| 1. |
- Feng, Kailun, et al.
(författare)
-
Energy-efficient retrofitting with incomplete building information : a data-driven approach
- 2022
-
Ingår i: E3S web of conferences. - : EDP Sciences.
-
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.
|
|
| 2. |
- Liu, Bokai, et al.
(författare)
-
Data-driven quantitative analysis of an integrated open digital ecosystems platform for user-centric energy retrofits : A case study in northern Sweden
- 2023
-
Ingår i: Technology in society. - : Elsevier. - 0160-791X .- 1879-3274. ; 75
-
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.
|
|
| 3. |
- Lu, Chujie, et al.
(författare)
-
Automated machine learning-based framework of heating and cooling load prediction for quick residential building design
- 2023
-
Ingår i: Energy. - : Elsevier. - 0360-5442 .- 1873-6785. ; 274
-
Tidskriftsartikel (refereegranskat)abstract
- Reducing the heating and cooling load through energy-efficient building design can help decarbonize the building sector. Heating and cooling load prediction using machine learning (ML) techniques become increasingly important in the rapid assessment of building design variables at the early design stage. However, when applying the ML techniques, it still requires expert knowledge and manually frequent intervention to improve the prediction performance. Hence, this study proposed an automated machine learning (AutoML)-based framework to automatically generate the optimal ML pipelines for heating and cooling load prediction. An experimental dataset of residential buildings was used to evaluate the proposed framework. The proposed framework achieved the best performance with R2 of 0.9965 and RMSE of 0.602 kWh/m2 for heating load prediction, and R2 of 0.9899 and RMSE of 0.973 kWh/m2 for cooling load prediction. The prediction results showed that the proposed framework outperformed the other improved ML models from the representative studies in the last five years. Further, an explainable analysis of the ML models was explored to reveal the relationships between design variables and heating and cooling load. The proposed framework aims at promoting the AutoML-based framework to designers for building energy performance prediction without excessive ML knowledge and manually frequent intervention.
|
|
| 4. |
- Penaka, Santhan Reddy, et al.
(författare)
-
A data-driven framework for building energy benchmarking and renovation decision-making support in Sweden
- 2023
-
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.
|
|
| 5. |
- Penaka, Santhan Reddy, et al.
(författare)
-
Improved energy retrofit decision making through enhanced bottom-up building stock modelling
- 2024
-
Ingår i: Energy and Buildings. - : Elsevier. - 0378-7788 .- 1872-6178. ; 318
-
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.
|
|
| 6. |
- Reddy Penaka, Santhan, et al.
(författare)
-
Digital Mapping of Techno-Economic Performance of a Water-Based Solar Photovoltaic/Thermal (PVT) System for Buildings over Large Geographical Cities
- 2020
-
Ingår i: Buildings. - : MDPI AG. - 2075-5309. ; 10:9
-
Tidskriftsartikel (refereegranskat)abstract
- Solar photovoltaic thermal (PVT) is an emerging technology capable of producing electrical and thermal energy using a single collector. However, to achieve larger market penetration of this technology, it is imperative to have an understanding of the energetic performance for different climatic conditions and the economic performance under various financial scenarios. This paper thus presents a techno-economic evaluation of a typical water-based PVT system for a single-family house to generate electricity and domestic hot water applications in 85 locations worldwide. The simulations are performed using a validated tool with one-hour time step for output. The thermal performance of the collector is evaluated using energy utilization ratio and exergy efficiency as key performance indicators, which are further visualized by the digital mapping approach. The economic performance is assessed using net present value and payback period under two financial scenarios: (1) total system cost as a capital investment in the first year; (2) only 25% of total system cost is a capital investment and the remaining 75% investment is considered for a financing period with a certain interest rate. The results show that such a PVT system has better energy and exergy performance for the locations with a low annual ambient temperature and vice versa. Furthermore, it is seen that the system boundaries, such as load profile, hot water storage volume, etc., can have a significant effect on the annual energy production of the system. Economic analysis indicates that the average net present values per unit collector area are 1800 and 2200 EUR, respectively, among the 85 cities for financial model 1 and financial model 2. Nevertheless, from the payback period point of view, financial model 1 is recommended for locations with high interest rate. The study is helpful to set an understanding of general factors influencing the techno-economic performance dynamics of PVT systems for various locations.
|
|
| 7. |
- Yu, Haitao, et al.
(författare)
-
Data-driven modelling of building retrofitting with incomplete physics : a generative design and machine learning approach
- 2023
-
Ingår i: Journal of Physics, Conference Series. - : Institute of Physics (IOP). - 1742-6588 .- 1742-6596. ; 2654
-
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.
|
|
| 8. |
- Zhang, Xingxing, et al.
(författare)
-
Characterizing Positive Energy District (PED) through a Preliminary Review of 60 Existing Projects in Europe
- 2021
-
Ingår i: Buildings. - : MDPI AG. - 2075-5309. ; 11
-
Tidskriftsartikel (refereegranskat)abstract
- Positive Energy District (PED) is recently proposed to be an integral part of a district/urban energy system with a corresponding positive influence. Thus, the PED concept could become the key solution to energy system transition towards carbon neutrality. This paper intends to report and visualize the initial analytical results of 60 existing PED projects in Europe about their main characteristics, including geographical information, spatial-temporal scale, energy concepts, building archetypes, finance source, keywords, finance model and challenges/barriers. As a result, a dedicated date base is developed and it could be further expanded/interoperated through an interactive dashboard. It is found that Norway and Italy have the most PED projects so far. Many PED projects state a ‘yearly’ time scale while nearly 1/3 projects have less than 0.2 km2 area in terms of spatial scale. The private investment together with regional/national grants is commonly observed. A mixture of residential, commercial and office/social buildings are found. The most common renewable energy systems include solar energy, district heating/cooling, wind and geothermal energy. Challenges and barriers for PED related projects vary from the planning stage to the implementation stage. Furthermore, the text mining approach is applied to examine the keywords or concentrations of PED-related projects at different stages. These preliminary results are expected to give useful guidance for future PED definitions and proposals of ‘reference PED’.
|
|
