Energy and Buildings最新文献_第2页

Experimental and numerical investigation of vertical temperature gradients in warehouses: Retrofit interventions to manage temperature sensitive products

IF 6.6 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Energy and Buildings

Pub Date : 2025-02-11 DOI: 10.1016/j.enbuild.2025.115456

Mümine Gerçek Şen, Tahsin Başaran

This paper investigates vertical temperature gradients in warehouse design to ensure optimal storage conditions. In warehouses with ceilings over 10.0 m high, buoyancy-driven warm air often causes significant temperature disparities. This study uses a combination of field measurements and computational fluid dynamics (CFD) simulations to measure thermal stratification. It also examines the impact of mechanical systems, such as ceiling-mounted radiant cooling and floor heating. CFD simulations are validated against field data, showing that destratification cooling systems can reduce ceiling temperatures by up to 4.0 °C in summer. These systems can also raise floor temperatures by 7.0 °C during heating. Field data collected over a year show vertical temperature gradients up to 3.0 °C. However, the temperature difference between ceiling and floor remains below 0.2 °C, keeping indoor temperatures within an ideal range of 20.0–24.0 °C year-round. The study highlights the benefits of combining radiant cooling with floor heating to achieve temperature uniformity. Floor heating scenarios generate air velocities of up to 0.8 m/s, with an average velocity of 0.2 m/s. In contrast, ceiling-mounted cooling systems result in slightly lower air velocities, reaching a maximum of 0.5 m/s and an average of 0.1 m/s. This research is especially relevant for temperature-sensitive products, as illustrated by a case study involving cured tobacco bales. The retrofit proposals ensure optimal indoor conditions and reduce vertical temperature gradients. These findings validate the proposed methodology as a reliable approach for managing temperature variations in warehouses handling temperature-sensitive goods.

{"title":"Experimental and numerical investigation of vertical temperature gradients in warehouses: Retrofit interventions to manage temperature sensitive products","authors":"Mümine Gerçek Şen, Tahsin Başaran","doi":"10.1016/j.enbuild.2025.115456","DOIUrl":"10.1016/j.enbuild.2025.115456","url":null,"abstract":"<div><div>This paper investigates vertical temperature gradients in warehouse design to ensure optimal storage conditions. In warehouses with ceilings over 10.0 m high, buoyancy-driven warm air often causes significant temperature disparities. This study uses a combination of field measurements and computational fluid dynamics (CFD) simulations to measure thermal stratification. It also examines the impact of mechanical systems, such as ceiling-mounted radiant cooling and floor heating. CFD simulations are validated against field data, showing that destratification cooling systems can reduce ceiling temperatures by up to 4.0 °C in summer. These systems can also raise floor temperatures by 7.0 °C during heating. Field data collected over a year show vertical temperature gradients up to 3.0 °C. However, the temperature difference between ceiling and floor remains below 0.2 °C, keeping indoor temperatures within an ideal range of 20.0–24.0 °C year-round. The study highlights the benefits of combining radiant cooling with floor heating to achieve temperature uniformity. Floor heating scenarios generate air velocities of up to 0.8 m/s, with an average velocity of 0.2 m/s. In contrast, ceiling-mounted cooling systems result in slightly lower air velocities, reaching a maximum of 0.5 m/s and an average of 0.1 m/s. This research is especially relevant for temperature-sensitive products, as illustrated by a case study involving cured tobacco bales. The retrofit proposals ensure optimal indoor conditions and reduce vertical temperature gradients. These findings validate the proposed methodology as a reliable approach for managing temperature variations in warehouses handling temperature-sensitive goods.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"332 ","pages":"Article 115456"},"PeriodicalIF":6.6,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Control strategies for heat pumps in a residential area under consideration of system operator benefits and grid stability

IF 6.6 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Energy and Buildings

Pub Date : 2025-02-11 DOI: 10.1016/j.enbuild.2025.115442

J. Meiers , M. Ortleb , D. Jonas , L. Tadayon , G. Frey

The energy sector faces challenges due to the increasing use of weather-depending renewables in power generation. The resulting fluctuations must be balanced through storage technologies and Demand Side Management (DSM) methods. Heat pumps are generally recognized as shiftable loads for DSM. More and more heat pump manufacturers in Germany are using the Smart-Grid- (SG-) Ready interface, which enables grid operators on the one hand and system operators on the other hand to control heat pumps for the purpose of DSM aiming at either grid power balancing (grid operator friendly) or to increase the self-consumption rate of the residential energy system (system operator friendly). The presented work aims at a compromise between those two goals. To this end, different control strategies for SG-Ready enabled solar and heat pump systems are implemented in a simulation framework and evaluated for a residential area using different key performance indicators. The results show that a control strategy based on a dynamic price signal (PRBC2) with rule-based control and well-chosen switching points, taking into account considered building energy systems and environmental conditions used here, represents the best compromise between system operator friendly behavior and grid operator serviceability. The choice of switching points for the heat pump in the course of the price signal is crucial here, and must take into account the consumption and generation profiles of the local residential areas. The fulfillment value of the key indicators considered here for the representative residential area is 63.9%, whereas the value with the reference operating strategy, in which the heat pump is operated exclusively in SG-Ready Mode 2, is only 50.6%.

{"title":"Control strategies for heat pumps in a residential area under consideration of system operator benefits and grid stability","authors":"J. Meiers , M. Ortleb , D. Jonas , L. Tadayon , G. Frey","doi":"10.1016/j.enbuild.2025.115442","DOIUrl":"10.1016/j.enbuild.2025.115442","url":null,"abstract":"<div><div>The energy sector faces challenges due to the increasing use of weather-depending renewables in power generation. The resulting fluctuations must be balanced through storage technologies and Demand Side Management (DSM) methods. Heat pumps are generally recognized as shiftable loads for DSM. More and more heat pump manufacturers in Germany are using the Smart-Grid- (SG-) Ready interface, which enables grid operators on the one hand and system operators on the other hand to control heat pumps for the purpose of DSM aiming at either grid power balancing (grid operator friendly) or to increase the self-consumption rate of the residential energy system (system operator friendly). The presented work aims at a compromise between those two goals. To this end, different control strategies for SG-Ready enabled solar and heat pump systems are implemented in a simulation framework and evaluated for a residential area using different key performance indicators. The results show that a control strategy based on a dynamic price signal (<em>PRBC2</em>) with rule-based control and well-chosen switching points, taking into account considered building energy systems and environmental conditions used here, represents the best compromise between system operator friendly behavior and grid operator serviceability. The choice of switching points for the heat pump in the course of the price signal is crucial here, and must take into account the consumption and generation profiles of the local residential areas. The fulfillment value of the key indicators considered here for the representative residential area is 63.9%, whereas the value with the reference operating strategy, in which the heat pump is operated exclusively in SG-Ready Mode 2, is only 50.6%.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"332 ","pages":"Article 115442"},"PeriodicalIF":6.6,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Simulating land surface temperature impacts of proposed land use and land cover plans using an integrated deep neural network approach

IF 6.6 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Energy and Buildings

Pub Date : 2025-02-11 DOI: 10.1016/j.enbuild.2025.115437

Jiongye Li , Yingwei Yan , Rudi Stouffs

The increase in urban temperature driven by rapid urbanization, industrialization, and population growth has posed significant adverse impacts on public health, air quality, and ecosystems. Researchers have employed various machine learning models to simulate urban temperature based on land use/land cover (LULC) and other identified environmental factors, aiming to mitigate urban temperature through optimized LULC planning and other strategies. However, current research lacks a quantitative and spatial assessment of the impact of new LULC designs on land surface temperature (LST), making it challenging for urban planners to effectively utilize these simulations. This study proposes a novel approach that combines the ResNet model, known for its ability to capture complex features, with the U-Net model, which specializes in image segmentation, to assess the impact of LULC changes on LST. Using Singapore as the research site, we trained both ResNet and U-Net models, achieving high accuracy validated by several essential evaluation metrics. Applying the proposed method to assess several redevelopment plans for Paya Lebar Air Base in Singapore, we found that option 1 reduced the area with temperatures exceeding 33°C by 5.52%, while option 2 achieved an 8.77% reduction compared to the current LULC plan. These reductions result from converting airbase land into residential areas, green spaces, and commercial zones. The proposed research method offers urban planners and researchers valuable tools to assess the impacts of proposed LULC plans on LST, ensuring that new urban development strategies align with the goal of mitigating rising temperatures.

{"title":"Simulating land surface temperature impacts of proposed land use and land cover plans using an integrated deep neural network approach","authors":"Jiongye Li , Yingwei Yan , Rudi Stouffs","doi":"10.1016/j.enbuild.2025.115437","DOIUrl":"10.1016/j.enbuild.2025.115437","url":null,"abstract":"<div><div>The increase in urban temperature driven by rapid urbanization, industrialization, and population growth has posed significant adverse impacts on public health, air quality, and ecosystems. Researchers have employed various machine learning models to simulate urban temperature based on land use/land cover (LULC) and other identified environmental factors, aiming to mitigate urban temperature through optimized LULC planning and other strategies. However, current research lacks a quantitative and spatial assessment of the impact of new LULC designs on land surface temperature (LST), making it challenging for urban planners to effectively utilize these simulations. This study proposes a novel approach that combines the ResNet model, known for its ability to capture complex features, with the U-Net model, which specializes in image segmentation, to assess the impact of LULC changes on LST. Using Singapore as the research site, we trained both ResNet and U-Net models, achieving high accuracy validated by several essential evaluation metrics. Applying the proposed method to assess several redevelopment plans for Paya Lebar Air Base in Singapore, we found that option 1 reduced the area with temperatures exceeding 33°C by 5.52%, while option 2 achieved an 8.77% reduction compared to the current LULC plan. These reductions result from converting airbase land into residential areas, green spaces, and commercial zones. The proposed research method offers urban planners and researchers valuable tools to assess the impacts of proposed LULC plans on LST, ensuring that new urban development strategies align with the goal of mitigating rising temperatures.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"332 ","pages":"Article 115437"},"PeriodicalIF":6.6,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Developing reference building models for the non-residential sector to support public policies in Brazil

IF 6.6 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Energy and Buildings

Pub Date : 2025-02-11 DOI: 10.1016/j.enbuild.2025.115419

Matheus Geraldi, Liége Garlet, Natasha Gapski, Tiago Quevedo, Ana Paula Melo, Roberto Lamberts

This study introduces a methodology to develop reference building models for energy simulations in non-residential building sector in Brazil. The method uses data from national energy efficiency regulations and research projects to model thermal properties, internal gains, schedules of operation, HVAC systems, and geometries obtained from representative archetypes. These reference models represent typical characteristics of the Brazilian commercial building stock and are designed to assist in predicting energy consumption and support the Brazilian Building Energy Labeling Program. Benchmarks for thermal loads and electricity end-uses were calculated and compared across building types in Brazil, revealing significant variations in performance. Generalization analysis of the models showed the relevance of building orientation and operational schedules on energy use, highlighting their importance in the analysis. Reference building energy use intensity (EUI) was also compared with actual data from the Brazilian building stock, demonstrating that most reference models have a higher EUI than the average but remain below maximum values. This work fills a gap in the development of reference buildings for the non-residential sector in Brazil, offering validated models that can be applied in energy planning and used to evaluate the impact of new technologies in specific building types.

{"title":"Developing reference building models for the non-residential sector to support public policies in Brazil","authors":"Matheus Geraldi, Liége Garlet, Natasha Gapski, Tiago Quevedo, Ana Paula Melo, Roberto Lamberts","doi":"10.1016/j.enbuild.2025.115419","DOIUrl":"10.1016/j.enbuild.2025.115419","url":null,"abstract":"<div><div>This study introduces a methodology to develop reference building models for energy simulations in non-residential building sector in Brazil. The method uses data from national energy efficiency regulations and research projects to model thermal properties, internal gains, schedules of operation, HVAC systems, and geometries obtained from representative archetypes. These reference models represent typical characteristics of the Brazilian commercial building stock and are designed to assist in predicting energy consumption and support the Brazilian Building Energy Labeling Program. Benchmarks for thermal loads and electricity end-uses were calculated and compared across building types in Brazil, revealing significant variations in performance. Generalization analysis of the models showed the relevance of building orientation and operational schedules on energy use, highlighting their importance in the analysis. Reference building energy use intensity (EUI) was also compared with actual data from the Brazilian building stock, demonstrating that most reference models have a higher EUI than the average but remain below maximum values. This work fills a gap in the development of reference buildings for the non-residential sector in Brazil, offering validated models that can be applied in energy planning and used to evaluate the impact of new technologies in specific building types.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"332 ","pages":"Article 115419"},"PeriodicalIF":6.6,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Examining the reasons for changes in buildings’ energy consumption in the United States, China and the European Union

IF 6.6 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Energy and Buildings

Pub Date : 2025-02-11 DOI: 10.1016/j.enbuild.2025.115461

M. González-Torres , L. Pérez-Lombard , E.L. Clementi , J.F. Coronel

Buildings are responsible for one third of global operational energy consumption and greenhouse gas (GHG) emissions. Addressing their impact requires the development and monitoring of effective policies, supported by detailed and costly data on building stock and energy use as well as their corresponding analysis. The paper proposes a pyramidal approach to decompose buildings’ energy use into drivers —activity, structure, and efficiency— considering factors like population, floor area, urbanisation, building size, occupancy and climate. Energy-use intensity measures efficiency, while shifts among the residential and tertiary subsectors are captured as structural impacts. The relevance of the methodology is underscored by its potential to assess and quantify the causes of energy consumption changes, guiding policy-making. Applying this approach to China, the United States (US) and the European Union (EU), the paper criticises the lack of data, disaggregates energy consumption changes, outlines policy implications and validates the methodology’s added value. The analysis reveals the increased floor area as the primary driver of rising energy consumption over the past two decades (contributing to changes by 9% in the US, 24% in the EU and 97% in China). This may be reduced by managing urbanisation rates and compensated by an improvement in efficiency. While this has been sufficient to stabilise consumption in the EU, a slight rebound is observed in the US due to the increase in population and in the demand for buildings per capita. In China, the urbanisation trend behind the rise in energy demand is approaching EU levels, highlighting the importance of mindful actions to ensure the sustainability of future expansion. Despite the limited geographical coverage, this study provides a pertinent analysis of almost half of the building energy consumption in the world (China, the US and the EU), offering insights into the sector’s current state and directions for future policy development.

{"title":"Examining the reasons for changes in buildings’ energy consumption in the United States, China and the European Union","authors":"M. González-Torres , L. Pérez-Lombard , E.L. Clementi , J.F. Coronel","doi":"10.1016/j.enbuild.2025.115461","DOIUrl":"10.1016/j.enbuild.2025.115461","url":null,"abstract":"<div><div>Buildings are responsible for one third of global operational energy consumption and greenhouse gas (GHG) emissions. Addressing their impact requires the development and monitoring of effective policies, supported by detailed and costly data on building stock and energy use as well as their corresponding analysis. The paper proposes a pyramidal approach to decompose buildings’ energy use into drivers —activity, structure, and efficiency— considering factors like population, floor area, urbanisation, building size, occupancy and climate. Energy-use intensity measures efficiency, while shifts among the residential and tertiary subsectors are captured as structural impacts. The relevance of the methodology is underscored by its potential to assess and quantify the causes of energy consumption changes, guiding policy-making. Applying this approach to China, the United States (US) and the European Union (EU), the paper criticises the lack of data, disaggregates energy consumption changes, outlines policy implications and validates the methodology’s added value. The analysis reveals the increased floor area as the primary driver of rising energy consumption over the past two decades (contributing to changes by 9% in the US, 24% in the EU and 97% in China). This may be reduced by managing urbanisation rates and compensated by an improvement in efficiency. While this has been sufficient to stabilise consumption in the EU, a slight rebound is observed in the US due to the increase in population and in the demand for buildings per capita. In China, the urbanisation trend behind the rise in energy demand is approaching EU levels, highlighting the importance of mindful actions to ensure the sustainability of future expansion. Despite the limited geographical coverage, this study provides a pertinent analysis of almost half of the building energy consumption in the world (China, the US and the EU), offering insights into the sector’s current state and directions for future policy development.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"332 ","pages":"Article 115461"},"PeriodicalIF":6.6,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI-driven design optimization for sustainable buildings: A systematic review

IF 6.6 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Energy and Buildings

Pub Date : 2025-02-10 DOI: 10.1016/j.enbuild.2025.115440

Piragash Manmatharasan , Girma Bitsuamlak , Katarina Grolinger

Buildings are major contributors to global carbon emissions, accounting for a substantial portion of energy consumption and environmental impact. This situation presents a critical opportunity for energy conservation through strategic interventions in both building design and operational phases. Artificial Intelligence (AI) has emerged as a transformative approach in this context, enhancing the efficiency and precision of energy management efforts. In the operational phase, AI is extensively utilized as smart controllers for Heating, Ventilation, and Air Conditioning (HVAC) systems and passive energy gains, as well as for fault detection. In the design phase, AI is pivotal as a surrogate model, enabling rapid and accurate evaluation of design options and allowing designers to optimize building performance with minimal computational resources. As the early-stage optimization is more cost-effective than post-construction modifications, design phase optimization has a great potential. Consequently, this paper examines recent advancements in surrogate-assisted design optimization for sustainable buildings, providing a comprehensive overview of the entire optimization process, from data preparation and surrogate model training to final optimization. The review categorizes studies based on experimental approaches and methodologies, identifying trends, gaps, and opportunities in the field. Notably, it highlights how modern AI techniques can incorporate previously unexplored dimensions into surrogate-assisted optimization, broadening the scope and potential of surrogate models. Therefore, this study provides guidance for future research and practical applications of AI-driven strategies in sustainable building practices.

{"title":"AI-driven design optimization for sustainable buildings: A systematic review","authors":"Piragash Manmatharasan , Girma Bitsuamlak , Katarina Grolinger","doi":"10.1016/j.enbuild.2025.115440","DOIUrl":"10.1016/j.enbuild.2025.115440","url":null,"abstract":"<div><div>Buildings are major contributors to global carbon emissions, accounting for a substantial portion of energy consumption and environmental impact. This situation presents a critical opportunity for energy conservation through strategic interventions in both building design and operational phases. Artificial Intelligence (AI) has emerged as a transformative approach in this context, enhancing the efficiency and precision of energy management efforts. In the operational phase, AI is extensively utilized as smart controllers for Heating, Ventilation, and Air Conditioning (HVAC) systems and passive energy gains, as well as for fault detection. In the design phase, AI is pivotal as a surrogate model, enabling rapid and accurate evaluation of design options and allowing designers to optimize building performance with minimal computational resources. As the early-stage optimization is more cost-effective than post-construction modifications, design phase optimization has a great potential. Consequently, this paper examines recent advancements in surrogate-assisted design optimization for sustainable buildings, providing a comprehensive overview of the entire optimization process, from data preparation and surrogate model training to final optimization. The review categorizes studies based on experimental approaches and methodologies, identifying trends, gaps, and opportunities in the field. Notably, it highlights how modern AI techniques can incorporate previously unexplored dimensions into surrogate-assisted optimization, broadening the scope and potential of surrogate models. Therefore, this study provides guidance for future research and practical applications of AI-driven strategies in sustainable building practices.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"332 ","pages":"Article 115440"},"PeriodicalIF":6.6,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Liquid sorption storage for high solar fraction heat supply in residential buildings under different climatic conditions

IF 6.6 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Energy and Buildings

Pub Date : 2025-02-10 DOI: 10.1016/j.enbuild.2025.115446

Robert Weber , Benjamin Fumey , Luca Baldini

Thermochemical energy storage is an attractive option for seasonal thermal energy storage, particularly in building applications. However, several research gaps in the field of sorption storage systems such as restricted focus on specific reactor concepts or sorption couples or lack of systematic performance studies hinder their practical implementation. This study addresses these gaps by evaluating the performance and cost-effectiveness of a solar thermal space heating system integrated with liquid sorption storage across various building types (single and multi-family homes with different envelope qualities) and climates (Zurich, Switzerland; Harbin, China; Helsinki, Finland). The study systematically investigates the impact of different sizes of individual system components (number of ground heat exchangers, solar collector area, sorption reactor capacity, size and distribution of thermal buffers) on the overall system performance using a previously presented greybox sorption reactor model based on a lab-scale prototype. The simulation results demonstrate that high solar fractions above 80 % can be achieved with long-term sorption storage. To reach this, substantial storage volumes of around 0.8––1 m³ per m² of solar collector area are needed for the multi-family home cases in Zurich climate despite the increased volumetric energy density of sorption storage when compared to classical water storage. This emphasizes the significance of building envelope quality, available roof area, and careful system component sizing for enhancing solar fractions and cost-effective renewable heat generation. The findings provide valuable insights into optimizing sorption storage systems, fostering the practical implementation of renewable energy solutions for space heating in buildings.

{"title":"Liquid sorption storage for high solar fraction heat supply in residential buildings under different climatic conditions","authors":"Robert Weber , Benjamin Fumey , Luca Baldini","doi":"10.1016/j.enbuild.2025.115446","DOIUrl":"10.1016/j.enbuild.2025.115446","url":null,"abstract":"<div><div>Thermochemical energy storage is an attractive option for seasonal thermal energy storage, particularly in building applications. However, several research gaps in the field of sorption storage systems such as restricted focus on specific reactor concepts or sorption couples or lack of systematic performance studies hinder their practical implementation. This study addresses these gaps by evaluating the performance and cost-effectiveness of a solar thermal space heating system integrated with liquid sorption storage across various building types (single and multi-family homes with different envelope qualities) and climates (Zurich, Switzerland; Harbin, China; Helsinki, Finland). The study systematically investigates the impact of different sizes of individual system components (number of ground heat exchangers, solar collector area, sorption reactor capacity, size and distribution of thermal buffers) on the overall system performance using a previously presented greybox sorption reactor model based on a lab-scale prototype. The simulation results demonstrate that high solar fractions above 80 % can be achieved with long-term sorption storage. To reach this, substantial storage volumes of around 0.8––1 m<sup>3</sup> per m<sup>2</sup> of solar collector area are needed for the multi-family home cases in Zurich climate despite the increased volumetric energy density of sorption storage when compared to classical water storage. This emphasizes the significance of building envelope quality, available roof area, and careful system component sizing for enhancing solar fractions and cost-effective renewable heat generation. The findings provide valuable insights into optimizing sorption storage systems, fostering the practical implementation of renewable energy solutions for space heating in buildings.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"332 ","pages":"Article 115446"},"PeriodicalIF":6.6,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Understanding the impacts of space design on local outdoor thermal comfort: An approach combining DepthmapX and XGBoost

IF 6.6 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Energy and Buildings

Pub Date : 2025-02-10 DOI: 10.1016/j.enbuild.2025.115451

Ye Xia , Weisheng Lu , Ziyu Peng , Jinfeng Lou , Jianxiang Huang , Jianlei Niu

Researchers and designers alike are keen to explore proper space designs to alleviate the heat waves. A niche research area of this type focuses on the smaller scale, local communities to achieve the so-called ‘cool spots’ for residents. However, despite the momentum gained so far, it remains unclear what the key design elements are (e.g., space orientation, aspect ratio, or building density) and how they interact with each other in impacting outdoor thermal comfort (OTC), particularly in some complex, vertical ‘spots’. This research aims to provide an improved understanding of cool spot reasoning by proposing a new paradigm to engage DepthmapX (an analytic tool for urban spatial configuration), XGBoost (a machine learning tool), and on-site verification. By implementing the paradigm on a university campus in Hong Kong for the case study, it was discovered that Percentage of View (PV), Average Height Index (AHI), and Connectivity (CON) are the three most influential factors leading to the formation of a ‘cool spot’. DepthmapX can not only help quantify space designs but also help translate the indexes back to real-life space design options. The XGBoost can help better interpret the pathway from different space design indexes to different OTC but more explainable causal relationships are desired. This research advanced our understanding of the impacts of different space designs on OTC and provided references to designers in achieving OTC in smaller--scale, local communities. It also opens a new avenue to understand the causal relationships in a more detailed and explainable fashion.

{"title":"Understanding the impacts of space design on local outdoor thermal comfort: An approach combining DepthmapX and XGBoost","authors":"Ye Xia , Weisheng Lu , Ziyu Peng , Jinfeng Lou , Jianxiang Huang , Jianlei Niu","doi":"10.1016/j.enbuild.2025.115451","DOIUrl":"10.1016/j.enbuild.2025.115451","url":null,"abstract":"<div><div>Researchers and designers alike are keen to explore proper space designs to alleviate the heat waves. A niche research area of this type focuses on the smaller scale, local communities to achieve the so-called ‘cool spots’ for residents. However, despite the momentum gained so far, it remains unclear what the key design elements are (e.g., space orientation, aspect ratio, or building density) and how they interact with each other in impacting outdoor thermal comfort (OTC), particularly in some complex, vertical ‘spots’. This research aims to provide an improved understanding of cool spot reasoning by proposing a new paradigm to engage DepthmapX (an analytic tool for urban spatial configuration), XGBoost (a machine learning tool), and on-site verification. By implementing the paradigm on a university campus in Hong Kong for the case study, it was discovered that Percentage of View (PV), Average Height Index (AHI), and Connectivity (CON) are the three most influential factors leading to the formation of a ‘cool spot’. DepthmapX can not only help quantify space designs but also help translate the indexes back to real-life space design options. The XGBoost can help better interpret the pathway from different space design indexes to different OTC but more explainable causal relationships are desired. This research advanced our understanding of the impacts of different space designs on OTC and provided references to designers in achieving OTC in smaller--scale, local communities. It also opens a new avenue to understand the causal relationships in a more detailed and explainable fashion.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"332 ","pages":"Article 115451"},"PeriodicalIF":6.6,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Establishing a local energy planning and evaluation system prototype to support decarbonized community development

IF 6.6 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Energy and Buildings

Pub Date : 2025-02-10 DOI: 10.1016/j.enbuild.2025.115450

Yujiro Hirano , Kyoichiro Isozaki , Kenichi Adachi , Tsuyoshi Fijita , Kei Gomi , Tsuyoshi Yoshioka , Yukiko Yoshida

The introduction and spread of local energy systems by local governments and other local entities have been attracting attention; however, considering measures tailored to local conditions faces various challenges, and the development of versatile methods has become an important issue. This study aims to develop a prototype of a local energy planning and evaluation system based on detailed energy management simulations, considering regional conditions as a scientific tool. This system can perform simulations that combine electricity and heat demand patterns, renewable energy sources (solar and wind power), district heating and cooling using cogeneration systems, storage batteries and electric vehicles (EVs) used to adjust the supply and demand balance, and hydrogen production, and can consider the optimal system for each region. The developed prototype is designed for scalability across various regions and features an intuitive interface, ensuring ease of use for local government policymakers and energy company employees. Using this system, we evaluated the local energy business development in Shinchi, Fukushima Prefecture, Japan, as a case study. Specifically, we focused on the introduction of a local energy supply through cogeneration and quantified the CO₂ reduction effect obtained by expanding the supply area, increasing the supply of renewable energy, and introducing various energy management technologies to optimize the supply–demand balance.

{"title":"Establishing a local energy planning and evaluation system prototype to support decarbonized community development","authors":"Yujiro Hirano , Kyoichiro Isozaki , Kenichi Adachi , Tsuyoshi Fijita , Kei Gomi , Tsuyoshi Yoshioka , Yukiko Yoshida","doi":"10.1016/j.enbuild.2025.115450","DOIUrl":"10.1016/j.enbuild.2025.115450","url":null,"abstract":"<div><div>The introduction and spread of local energy systems by local governments and other local entities have been attracting attention; however, considering measures tailored to local conditions faces various challenges, and the development of versatile methods has become an important issue. This study aims to develop a prototype of a local energy planning and evaluation system based on detailed energy management simulations, considering regional conditions as a scientific tool. This system can perform simulations that combine electricity and heat demand patterns, renewable energy sources (solar and wind power), district heating and cooling using cogeneration systems, storage batteries and electric vehicles (EVs) used to adjust the supply and demand balance, and hydrogen production, and can consider the optimal system for each region. The developed prototype is designed for scalability across various regions and features an intuitive interface, ensuring ease of use for local government policymakers and energy company employees. Using this system, we evaluated the local energy business development in Shinchi, Fukushima Prefecture, Japan, as a case study. Specifically, we focused on the introduction of a local energy supply through cogeneration and quantified the CO<sub>2</sub> reduction effect obtained by expanding the supply area, increasing the supply of renewable energy, and introducing various energy management technologies to optimize the supply–demand balance.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"332 ","pages":"Article 115450"},"PeriodicalIF":6.6,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-driven pre-training framework for reinforcement learning of air-source heat pump (ASHP) systems based on historical data in office buildings: Field validation

IF 6.6 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Energy and Buildings

Pub Date : 2025-02-10 DOI: 10.1016/j.enbuild.2025.115436

Wenqi Zhang , Yong Yu , Zhongyuan Yuan , Peipei Tang , Bo Gao

Reinforcement Learning (RL) has demonstrated potential for optimal control of Heating, Ventilation, and Air Conditioning (HVAC) systems. Current research on RL in HVAC systems control is limited to simulation studies, with few real-world deployments that have minimal focus on supply-side optimization, along with a reliance on building simulation tools for pre-training. This paper proposes a practical data-driven pre-training framework for Air-Source Heat Pump (ASHP) system. The framework integrates data-driven models based on real-world historical data for load forecasting, equipment energy consumption, and heat transfer. As a case study, two classic value-based reinforcement learning agents, Q-learning and Deep Q-Network (DQN), were selected to dynamically control the number and frequency of pumps and the supply water temperature based on the fluctuating outdoor dry bulb temperature and building cooling load. The pre-training results indicate that DQN achieved energy-saving rates of 4.70% for the training data and 4.65% for the testing data, while Q-learning performed at -0.66% and 1.28% respectively, indicating that both agents outperformed historical control strategies, thereby demonstrating the effectiveness of the pre-training framework. After pre-training, each agent was deployed back into the real-world system for two days of field validation. During deployment, both agents outperformed the rule-based control, with DQN achieving an energy-saving rate of 9.28% and Q-learning achieving 9.04%, demonstrating that the proposed framework enables RL agents to continue real-world learning with an enhanced control strategy. This study provides a novel pre-training paradigm for implementing RL agents in supply-side control of HVAC systems, potentially enhancing both RL deployment and its online evolution.

{"title":"Data-driven pre-training framework for reinforcement learning of air-source heat pump (ASHP) systems based on historical data in office buildings: Field validation","authors":"Wenqi Zhang , Yong Yu , Zhongyuan Yuan , Peipei Tang , Bo Gao","doi":"10.1016/j.enbuild.2025.115436","DOIUrl":"10.1016/j.enbuild.2025.115436","url":null,"abstract":"<div><div>Reinforcement Learning (RL) has demonstrated potential for optimal control of Heating, Ventilation, and Air Conditioning (HVAC) systems. Current research on RL in HVAC systems control is limited to simulation studies, with few real-world deployments that have minimal focus on supply-side optimization, along with a reliance on building simulation tools for pre-training. This paper proposes a practical data-driven pre-training framework for Air-Source Heat Pump (ASHP) system. The framework integrates data-driven models based on real-world historical data for load forecasting, equipment energy consumption, and heat transfer. As a case study, two classic value-based reinforcement learning agents, Q-learning and Deep Q-Network (DQN), were selected to dynamically control the number and frequency of pumps and the supply water temperature based on the fluctuating outdoor dry bulb temperature and building cooling load. The pre-training results indicate that DQN achieved energy-saving rates of 4.70% for the training data and 4.65% for the testing data, while Q-learning performed at -0.66% and 1.28% respectively, indicating that both agents outperformed historical control strategies, thereby demonstrating the effectiveness of the pre-training framework. After pre-training, each agent was deployed back into the real-world system for two days of field validation. During deployment, both agents outperformed the rule-based control, with DQN achieving an energy-saving rate of 9.28% and Q-learning achieving 9.04%, demonstrating that the proposed framework enables RL agents to continue real-world learning with an enhanced control strategy. This study provides a novel pre-training paradigm for implementing RL agents in supply-side control of HVAC systems, potentially enhancing both RL deployment and its online evolution.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"332 ","pages":"Article 115436"},"PeriodicalIF":6.6,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0