignment problem, the statistical bounds have previously been found reliable when obtained from the Genetic algorithm whereas in this work they have been found less reliable. Finally, we provide statistical bounds to four 2-path network design problem instances for which the optimum is currently unknown.
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| 26. |
- Han, Mengjie, 1985-
(författare)
-
Heuristic optimization of the p-median problem and population re-distribution
- 2013
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis contributes to the heuristic optimization of the p-median problem and Swedish population redistribution. The p-median model is the most representative model in the location analysis. When facilities are located to a population geographically distributed in Q demand points, the p-median model systematically considers all the demand points such that each demand point will have an effect on the decision of the location. However, a series of questions arise. How do we measure the distances? Does the number of facilities to be located have a strong impact on the result? What scale of the network is suitable? How good is our solution? We have scrutinized a lot of issues like those. The reason why we are interested in those questions is that there are a lot of uncertainties in the solutions. We cannot guarantee our solution is good enough for making decisions. The technique of heuristic optimization is formulated in the thesis. Swedish population redistribution is examined by a spatio-temporal covariance model. A descriptive analysis is not always enough to describe the moving effects from the neighbouring population. A correlation or a covariance analysis is more explicit to show the tendencies. Similarly, the optimization technique of the parameter estimation is required and is executed in the frame of statistical modeling.
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| 27. |
- Han, Mengjie, et al.
(författare)
-
How do different densities in a network affect the optimal location of service centers?
- 2013
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The p-median problem is often used to locate p service centers by minimizing their distances to a geographically distributed demand (n). The optimal locations are sensitive to geographical context such as road network and demand points especially when they are asymmetrically distributed in the plane. Most studies focus on evaluating performances of the p-median model when p and n vary. To our knowledge this is not a very well-studied problem when the road network is alternated especially when it is applied in a real world context. The aim in this study is to analyze how the optimal location solutions vary, using the p-median model, when the density in the road network is alternated. The investigation is conducted by the means of a case study in a region in Sweden with an asymmetrically distributed population (15,000 weighted demand points), Dalecarlia. To locate 5 to 50 service centers we use the national transport administrations official road network (NVDB). The road network consists of 1.5 million nodes. To find the optimal location we start with 500 candidate nodes in the network and increase the number of candidate nodes in steps up to 67,000. To find the optimal solution we use a simulated annealing algorithm with adaptive tuning of the temperature. The results show that there is a limited improvement in the optimal solutions when nodes in the road network increase and p is low. When p is high the improvements are larger. The results also show that choice of the best network depends on p. The larger p the larger density of the network is needed.
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| 28. |
- Han, Mengjie, et al.
(författare)
-
How do neighbouring populations affect local population change over time?
- 2013
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This study covers a period when society changed from a pre-industrial agricultural society to a post-industrial service-producing society. Parallel with this social transformation, major population changes took place. In this study, we analyse how local population changes are affected by neighbouring populations. To do so we use the last 200 years of local population change that redistributed population in Sweden. We use literature to identify several different processes and spatial dependencies in the redistribution between a parish and its surrounding parishes. The analysis is based on a unique unchanged historical parish division, and we use an index of local spatial correlation to describe different kinds of spatial dependencies that have influenced the redistribution of the population. To control inherent time dependencies, we introduce a non-separable spatial temporal correlation model into the analysis of population redistribution. Hereby, several different spatial dependencies can be observed simultaneously over time. The main conclusions are that while local population changes have been highly dependent on the neighbouring populations in the 19th century, this spatial dependence have become insignificant already when two parishes is separated by 5 kilometres in the late 20th century. Another conclusion is that the time dependency in the population change is higher when the population redistribution is weak, as it currently is and as it was during the 19th century until the start of industrial revolution.
|
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| 29. |
- Han, Mengjie, et al.
(författare)
-
How does data quality in a network affect heuristic solutions?
- 2014
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- To have good data quality with high complexity is often seen to be important. Intuition says that the higher accuracy and complexity the data have the better the analytic solutions becomes if it is possible to handle the increasing computing time. However, for most of the practical computational problems, high complexity data means that computational times become too long or that heuristics used to solve the problem have difficulties to reach good solutions. This is even further stressed when the size of the combinatorial problem increases. Consequently, we often need a simplified data to deal with complex combinatorial problems. In this study we stress the question of how the complexity and accuracy in a network affect the quality of the heuristic solutions for different sizes of the combinatorial problem. We evaluate this question by applying the commonly usedp-median model, which is used to find optimal locations in a network of p supply points that serve n demand points. To evaluate this, we vary both the accuracy (the number of nodes) of the network and the size of the combinatorial problem (p).The investigation is conducted by the means of a case study in a region in Sweden with an asymmetrically distributed population (15,000 weighted demand points), Dalecarlia. To locate 5 to 50 supply points we use the national transport administrations official road network (NVDB). The road network consists of 1.5 million nodes. To find the optimal location we start with 500 candidate nodes in the network and increase the number of candidate nodes in steps up to 67,000 (which is aggregated from the 1.5 million nodes). To find the optimal solution we use a simulated annealing algorithm with adaptive tuning of the temperature. The results show that there is a limitedimprovement in the optimal solutions when the accuracy in the road network increase and the combinatorial problem (lowp) is simple. When the combinatorial problem is complex (large p) the improvements of increasing the accuracy in the road network are much larger. The results also show that choice of the best accuracy of the network depends on the complexity of the combinatorial (varying p) problem.
|
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| 30. |
- Han, Mengjie, et al.
(författare)
-
How does the use of different road networks effect the optimal location of facilities in rural areas?
- 2012
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The p-median problem is often used to locate P service facilities in a geographically distributed population. Important for the performance of such a model is the distance measure.Distance measure can vary if the accuracy of the road network varies. The rst aim in this study is to analyze how the optimal location solutions vary, using the p-median model, when the road network is alternated. It is hard to nd an exact optimal solution for p-median problems. Therefore, in this study two heuristic solutions are applied, simulating annealing and a classic heuristic. The secondary aim is to compare the optimal location solutions using dierent algorithms for large p-median problem. The investigation is conducted by the means of a case study in a rural region with an asymmetrically distributed population, Dalecarlia.The study shows that the use of more accurate road networks gives better solutions for optimal location, regardless what algorithm that is used and regardless how many service facilities that is optimized for. It is also shown that the simulated annealing algorithm not just is much faster than the classic heuristic used here, but also in most cases gives better location solutions.
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| 31. |
- Han, Mengjie, 1985-, et al.
(författare)
-
Intra-urban location of stores and labour turnover in retail
- 2019
-
Ingår i: International Review of Retail Distribution & Consumer Research. - : Informa UK Limited. - 0959-3969 .- 1466-4402. ; 29:4, s. 359-375
-
Tidskriftsartikel (refereegranskat)abstract
- The aim of this paper is to analyse labour turnover in retail firms with stores in different city locations. This case study of a Swedish mid-sized city uses comprehensive longitudinal register data on individuals. In a first step, an unconditional descriptive analysis shows that labour turnover in retail is higher in out-of-town locations, compared to more central locations in the city. In a second step, a generalized linear model (GLM) analysis is conducted where labour turnover in downtown and out-of-town locations are compared. Firm internal and industry factors, as well as employee characteristics, and location-specific factors are controlled for. The results indicate that commuting costs and intra-urban location have no statistically significant effect on labour turnover in retail firms. Instead, firm internal factors, such as human resource management, has a major influence on labour turnover rates. The findings indicate that in particular firms with multiple locations may need to pay extra attention to work conditions across stores in different places in a city, in order to avoid diverging levels of labour mobility. This paper complements previous survey-based studies on labour turnover by using a comprehensive micro-level dataset to analyse revealed rather than stated preferences concerning job-to-job mobility. An elaborated measure of labour turnover is used to analyse differences between shopping areas in different locations within the city. The particular research design used in this paper makes it possible to isolate the effect of intra-organizational conditions by analysing mobility within firms with workplaces in both downtown and out-of-town locations. This is the first comprehensive study of labour turnover and mobility with an intra-urban perspective in the retail sector.
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| 32. |
- Han, Mengjie, 1985-, et al.
(författare)
-
Perspectives of Machine Learning and Natural Language Processing on Characterizing Positive Energy Districts
- 2024
-
Ingår i: Buildings. - : MDPI. - 2075-5309. ; 14:2
-
Tidskriftsartikel (refereegranskat)abstract
- The concept of a Positive Energy District (PED) has become a vital component of the efforts to accelerate the transition to zero carbon emissions and climate-neutral living environments. Research is shifting its focus from energy-efficient single buildings to districts, where the aim is to achieve a positive energy balance across a given time period. Various innovation projects, programs, and activities have produced abundant insights into how to implement and operate PEDs. However, there is still no agreed way of determining what constitutes a PED for the purpose of identifying and evaluating its various elements. This paper thus sets out to create a process for characterizing PEDs. First, nineteen different elements of a PED were identified. Then, two AI techniques, machine learning (ML) and natural language processing (NLP), were introduced and examined to determine their potential for modeling, extracting, and mapping the elements of a PED. Lastly, state-of-the-art research papers were reviewed to identify any contribution they can make to the determination of the effectiveness of the ML and NLP models. The results suggest that both ML and NLP possess significant potential for modeling most of the identified elements in various areas, such as optimization, control, design, and stakeholder mapping. This potential is realized through the utilization of vast amounts of data, enabling these models to generate accurate and useful insights for PED planning and implementation. Several practical strategies have been identified to enhance the characterization of PEDs. These include a clear definition and quantification of the elements, the utilization of urban-scale energy modeling techniques, and the development of user-friendly interfaces capable of presenting model insights in an accessible manner. Thus, developing a holistic approach that integrates existing and novel techniques for PED characterization is essential to achieve sustainable and resilient urban environments.
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| 33. |
|
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| 34. |
- Han, Mengjie, 1985-, et al.
(författare)
-
Review of natural language processing techniques for characterizing positive energy districts
- 2023
-
Ingår i: journal of Physics; Conference series. - : Institute of Physics Publishing (IOPP).
-
Konferensbidrag (refereegranskat)abstract
- The concept of Positive Energy Districts (PEDs) has emerged as a crucial aspect of endeavours aimed at accelerating the transition to zero carbon emissions and climate-neutral living spaces. The focus of research has shifted from energy-efficient individual buildings to entire districts, where the objective is to achieve a positive energy balance over a specific timeframe. The consensus on the conceptualization of a PED has been evolving and a standardized checklist for identifying and evaluating its constituent elements needs to be addressed. This study aims to develop a methodology for characterizing PEDs by leveraging natural language processing (NLP) techniques to model, extract, and map these elements. Furthermore, a review of state-of-the-art research papers is conducted to ascertain their contribution to assessing the effectiveness of NLP models. The findings indicate that NLP holds significant potential in modelling the majority of the identified elements across various domains. To establish a systematic framework for AI modelling, it is crucial to adopt approaches that integrate established and innovative techniques for PED characterization. Such an approach would enable a comprehensive and effective implementation of NLP within the context of PEDs, facilitating the creation of sustainable and resilient urban environments. © 2023 Institute of Physics Publishing. All rights reserved.
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| 35. |
- Han, Mengjie, 1985-, et al.
(författare)
-
The reinforcement learning method for occupant behavior in building control : A review
- 2021
-
Ingår i: Energy and Built Environment. - : Elsevier BV. - 2666-1233. ; 2:2, s. 137-148
-
Tidskriftsartikel (refereegranskat)abstract
- Occupant behavior in buildings has been considered the major source of uncertainty for assessing energy consumption and building performance. Modeling frameworks are usually built to accomplish a certain task, but the stochasticity of the occupant makes it difficult to apply that experience to a similar but distinct environment. For complex and dynamic environments, the development of smart devices and computing power makes intelligent control methods for occupant behaviors more viable. It is expected that they will make a substantial contribution to reducing global energy consumption. Among these control techniques, the reinforcement learning (RL) method seems distinctive and applicable. The success of the reinforcement learning method in many artificial intelligence applications has given an explicit indication of how this method might be used to model and adjust occupant behavior in building control. Fruitful algorithms complement each other and guarantee the quality of the optimization. However, the examination of occupant behavior based on reinforcement learning methodologies is not well established. The way that occupant interacts with the RL agent is still unclear. This study briefly reviews the empirical applications using reinforcement learning, how they have contributed to shaping the modeling paradigms and how they might suggest a future research direction.
|
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| 36. |
- Han, Mengjie, et al.
(författare)
-
To what extent do neighbouring populations affect local population growth over time?
- 2016
-
Ingår i: Population, Space and Place. - : John Wiley & Sons. - 1544-8444 .- 1544-8452. ; 22:1, s. 68-83
-
Tidskriftsartikel (refereegranskat)abstract
- This study covers a period when society changed from a pre-industrial agricultural society to a post-industrial service-producing society. Parallel with this social transformation, major population changes took place. In this study, we analyse to what extent local population change is affected by neighbouring populations. To do this, we focused on the last 190 years of local population change that redistributed population in Sweden. We used literature to identify several different processes in the population redistribution. The different processes implied different spatial dependencies between local population change and the surrounding populations. The analysis is based on an unchanged historical parish division, and we used an index of local spatial correlation to describe different types of spatial dependencies that influenced the redistribution of the population. To control inherent time dependencies, we introduced a non-separable spatial-temporal correlation model into the analysis of population redistribution. Hereby, several different spatial dependencies could be simultaneously observed over time. The main conclusions are that while local population changes have been highly dependent on neighbouring populations in the 19th century, this spatial dependence became insignificant already when two parishes are separated by 5 km in the late 20th century. It is argued that the only process that significantly redistributed the population at the end of the 20th century is the immigration to Sweden.
|
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| 37. |
- Huang, Pei, et al.
(författare)
-
Characterization and optimization of energy sharing performances in energy-sharing communities in Sweden, Canada and Germany
- 2022
-
Ingår i: Applied Energy. - : Elsevier. - 0306-2619 .- 1872-9118. ; 326
-
Tidskriftsartikel (refereegranskat)abstract
- Peer-to-peer (P2P) renewable power sharing within a building community is a promising solution to enhance the community's self-sufficiency and relieve the grid stress posed by the increased deployment of distributed renewable power. Existing studies have pointed out that the energy sharing potentials of a building community are affected by various factors including location, community scale, renewable energy system (RES) capacity, energy system type, storage integration, etc. However, the impacts of these factors on the energy sharing potentials in a building community are not fully studied. Being unaware of those factors’ impacts could lead to reduced energy sharing potentials and thus limit the associated improvement in energy and economic performances. Thus, this study conducts a comprehensive analysis of various factors’ impacts on the energy sharing performances in building communities. Two performance indicators are first proposed to quantify the energy sharing performances: total amount of energy sharing and energy sharing ratio (ESR). Then, parametric studies are conducted based on real electricity demand data in three countries to reveal how these factors affect the proposed indictors and improvements in self-sufficiency, electricity costs, and energy exchanges with the power grid. Next, a genetic algorithm based design method is developed to optimize the influential parameters to maximize the energy sharing potentials in a community. The study results show that the main influential factors are RES capacity ratio, PV capacity ratio, and energy storage system capacity. A large energy storage capacity can enhance the ESR. To achieve the maximized ESR, the optimal RES capacity ratio should be around 0.4 ∼ 1.1. The maximum energy sharing ratio is usually smaller in high latitude districts such as Sweden. This study characterizes the energy sharing performances and provides a novel perspective to optimize the design of energy systems in energy sharing communities. It can pave the way for the large integration of distributed renewable power in the future. © 2022 The Author(s)
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| 38. |
|
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| 39. |
|
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| 40. |
|
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| 41. |
- Jin, Y., et al.
(författare)
-
District household electricity consumption pattern analysis based on auto-encoder algorithm
- 2019
-
Ingår i: IOP Conference Series: Materials Science and Engineering.
-
Konferensbidrag (refereegranskat)abstract
- The energy shortage is one key issue for sustainable development, a potential solution of which is the integration with the renewable energy resources. However, the temporal sequential characteristic of renewable resources is different from traditional power grid. For the entire power grid, it is essential to match the energy generation side with the energy consumption side, so the load characteristic at the energy use side is crucial for renewable power integration. Better understanding of energy consumption pattern in buildings contributes to matching different source of energy generation. Under the background of integration of traditional and renewable energy, this research focuses on analysis of different household electricity consumption patterns in an urban scale. The original data is from measurement of daily energy consumption with smart meter in households. To avoid the dimension explosion phenomenon, the auto-encoder algorithm is introduced during the clustering analysis of daily electricity use data, which plays the role of principal component analysis. The clustering based on auto-encoder gives a clear insight into the urban electricity use patterns in household. During the data analysis, several feature variables are proposed, which include peak value, valley value and average value. The distinction analysis is also conducted to evaluate the analysis performance. The study takes households in Nanjing city, China as a case study, to conduct the clustering analysis on electricity consumption of residential buildings. The analysis results can be further applied, such as during the capacity design of district energy storage.
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| 42. |
|
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| 43. |
- May, Ross
(författare)
-
On the Feasibility of Reinforcement Learning in Single- and Multi-Agent Systems : The Cases of Indoor Climate and Prosumer Electricity Trading Communities
- 2023
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Over half of the world’s population live in urban areas, a trend which is expected to only grow as we move further into the future. With this increasing trend in urbanisation, challenges are presented in the form of the management of urban infrastructure systems. As an essential infrastructure of any city, the energy system presents itself as one of the biggest challenges. Indeed, as cities expand in population and economically, global energy consumption increases, and as a result, so do greenhouse gas (GHG) emissions. Key to realising the goals as laid out by the 2030 Agenda for Sustainable Development, is the energy transition - embodied in the goals pertaining to affordable and clean energy, sustainable cities and communities, and climate action. Renewable energy systems (RESs) and energy efficiency have been shown as key strategies towards achieving these goals. While the building sector is considered to be one of the biggest contributors to climate change, it is also seen as an area with many opportunities for realising the energy transition. Indeed, the emergence of the smart city and the internet of things (IoT), alongside Photovoltaic and battery technology, offers opportunities for both the smart management of buildings, as well as the opportunity to form self-sufficient peer-to-peer (P2P) electricity trading communities. Within this context, advanced building control offers significant potential for mitigating global warming, grid instability, soaring energy costs, and exposure to poor indoor building climates. Most advanced control strategies, however, rely on complex mathematical models, which require a great deal of expertise to construct, thereby costing in time and money, and are unlikely to be frequently updated - which can lead to sub-optimal or even wrong performance. Furthermore, arriving at solutions in economic settings as complex and dynamic as the P2P electricity markets referred to above, often leads to solutions that are computationally intractable. A model-based approach thus seems, as alluded to above, unsustainable, and I thus propose taking a model-free alternative instead. One such alternative is the reinforcement learning (RL) method. This method provides a beautiful solution that addresses many of the limitations seen in more classical approaches - those based on complex mathematical models - to single- and multi-agent systems. To address the feasibility of RL in the context of building systems, I have developed four papers. In studying the literature, while there is much review work in support of RL for controlling energy consumption, it was found that there were no such works analysing RL from a methodological perspective w.r.t. controlling the comfort level of building occupants. Thus, in Paper I, to fill in this gap in knowledge, a comprehensive review in this area was carried out. To follow up, in Paper II, a case study was conducted to further assess, among other things, the computational feasibility of RL for controlling occupant comfort in a single agent context. It was found that the RL method was able to improve thermal and indoor air quality by more than 90% when compared with historically observed occupant data. Broadening the scope of RL, Papers III and IV considered the feasibility of RL at the district scale by considering the efficient trade of renewable electricity in a peer-to-peer prosumer energy market. In particular, in Paper III, by extending an open source economic simulation framework, multi-agent reinforcement learning (MARL) was used to optimise a dynamic price policy for trading the locally produced electricity. Compared with a benchmark fixed price signal, the dynamic price mechanism arrived at by RL, increased community net profit by more than 28%, and median community self-sufficiency by more than 2%. Furthermore, emergent social-economic behaviours such as changes in supply w.r.t changes in price were identified. A limitation of Paper III, however, is that it was conducted in a single environment. To address this limitation and to assess the general validity of the proposed MARL-solution, in Paper IV a full factorial experiment based on the factors of climate - manifested in heterogeneous demand/supply profiles and associated battery parameters, community scale, and price mechanism, was conducted in order to ascertain the response of the community w.r.t net-loss (financial gain), self-sufficiency, and income equality from trading locally produced electricity. The central finding of Paper IV was that the community, w.r.t net-loss, performs significantly better under a learned dynamic price mechanism than under the benchmark fixed price mechanism, and furthermore, a community under such a dynamic price mechanism stands an odds of 2 to 1 in increased financial savings.
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| 44. |
- May, Ross, et al.
(författare)
-
Reinforcement learning control for indoor comfort : A survey
- 2019
-
Ingår i: IOP Conference Series: Materials Science and Engineering.
-
Konferensbidrag (refereegranskat)abstract
- Building control systems are prone to fail in complex and dynamic environments. The reinforcement learning (RL) method is becoming more and more attractive in automatic control. The success of the reinforcement learning method in many artificial intelligence applications has resulted in an open question on how to implement the method in building control systems. This paper therefore conducts a comprehensive review of the RL methods applied in control systems for indoor comfort and environment. The empirical applications of RL-based control systems are then presented, depending on optimisation objectives and the measurement of energy use. This paper illustrates the class of algorithms and implementation details regarding how the value functions have been represented and how the policies are improved. This paper is expected to clarify the feasible theory and functions of RL for building control systems, which would promote their wider-spread application and thus contribute to the social economic benefits in the energy and built environments.
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| 45. |
- May, Ross
(författare)
-
The reinforcement learning method : A feasible and sustainable control strategy for efficient occupant-centred building operation in smart cities
- 2019
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Over half of the world’s population lives in urban areas, a trend which is expected to only grow as we move further into the future. With this increasing trend in urbanisation, challenges are presented in the form of the management of urban infrastructure systems. As an essential infrastructure of any city, the energy system presents itself as one of the biggest challenges. As cities expand in population and economically, global energy consumption increases and as a result so do greenhouse gas (GHG) emissions. To achieve the 2030 Agenda’s sustainable development goal on energy (SDG 7), renewable energy and energy efficiency have been shown as key strategies for attaining SDG 7. As the largest contributor to climate change, the building sector is responsible for more than half of the global final energy consumption and GHG emissions. As people spend most of their time indoors, the demand for energy is made worse as a result of maintaining the comfort level of the indoor environment. However, the emergence of the smart city and the internet of things (IoT) offers the opportunity for the smart management of buildings. Focusing on the latter strategy towards attaining SDG 7, intelligent building control offers significant potential for saving energy while respecting occupant comfort (OC). Most intelligent control strategies, however, rely on complex mathematical models which require a great deal of expertise to construct thereby costing in time and money. Furthermore, if these are inaccurate then energy is wasted and the comfort of occupants is decreased. Moreover, any change in the physical environment such as retrofits result in obsolete models which must be re-identified to match the new state of the environment. This model-based approach seems unsustainable and so a new model-free alternative is proposed. One such alternative is the reinforcement learning (RL) method. This method provides a beautiful solution to accomplishing the tradeoff between energy efficiency and OC within the smart city and more importantly to achieving SDG 7. To address the feasibility of RL as a sustainable control strategy for efficient occupant-centred building operation, a comprehensive review of RL for controlling OC in buildings as well as a case study implementing RL for improving OC via a window system are presented. The outcomes of each seem to suggest RL as a feasible solution, however, more work is required in the form of addressing current open issues such as cooperative multi-agent RL (MARL) needed for multi-occupant/multi-zonal buildings.
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| 46. |
- Ngoc Phuong, Chau
(författare)
-
Machine Learning Approaches to Develop Weather Normalize Models for Urban Air Quality
- 2024
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- According to the World Health Organization, almost all human population (99%) lives in 117 countries with over 6000 cities, where air pollutant concentration exceeds recommended thresholds. The most common, so-called criteria, air pollutants that affect human lives, are particulate matter (PM) and gas-phase (SO2, CO, NO2, O3 and others). Therefore, many countries or regions worldwide have imposed regulations or interventions to reduce these effects. Whenever an intervention occurs, air quality changes due to changes in ambient factors, such as weather characteristics and human activities. One approach for assessing the effects of interventions or events on air quality is through the use of the Weather Normalized Model (WNM). However, current deterministic models struggle to accurately capture the complex, non-linear relationship between pollutant concentrations and their emission sources. Hence, the primary objective of this thesis is to examine the power of machine learning (ML) and deep learning (DL) techniques to develop and improve WNMs. Subsequently, these enhanced WNMs are employed to assess the impact of events on air quality. Furthermore, these ML/DL-based WNMs can serve as valuable tools for conducting exploratory data analysis (EDA) to uncover the correlations between independent variables (meteorological and temporal features) and air pollutant concentrations within the models. It has been discovered that DL techniques demonstrated their efficiency and high performance in different fields, such as natural language processing, image processing, biology, and environment. Therefore, several appropriate DL architectures (Long Short-Term Memory - LSTM, Recurrent Neural Network - RNN, Bidirectional Recurrent Neural Network - BIRNN, Convolutional Neural Network - CNN, and Gated Recurrent Unit - GRU) were tested to develop the WNMs presented in Paper I. When comparing these DL architectures and Gradient Boosting Machine (GBM), LSTM-based methods (LSTM, BiRNN) have obtained superior results in developing WNMs. The study also showed that our WNMs (DL-based) could capture the correlations between input variables (meteorological and temporal variables) and five criteria contaminants (SO2, CO, NO2, O3 and PM2.5). This is because the SHapley Additive exPlanations (SHAP) library allowed us to discover the significant factors in DL-based WNMs. Additionally, these WNMs were used to assess the air quality changes during COVID-19 lockdown periods in Ecuador. The existing normalized models operate based on the original units of pollutants and are designed for assessing pollutant concentrations under “average” or consistent weather conditions. Predicting pollution peaks presents an even greater challenge because they often lack discernible patterns. To address this, we enhanced the Weather Normalized Models (WNMs) to boost their performance specifically during daily concentration peak conditions. In the second paper, we accomplished this by developing supervised learning techniques, including Ensemble Deep Learning methods, to distinguish between daily peak and non-peak pollutant concentrations. This approach offers flexibility in categorizing pollutant concentrations as either daily concentration peaks or non-daily concentration peaks. However, it is worth noting that this method may introduce potential bias when selecting non-peak values. In the third paper, WNMs are directly applied to daily concentration peaks to predict and analyse the correlations between meteorological, temporal features and daily concentration peaks of air pollutants.
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| 47. |
- Quintana, Samer, et al.
(författare)
-
A Top-Down Digital Mapping of Spatial-Temporal Energy Use for Municipality-Owned Buildings : A Case Study in Borlänge, Sweden
- 2021
-
Ingår i: Buildings. - : MDPI. - 2075-5309. ; 11:2
-
Tidskriftsartikel (refereegranskat)abstract
- Urban energy mapping plays a crucial role in benchmarking the energy performance of buildings for many stakeholders. This study examined a set of buildings in the city of Borlange, Sweden, owned by the municipality. The aim was to present a digital spatial map of both electricity use and district heating demand in the spatial-temporal dimension. A toolkit for top-down data processing and analysis was considered based on the energy performance database of municipality-owned buildings. The data were initially cleaned, transformed and geocoded using custom scripts and an application program interface (API) for OpenStreetMap and Google Maps. The dataset consisted of 228 and 105 geocoded addresses for, respectively, electricity and district heating monthly consumption for the year 2018. A number of extra parameters were manually incorporated to this data, i.e., the total floor area, the building year of construction and occupancy ratio. The electricity use and heating demand in the building samples were about 24.47 kWh/m(2) and 268.78 kWh/m(2), respectively, for which great potential for saving heating energy was observed. Compared to the electricity use, the district heating showed a more homogenous pattern following the changes of the seasons. The digital mapping revealed a spatial representation of identifiable hotspots for electricity uses in high-occupancy/density areas and for district heating needs in districts with buildings mostly constructed before 1980. These results provide a comprehensive means of understanding the existing energy distributions for stakeholders and energy advisors. They also facilitate strategy geared towards future energy planning in the city, such as energy benchmarking policies.
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| 48. |
- Rebreyend, Pascal, et al.
(författare)
-
How does different algorithm work when applied on the different road networks when optimal location of facilities is searched for in rural areas?
- 2014
-
Ingår i: Web Information Systems Engineering – WISE 2013 Workshops. - Berlin : Springer Berlin/Heidelberg. - 9783642543708 - 9783642543692 ; , s. 284-291
-
Konferensbidrag (refereegranskat)abstract
- The p-median problem is often used to locate P service facilities in a geographically distributed population. Important for the performance of such a model is the distance measure. The first aim in this study is to analyze how the optimal location solutions vary, using the p-median model, when the road network is alternated. It is hard to find an exact optimal solution for p-median problems. Therefore, in this study two heuristic solutions are applied, simulating annealing and a classic heuristic. The secondary aim is to compare the optimal location solutions using different algorithms for large p-median problem. The investigation is conducted by the means of a case study in a rural region with a. asymmetrically distributed population, Dalecarlia. The study shows that the use of more accurate road networks gives better solutions for optimal location, regardless what algorithm that is used and regardless how many service facilities that is opt for. It is also shown that the Simulating annealing algorithm not just is much faster than the classic heuristic used here, but also in most cases gives better solutions.
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| 49. |
- Sadeghian, Paria
(författare)
-
A Multi-Dimensional Approach to Human Mobility and Transportation Mode Detection Using GPS Data
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- GPS tracking data is an essential resource for analyzing human travel patterns and evaluating the effects on transportation systems. The primary challenge, however, is to accurately identify the modes of transportation within unlabeled GPS data. These approaches range from simple rule-based systems to advanced machine-learning techniques. This dissertation aims to bridge this gap by examining the critical features and techniques of these methods and proposing a novel approach for detecting transportation modes in GPS tracking data. To achieve this goal, a comprehensive understanding of individual journeys is crucial. Thus, this research adopts a microdata analytic approach, encompassing data collection, processing, analysis, and decision-making stages. Doing so contributes to advancing human mobility research and transportation mode detection. Paper I undertook a systematic review of transport mode detection methodologies to fill the research gap, emphasizing the predominance of supervised learning algorithms and highlighting the need for further research to address the limitations of small datasets. Paper II introduced a stepwise methodology, integrating unsupervised learning, GIS, and supervised algorithms to detect transport modes while minimizing reliance on labelled data. The Random Forest algorithm emerged as a precise but time-intensive solution. Paper III showcased a novel approach to transport mode detection using deep learning models, outperforming traditional machine learning methods. This paper signals the potential of deep learning in the field and demonstrates the importance of raw GPS data in enhancing accuracy. Paper V addressed the challenge of predicting human mobility patterns under the Hidden Markov Model (HMM) framework, highlighting the applicability of HMMs to understanding and predicting complex mobility behaviour. This paper emphasized the need for GPS tracking data in developing advanced mobility models. Paper IV ventured into hybrid methodology by combining K-means clustering with the ANP-PSO algorithm to enhance transportation mode classification. This pioneering approach improved classification accuracy while reducing dependence on labelled datasets. Collectively, these papers underscore the opportunities and limitations in human mobility research, offering insights into future directions for mitigating data quality issues and improving the accuracy of transportation mode detection. These innovative methodologies have practical implications for transportation planning, resource allocation, and intelligent transportation system development, ultimately shaping the future of transportation research and decision-making. Standardized data collection, processing, and labelling methods are crucial and need attention in future research. Future research can focus on developing such benchmarks and validation protocols to enhance the reliability and comparability of results.
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- Sadeghian, Paria, et al.
(författare)
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Testing feasibility of using a hidden Markov model on predicting human mobility based on GPS tracking data
- 2024
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Ingår i: Transportmetrica B. - : Taylor & Francis. - 2168-0566. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- Human mobility behaviour is far from random and can be predictable. Predicting human mobility behaviour has the potential to improve location selection for facilities, transportation services, urban planning, and can be beneficial in providing more efficient sustainable urban development strategies. However, it is difficult to model urban mobility patterns since incentives for mobility is complex, and influenced by several factors, such as dynamic population, weather conditions. Thus, this paper proposes a prediction-oriented algorithm under the framework of a Hidden Markov Model to predict next-location and time-of-arrival of human mobility. A comprehensive evaluation of these two schemes for the representation of latent and observable variables is discussed. In conclusion, the paper provides a valuable contribution to the field of mobility behaviour prediction by proposing a novel algorithm. The evaluation shows that the proposed algorithm is stable and consistent in predicting the next location of users based on their past trajectories. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
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