| 1. |
- Brembilla, Christian, 1983-
(författare)
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Efficiency factors for space heating system in buildings
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The thesis focuses on the efficiency of the space heating system. In particular, the efficiency factors measure the efficiency of thermal zone. The efficiency factors measures how the energy is used in a space heating. Efficiency factors relatively close to one mean that the energy is used "efficiently'', by contrast, efficiency factors close to the zero mean that the majority of the energy is lost to the outdoor environment. This method for the appraisal of space heating performance reads as if it is apparently simple and intuitive. In reality, the efficiency factor method has several pitfalls.The thesis provides tools, insights and remarks on how to apply the efficiency factor method to space heating systems equipped with hydronic panel radiator and floor heating respectively. Models of the latter heaters together with the multilayer wall were developed and validated to understand the reliability of their predictions. The hypothesis is that the heat stored in the building thermal mass and heaters plays a role in defining the building thermal performance and as a result in the appraisal of the efficiency factors. The validation is based on the sensitivity bands of the models' predictions. The heaters were tested in in a thermostatic booth simulator. Benefits and drawbacks of each model were highlighted to increase awareness of their use in the engineering fields. The results showed how the models accounting for the heat stored performed the charging phase. In addition, results of how the multilayer wall delayed and damped down the heat wave coming from the outdoor environment were presented with the appraisal of the decrement factor and time delay of the indoor temperature. The results of the efficiency factors analysis reveal how the weather affects the efficiency of each locality situated in cold climates. Lastly how different control strategies impact on the efficiency factors of space heating and its distribution system. To conclude, this study highlights the paradoxes around the efficiency factor method. The thesis proposes how such factors have to be interpreted by researchers and scientists tackling the lack of information around this topic.
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| 2. |
- Allard Stolterman, Ingrid, 1986-
(författare)
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Regulating energy performance of residential buildings in cold climate : a study of indicators, criteria, and evaluation methods
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Building energy performance has been important in Fennoscandia ever since the early vernacular houses, to combat the cold climate. Due to EU directive 2010/31/EU on the energy performance of buildings (EPBD recast), building energy performance has become even more relevant in northern Europe the last decade. Objectives for improving building energy performance may include reducing cost and CO2-emissions, increasing energy independency, and improving the indoor climate. Different indicators, criteria, and evaluations methods may be used to reach these objectives. This dissertation addresses indicators, criteria, and evaluation methods used to regulate energy performance of residential buildings in Sweden, Norway, Finland, and Russia. Four research objectives are covered: (RO1) comparing criteria and evaluation methods used to regulate energy performance of residential buildings in Sweden, Norway, and Finland, (RO2) studying the perspective of professionals with experience in building energy performance evaluation on (a) methods for evaluating envelope air leakage of residential buildings in Sweden and Finland and (b) potential energy performance indicators in the Swedish procurement process of multi-family buildings, (RO3) developing an approach for analysing the performance gap between design predictions and measurements that can be used to verify compliance with requirements on building energy use in practice, and (RO4) comparing the stringency of the energy performance criteria for residential buildings between the Swedish, Norwegian, Finnish, and Russian national building code. Many differences were found between how energy performance of residential buildings was regulated in the four countries. In Sweden, measurements were used more for evaluating building energy performance than in the other countries. As of 1st January 2020, the Finnish building code was characterized by its focus on the building heat loss and stringent energy performance criteria compared to the other countries. The Norwegian building code was characterized by a relatively narrow system perspective on energy performance, with no regulation of the energy production efficiency or energy source. The Russian building code also had a narrow system perspective but was also characterized by its focus on the form factor – the relationship between building volume and enclosing area. The practitioners wanted to minimize the influence from building operation and user behaviour on energy performance evaluations in the Swedish building procurement process of multi-family buildings. Hence, they preferred component-focused indicators or indicators with a narrow system boundary. An approach has been developed for analyzing the performance gap between design phase predictions and measurements. The approach can be used to verify the finished building’s energy performance, with minimal influence from occupant behavior and building operation.
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| 3. |
- Azizi, Shoaib, 1989-
(författare)
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A multi-method assessment to support energy efficiency decisions in existing residential and academic buildings
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Rapid decarbonization of building stock is essential for the energy transition required to mitigate climate change and limit the global temperature rise below 1.5 °C. The decision-making for the adoption of energy efficiency measures (EEMs) is often complex and involves lasting consequences and risks. The strategy to direct and support the decision makers can significantly increase the adoption rate of EEMs in buildings. This Ph.D. project focuses on facilitating sustainability improvement in buildings by supporting the decision makers who are accountable for the consequences of adopting the EEMs. Energy efficiency improvement is decided and managed differently in various types of buildings and contexts and encounters different challenges and opportunities. Accordingly, it is required to understand the needs to select adequate strategies and to devise effective supporting interventions for energy efficiency improvement.The owners of single-family houses are often the occupants who are in charge of the most decisions to improve energy efficiency in their dwellings. The situation is rather different in multi-family buildings and academic buildings in which organizational management adds more complexity and the decisions affect various stakeholders. The studies in this project are based on qualitative and quantitative data collected from single-family houses, multi-family buildings, and university buildings in northern Sweden. Surveys were used to elicit the decision makers' perceptions of different types of buildings. Moreover, sensor data from university buildings were used in the case studies to develop informative metrics for space use efficiency and to analyze the effect of sensor positioning on monitored data.The initial work involved understanding the opportunities and challenges of improving energy efficiency in buildings and the tradeoffs between the perceived benefits and barriers. This part of the thesis provided the foundation and inspiration for the rest of the project, including investigating how to bundle several measures and use information and communication technologies (ICT) for building sustainability. The findings show lack of information and evidence that could justify the beneficial outcomes of EEMs is a major barrier for effective decision-making. Clear information on potential improvements allows sharing the responsibilities among different stakeholders and increases the management capacity to handle projects and adopt EEMs. Using feedback tools (for example, space use and/or energy use visualizations) might be an effective strategy to influence decision makers.Various studies incorporated in this multidisciplinary Ph.D. thesis develop and investigate strategies to support decision makers to improve energy efficiency in buildings. The findings provide insights to policymakers and businesses to devise intervention strategies for energy efficiency in buildings.
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| 4. |
- Nydahl, Helena, 1990-
(författare)
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Communication of life cycle assessment results : life cycle key performance indicators
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The global warming that we are on track for will result in a severe loss of natural capital leading to significant losses in economic capital when urban infrastructure is destroyed, agricultural productivity declines and poverty spread among other disasters. Climate change due to emissions does not only affect the polluter, the hazardous effects becomes evident on a global level. An essential tool to enable decision‑making with concern to the welfare of the global commons is life cycle assessment (LCA). LCA compile and evaluate the inputs, outputs, and potential environmental impacts of a product system throughout its life cycle. The reviewed literature frames a gap regarding interpretation of LCA-results and inquire for guidelines that address a wide range of stakeholders to enable informed decision-making with regard to the welfare of the global commons. Some studies even argue that an apparent weakness of LCA-result communication is the understanding of what the results mean for the economic key performance indicators (KPIs) of the stakeholder. Thus, this thesis aims to contribute to the development of guidelines for interpretation of LCA-results by introducing an approach for communicating LCA-results that is compatible with the economically driven nature of stakeholders. The specific research questions (RQ) of this thesis are: (RQ1) How can well-established economic KPIs be utilised to quantify environmental impact? and (RQ2) How does incorporation of monetary valuation of environmental impacts and related environmental aspects affect the LCA-result and communication of results?These research questions have led to life cycle key performance indicators (LC‑KPIs) that quantify life cycle economic and environmental impacts in a way that take after the traditional economic KPIs of the stakeholders, which is outlined as essential to improve the understanding of LCA-results. The LC‑KPIs utilize the traditional economic KPIs of return on investment (ROI) and annual yield (AY). Additionally, to manage the large amount of non-commensurate units of holistic life cycle sustainability assessment, monetary valuation has been applied. Hence, contributing to the research area of monetary valuation in LCA by introducing and testing new approaches.The introduced LC-KPIs have been specified for building LCA and exemplified by applying them to a number of Swedish case buildings. The result show that the climate-economic assessment of building refurbishment differs compared to the traditional economic assessment when monetary valuation is utilized in LCA with the LC-KPI of ROIEconomy+. However, in the comparative assessment of building refurbishment and new construction, the LCA‑result does not change compared to the traditional economic assessment when monetary valuation is utilized in LCA with the LC-KPI of ELCCA. This is explained by the high costs associated with the investment and energy use of buildings and may not be the case if products with lower investment and energy use costs and high life cycle greenhouse-gas emissions would be studied. Still, if a purely environmental assessment of a product is wished for, the LC-KPI should only include monetary valuation of environmental impact factors and exclude traditional economic performance. Thus, the case study result defines further scope for research on the subject of monetary valuation in LCA and inquire for a wider spectrum of LC-KPIs that utilizes monetary valuation.The introduced approach of this thesis contribute to the development of guidelines for interpretation of LCA-results. Nevertheless, there are still challenges that needs to be addressed in the development of robust LCA-result interpretation. Still, the LC‑KPIs used in this thesis address the “cognitive logics” of a wide range of stakeholders and provide an approach for communication of LCA-results which improve the understanding of LCA-results.
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| 5. |
- Feng, Kailun, et al.
(författare)
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Energy-efficient retrofitting with incomplete building information : a data-driven approach
- 2022
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Ingår i: E3S web of conferences. - : EDP Sciences.
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Konferensbidrag (refereegranskat)abstract
- The high-performance insulations and energy-efficient HVAC have been widely employed as energy-efficient retrofitting for building renovation. Building performance simulation (BPS) based on physical models is a popular method to estimate expected energy savings for building retrofitting. However, many buildings, especially the older building constructed several decades ago, do not have full access to complete information for a BPS method. To address this challenge, this paper proposes a data-driven approach to support the decision-making of building retrofitting under incomplete information. The data-driven approach is constructed by integrating backpropagation neural networks (BRBNN), fuzzy C-means clustering (FCM), principal component analysis (PCA), and trimmed scores regression (TSR). It is motivated by the available big data sources from real-life building performance datasets to directly model the retrofitting performances without generally missing information, and simultaneously impute the case-specific incomplete information. This empirical study is conducted on real-life buildings in Sweden. The result indicates that the approach can model the performance ranges of energy-efficient retrofitting for family houses with more than 90% confidence. The developed approach provides a tool to predict the performance of individual buildings from different retrofitting measures, enabling supportive decision-making for building owners with inaccessible complete building information, to compare alternative retrofitting measures.
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| 6. |
- Hu, Siying, et al.
(författare)
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A data-driven exploration of the relations between occupant behaviors and comfort performances of energy-efficient measures
- 2023
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Ingår i: ICCREM 2023. - : American Society of Civil Engineers (ASCE). - 9780784485217 ; , s. 592-604
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Konferensbidrag (refereegranskat)abstract
- Energy-efficient building retrofitting plays a crucial role in reducing energy consumption and carbon emissions within the building sector. Energy-efficient retrofitting brings about changes in the built environment and it could influence the occupant behaviors. Additionally, occupant behaviors, in turn, alter the indoor environment, thereby affecting the comfort performance of the building after retrofitting. To explore this intricate relation between occupant behaviors and comfort performances of energy-efficient measures, this paper employs a data-driven approach to compile a comprehensive dataset encompassing occupant behaviors, energy-efficient measures, and associated indoor comfort of an office building in Umeå University, Sweden. Multiple binary logistic regression is applied to quantify the relationship between occupant behaviors and comfort performances of energy-efficient measures. The findings of this study hold significant value, providing guidance for occupants in adapting to energy-efficient measures while also informing future retrofitting implementation.
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| 7. |
- Liu, Bokai, et al.
(författare)
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Data-driven quantitative analysis of an integrated open digital ecosystems platform for user-centric energy retrofits : A case study in northern Sweden
- 2023
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Ingår i: Technology in society. - : Elsevier. - 0160-791X .- 1879-3274. ; 75
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents an open digital ecosystem based on a web-framework with a functional back-end server for user-centric energy retrofits. This data-driven web framework is proposed for building energy renovation benchmarking as part of an energy advisory service development for the Västerbotten region, Sweden. A 4-tier architecture is developed and programmed to achieve users’ interactive design and visualization via a web browser. Six data-driven methods are integrated into this framework as backend server functions. Based on these functions, users can be supported by this decision-making system when they want to know if a renovation is needed or not. Meanwhile, influential factors (input values) from the database that affect energy usage in buildings are to be analyzed via quantitative analysis, i.e., sensitivity analysis. The contributions to this open ecosystem platform in energy renovation are: 1) A systematic framework that can be applied to energy efficiency with data-driven approaches, 2) A user-friendly web-based platform that is easy and flexible to use, and 3) integrated quantitative analysis into the framework to obtain the importance among all the relevant factors. This computational framework is designed for stakeholders who would like to get preliminary information in energy advisory. The improved energy advisor service enabled by the developed platform can significantly reduce the cost of decision-making, enabling decision-makers to participate in such professional knowledge-required decisions in a deliberate and efficient manner. This work is funded by the AURORAL project, which integrates an open and interoperable digital platform, demonstrated through regional large-scale pilots in different countries of Europe by interdisciplinary applications.
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| 8. |
- Liu, Bokai, et al.
(författare)
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Multi-scale modeling in thermal conductivity of polyurethane incorporated with phase change materials using physics-informed neural networks
- 2024
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Ingår i: Renewable energy. - : Elsevier. - 0960-1481 .- 1879-0682. ; 220
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Tidskriftsartikel (refereegranskat)abstract
- Polyurethane (PU) possesses excellent thermal properties, making it an ideal material for thermal insulation. Incorporating Phase Change Materials (PCMs) capsules into Polyurethane has proven to be an effective strategy for enhancing building envelopes. This innovative design substantially enhances indoor thermal stability and minimizes fluctuations in indoor air temperature. To investigate the thermal conductivity of the Polyurethane-Phase Change Materials foam composite, we propose a hierarchical multi-scale model utilizing Physics-Informed Neural Networks (PINNs). This model allows accurate prediction and analysis of the material’s thermal conductivity at both the meso-scale and macro-scale. By leveraging the integration of physics-based knowledge and data-driven learning offered by Physics-Informed Neural Networks, we effectively tackle inverse problems and address complex multi-scale phenomena. Furthermore, the obtained thermal conductivity data facilitates the optimization of material design. To fully consider the occupants’ thermal comfort within a building envelope, we conduct a case study evaluating the performance of this optimized material in a detached house. Simultaneously, we predict the energy consumption associated with this scenario. All outcomes demonstrate the promising nature of this design, enabling passive building energy design and significantly improving occupants’ comfort. The successful development of this Physics-Informed Neural Networks-based multi-scale model holds immense potential for advancing our understanding of Polyurethane-Phase Change Material’s thermal properties. It can contribute to the design and optimization of materials for various practical applications, including thermal energy storage systems and insulation design in advanced building envelopes.
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| 9. |
- Liu, Bokai, et al.
(författare)
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Multiscale modeling of Heat transfer in Polyurethane - Phase Change Materials composites
- 2023
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Ingår i: Yound investigators symposium Umeå 2023. - Umeå : Umeå University. ; , s. 29-29
-
Konferensbidrag (refereegranskat)abstract
- Polyurethane (PU) exhibits exceptional thermal properties, making it an ideal material for thermal insulation. Incorporating Phase Change Materials (PCMs) capsules into Polyurethane (PU) has proven to be highly effective in enhancing building envelopes. This innovative design greatly enhances the stability of indoor thermal environments and reduces fluctuations in indoor air temperature. To investigate the thermal conductivity of this composite material, we have developed a comprehensive multiscale model of a PU-PCM foam composite. By obtaining thermal conductivity data, we can optimize the material's design for maximum effectiveness. To fully assess the thermal comfort of occupants within a building envelope, we have conducted a case study based on the performance of this optimized material. Specifically, we focused on a single room where PU-PCM composites were applied. Simultaneously, we predicted the energy consumption associated with this scenario. The results of our study clearly demonstrate the promising nature of this design, as it enables passive building energy design and significantly improves the comfort experienced by occupants.
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| 10. |
- Liu, Bokai, et al.
(författare)
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Multiscale modeling of thermal properties in Polyurethane incorporated with phase change materials composites : a case study
- 2023
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Ingår i: Healthy buildings Europe 2023. - Red Hook, NY : Curran Associates, Inc.. - 9781713877158 ; , s. 923-929
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Konferensbidrag (refereegranskat)abstract
- Polyurethane (PU) is an ideal thermal insulation material due to its excellent thermal properties. The incorporation of Phase Change Materials (PCMs) capsules into Polyurethane (PU) has been shown to be effective in building envelopes. This design can significantly increase the stability of the indoor thermal environment and reduce the fluctuation of indoor air temperature. We develop a multiscale model of a PU-PCM foam composite and study the thermal conductivity of this material. Later, the design of materials can be optimized by obtaining thermal conductivity. We conduct a case study based on the performance of this optimized material to fully consider the thermal comfort of the occupants of a building envelope with the application of PU-PCMs composites in a single room. At the same time, we also predict the energy consumption of this case. All the outcomes show that this design is promising, enabling the passive design of building energy and significantly improving occupants' comfort.
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| 11. |
- Liu, Bokai, et al.
(författare)
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Stochastic interpretable machine learning based multiscale modeling in thermal conductivity of Polymeric graphene-enhanced composites
- 2024
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Ingår i: Composite structures. - : Elsevier. - 0263-8223 .- 1879-1085. ; 327
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Tidskriftsartikel (refereegranskat)abstract
- We introduce an interpretable stochastic integrated machine learning based multiscale approach for the prediction of the macroscopic thermal conductivity in Polymeric graphene-enhanced composites (PGECs). This method encompasses the propagation of uncertain input parameters from the meso to macro scale, implemented through a foundational bottom-up multi-scale framework. In this context, Representative Volume Elements in Finite Element Modeling (RVE-FEM) are employed to derive the homogenized thermal conductivity. Besides, we employ two sets of techniques: Regression-tree-based methods (Random Forest and Gradient Boosting Machine) and Neural networks-based approaches (Artificial Neural Networks and Deep Neural Networks). To ascertain the relative influence of factors on output estimations, the SHapley Additive exPlanations (SHAP) algorithm is integrated. This interpretable machine learning methodology demonstrates strong alignment with published experimental data. It holds promise as an efficient and versatile tool for designing new composite materials tailored to applications involving thermal management.
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| 12. |
- Man, Qingpeng, et al.
(författare)
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Transfer of building retrofitting evaluations for data-scarce conditions : an empirical study for Sweden to China
- 2024
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Ingår i: Energy and Buildings. - : Elsevier. - 0378-7788 .- 1872-6178. ; 310
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Tidskriftsartikel (refereegranskat)abstract
- Evaluating and comparing the performances of different strategies is critical for energy-efficient building retrofitting. Data-driven modelling based on large building performance datasets is an effective method for such evaluations. However, it could be challenging to apply this approach to buildings from data-scarce areas where local building performance datasets have not been well-established, which means the data falls short of the high demand for building retrofitting on a global level. To address this, a transfer learning approach is proposed in this study that can evaluate the performance of buildings without local well-established building performance datasets. The proposed approach is applied in the Swedish-Chinese empirical study that relies on the Swedish dataset to transfer and predict the building performance in China without well-established datasets. It was achieved by applying fuzzy C-means clustering and a neural network (FCM-BRBNN) to pre-train the evaluation model based on the Swedish dataset. Then, the proposed approach collects a small sample of Chinese buildings in the data-scarce area and transfers the model to local building performance prediction. The results show that the transfer learning approach can reliably predict the performance of building retrofitting in data-scarce areas with only hundreds of local building samples. As such, this study provides a novel methodology that can support the evaluation and comparison of retrofitting strategies in data-scarce regions and countries with only limited local data. It could efficiently assist designers in optimizing energy-efficient designs in the pre-retrofit stage. Crucially, the methodology enables the transfer of knowledge regarding building performance across different countries and regions, being pivotal for the international collaboration required to stimulate the global energy-efficiency transformation.
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| 13. |
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| 14. |
- Penaka, Santhan Reddy, et al.
(författare)
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A data-driven framework for building energy benchmarking and renovation decision-making support in Sweden
- 2023
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Ingår i: SBE23-Thessaloniki. - : Institute of Physics (IOP).
-
Konferensbidrag (refereegranskat)abstract
- In Europe, the buildings sector is responsible for 40% of energy use and more than 30% of buildings are older than 50 years. Due to ageing, a large number of houses require energy-efficient renovation to meet building energy performance standards and the national energy efficiency target. Although Swedish house owners are willing to improve energy efficiency, there is a need for a dedicated platform providing decision-making knowledge for house owners to benchmark their buildings. This paper proposes a data-driven framework for building energy renovation benchmarking as part of an energy advisory service development for the Vasterbotten region, Sweden. This benchmark model facilitates regional homeowners to benchmark their building energy performance relative to the national target and similar neighbourhood buildings. Specifically, based on user input data such as energy use, location, construction year, floor area, etc., this model benchmarks the user's building performance using two benchmark references i.e., 1) Sweden's target to reduce buildings by 50% energy use intensity (EUI) by 50% by 2050 compared to the average EUI in 1995, 2) comparing user building with the most relevant peer group of buildings, using energy performance certificates (EPC) big data. Several building groups will be classified based on influential factors that affect building energy use. Hence, this benchmark provides decision-making supportive knowledge to homeowners e.g., whether they need to perform an energy-efficient renovation. In the future, this methodology will be extended and implemented in the digital platform to provide helpful insights to decide on suitable EEMs. This work is an integral part of project AURORAL aims to deliver an interoperable, open, and integrated digital platform, demonstrated by cross-domain applications through large-scale pilots in 8 regions in Europe, including Vasterbotten.
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| 15. |
- Penaka, Santhan Reddy, et al.
(författare)
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Improved energy retrofit decision making through enhanced bottom-up building stock modelling
- 2024
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Ingår i: Energy and Buildings. - : Elsevier. - 0378-7788 .- 1872-6178. ; 318
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Tidskriftsartikel (refereegranskat)abstract
- Modelling the performance of building stocks is crucial in facilitating the renovation at the building stock level. Bottom-up building stock modelling begins by detailing individual buildings and then aggregates them into stock level. Its primary advantage lies in capturing the inherent heterogeneity among distinct buildings, which enables tailored retrofitting. Naturally, this approach requires a comprehensive dataset with detailed building information such as geometry and envelope thermal properties. However, a common challenge is the incompleteness of available data in individual datasets. To address this, previous bottom-up studies have filled the missing data with representative or statistical data. Such practice could lead to homogeneous modelling of distinct buildings within the same statistical group. This limits the utilization of key ability of bottom-up building stock modelling in capturing heterogeneity, such as tailored retrofitting to explore potential retrofitting areas and strategies. To address this challenge of homogeneous modelling, we utilize data fusion framework for bottom-up building stock modelling, employing probabilistic record linkage and inverse modelling techniques to integrate multiple incomplete building performance datasets. This framework fills the missing data in one dataset with information from another, thus capturing inherent heterogeneity in the building stock. An empirical study was conducted in Umeå, Sweden, to investigate the framework's effectiveness by modelling building stock with various retrofitting strategies. This study contribution lies in enhancing bottom-up building stock modelling by capturing inherent heterogeneity, to provide tailored retrofitting solutions.
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| 16. |
- Puttige, Anjan Rao, 1990-, et al.
(författare)
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Are radiators ready for the challenges of the future : a review of advancements in radiators
- 2022
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Ingår i: E3S Web of Conferences. - : EDP Sciences.
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Konferensbidrag (refereegranskat)abstract
- Radiators play an important role in providing a comfortable and safe indoor environment while maintaining high-energy efficiency. In the perspective of future climate change with expected larger temperature fluctuations and the rapidly changing heat supply and demand, it is required that the current radiator technology is adaptable. The heat supply is changing towards a lower supply temperature to enable an increase in energy efficiency and an increase in the share of renewable energy. Simultaneously, both the heat supply and demand are expected to have more variations in the future. An additional concern that has come into more focus after the experience with the COVID 19 pandemic is the prevention of the spread of infection in indoor environments. Researchers have extensively studied several innovations in radiator technologies and their deployment that addresses these challenges. Some of the solutions available in the literature include floor heating, ceiling heating, ventilation radiator, stratum ventilation. Researchers have used advanced modeling and experimental techniques to understand how to deploy different types of radiator technologies. This review summarizes solutions in the literature that address these challenges and identifies knowledge gaps that need to be addressed. In particular, this study explores the gaps in knowledge of practical issues, such as the position of furniture and the position of people, which have received less attention in the literature. Research that addresses the effect of radiators on ventilation and a healthy indoor environment is also of particular interest in this review.
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| 17. |
- Puttige, Anjan Rao, 1990-
(författare)
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Utilization of a GSHP System in a DHC Network : modeling and optimization
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The ground source heat pumps (GSHPs) of customers connected to the district heating and cooling (DHC) network can benefit both the customer and the energy company. However, operating the GSHP to minimize the cost of providing heating and cooling to the customer while ensuring the long-term stability of the ground temperature is a challenge. This thesis addresses the challenge by developing accurate models of GSHP and optimizing the operation of the GSHP system using these models.The models presented in this thesis use field measurements to develop accurate models with low computational time. The main components of a GSHP system are the heat pump and the borehole heat exchanger (BHE). This thesis presents two approaches to use measured data to improve the accuracy of analytical models for BHE. The first approach is the calibration of the model parameters using this measured data. The second approach combines the analytical model with an artificial neural network model resulting in a hybrid model. The calibration approach reduced the relative RMSE of the analytical model from 21.9% to 13.9% in the testing period. The relative RMSE of the hybrid model for the testing period was 6.3%.We compared different data-driven models for heat pumps and determined that artificial neural network models have an advantage over traditional regression models when field measurements are available. The artificial neural network model was refined to better utilize the measured data. The refined models of heat pumps had a relative RMSE of less than 5%.The hybrid BHE model and an artificial neural network model for the heat pumps were used to model the GSHP system. The model was validated using four years of field measurements. The relative MAE for the compressor power and BHE power were 7.3% and 19.1% respectively.The validated model was used to optimize the operation of the GSHP system. In optimal operation, the cost of providing heating and cooling to the area was minimized from the perspective of the energy company while maintaining a stable temperature in the ground. In optimal operation, the annual cost of operation was shown to reduce by 64 t€ and the annual CO2 emission was shown to reduce by 92 tons.
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| 18. |
- Vesterberg, Jimmy, 1976-
(författare)
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A regression approach for assessment of building energy performance
- 2014
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Reliable evaluation methods is needed to ensure that investments in energy conservation measures (ECMs) and the construction of new energy efficient buildings lives up to the promised and expected performance.This thesis presents and evaluates a regression method for estimation of influential building parameters: transmission losses above ground (including air leakage), ground heat loss, and overall heat loss coefficient.The analysis is conducted with separately metered electricity, heating and weather data using linear regression models based on the simplified steady-state power balance for a whole building.The evaluation consists of analyzing the robustness of the extracted parameters, their suitability to be used as input values to building energy simulations (BES) tools. In addition, differences between uncalibrated and calibrated BES models are analyzed when they are used to calculate energy savings. Finally the suitability of using a buildings overall heat loss coefficient as a performance verification tool is studied.The presented regression method exhibits high robustness and good agreement with theory. Knowledge of these parameters also proved beneficial in BES calibration procedures as well as in performance verifications. Thus, the presented method shows promising features for reliable energy performance assessments of buildings.
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| 19. |
- Yu, Haitao, et al.
(författare)
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Data-driven modelling of building retrofitting with incomplete physics : a generative design and machine learning approach
- 2023
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Ingår i: Journal of Physics, Conference Series. - : Institute of Physics (IOP). - 1742-6588 .- 1742-6596. ; 2654
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Tidskriftsartikel (refereegranskat)abstract
- Building performance simulation (BPS) based on physical models is a popular method for estimating the expected energy savings from energy-efficient building retrofitting. However, for many buildings, especially older buildings, built several decades ago, an operator do not have full access to the complete information for the BPS method. Incomplete information comes from the lack of detailed building physics, such as the thermal transmittance of some building components due to the deterioration of components over time. To address this challenge, this paper proposed a data-driven approach to support the decision-making of building retrofitting selections under incomplete information conditions. The data-driven approach integrates the backpropagation neural networks (BRBNN), fuzzy C-means clustering (FCM), and generative design (GD). It generates the required big database of building performance through generative design, which can overcome the problem of incomplete information during building performance simulation and energy-efficient retrofitting. The case study is based on old residential buildings in severe cold regions of China, using the proposed approach to predict energy-efficient retrofitting performance. The results indicated that the proposed approach can model the performance of residential buildings with more than 90% confidence, and show the variation of results. The core contribution of the proposed approach is to provide a way of performance prediction of individual buildings resulting from different retrofitting measures under the incomplete physics condition.
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