Building Energy Research Articles

Building electrical load forecasting with occupancy data based on wireless sensing

Building electrical load forecasting, as a necessary foundation for building energy management, is of great significance for building energy efficiency and sustainable urban development. However, the accuracy of forecasting can hardly be guaranteed due to the stochastic nature of occupant behavior. To overcome this challenge, this paper proposes a data fusion-based building electrical load forecasting method with occupancy data obtained by wireless sensing technology. Firstly, a wireless sensing scheme is developed, which utilizes pre-existing wireless devices within the building energy management system (BEMS), offering a cost-effective means of obtaining occupancy information without violating occupant privacy. Moreover, to estimate the pattern of occupant behavior in the entire building, an improved stacked sparse auto-encoder (ISSAE) model is developed, which involves unsupervised feature fusion from information sources of varying significance. Finally, to cope with the time-varying and strongly fluctuating building load, a multi-source data fusion forecasting model based on the ensemble deep random vector functional link (edRVFL) is proposed. This model integrates the contributions of the latest accuracy and diversity through the ranking-based dynamic integration strategy. The effectiveness of the proposed method is validated in a commercial building. The experimental results demonstrate that, compared with the load forecasting scheme without occupancy information, the proposed method can improve the forecasting accuracy on RMSE and MAPE by 13.21% and 14.97%, respectively, while cost-effectiveness and privacy are ensured.

Improvement of building energy flexibility with PV battery system based on prediction and load management

Improvement of building energy flexibility with PV battery system based on prediction and load management

Building energy efficiency evaluation based on fusion weight method and grey clustering method

The renovation and evaluation of building energy-saving projects can provide important support for building an energy-saving society. The study proposes using the contract energy management model to analyze building energy-saving projects and construct an evaluation index system. We also innovatively integrated the Analytic Hierarchy Process and Entropy Weight Method to calculate the weights of indicators, in order to leverage the effective influence of subjective and objective factors. Finally, we used Grey Cluster Analysis to obtain the evaluation effect of building energy-saving projects. Through weight calculation and evaluation analysis, it was found that the energy-saving rates of year-end electricity consumption and air conditioning electricity consumption in buildings after energy-saving renovation were 59.80% and 54.95%, respectively. The overall effectiveness of energy-saving buildings was above 50%, indicating a significant energy-saving effect. In the indicator evaluation system, the weight results of energy-saving service company indicators were relatively high, with values of 0.52, 0.48, and 0.51, respectively. The transformation effect was relatively good. The building energy-saving cost and economic benefits obtained from a 65% energy-saving rate were 3 million yuan and 530,000 yuan, respectively, which were significantly better than the simulation results of other energy-saving rates. The contract energy management model based on the fusion weight method and grey clustering method has superiority, which is effective for evaluating building energy-saving projects. It also provides technical reference and scientific suggestions for building energy-saving renovation.

Thermal comfort evaluation using different glazing technologies in an energy model of rural housing in a cold climate

Building energy efficiency, consumption and comfort are aspects to be evaluated in the energy simulation of buildings (BES). However, their results deviate from reality leading to the need to improve their accuracy through calibration. In this regard, a rural house located in a cold climate at more than 3000 meters above sea level, is energetically modeled with its subsequent calibration and evaluation of thermal comfort. The study is developed in four stages, starting with the monitoring of climatological variables and followed by the house energy modeling. Subsequently, manual and iterative calibration is conducted by means of statistical comparison and scatter plots. In this way, a model with more than 80% accuracy between monitored and simulated data is achieved. Based on the previously calibrated model, the thermal comfort is evaluated using different glazing technologies and then, the number of hours for two levels of thermal sensation are evaluated (comfortable and not comfortable). For this purpose, statistical methods such as predicted mean vote (PMV) and predicted percentage of dissatisfied (PPD) are used, which are included in the Design Builder software. This study identified that single bronze glazing technology increased thermal comfort hours by 14.2% with respect to the case study, allowing the development of a methodology that improves thermal comfort through passive heating in rural dwellings in cold climates.

Structural insulation panel system (SIP) as an alternative for cooling energy saving, decarbonization, and energy cost reduction in hot and dry climates. A simulation-based approach

ABSTRACT During the last decades, the growing urban prospects represent a challenging condition to develop climate change adaptation solutions before the urban overheating effects and its challenges to the environment, human health, and building energy consumption, which is critical in hot arid climates with record-breaking heatwaves where society is turning to air conditioning instead of energy-efficient building envelopes. The primary aim of this study was to examine the influence of the Structural Insulation Panel (SIP) system heat transfer in energy-related aspects and contrast it with those of conventional construction systems. Therefore, a comparative study was carried out in low-income dwellings to measure the thermal transmittance of the building envelope and assess the thermal energy simulation allocated in Köppen-Geiger´s hot desert climate, characterized by typical maximum temperatures of 45°C in the summer season. The investigation ascertained the levels of energy consumption, the capacity of the air conditioning system, the associated energy costs, and the potential decrease in carbon footprint. The study shows SIP as a viable construction system alternative, the evidence shows a 20% heat gain reduction, the Heating, Ventilation, and Air Conditioning (HVAC) system capacity was reduced by 38%, and energy cost by 27%. The system also contributes to the decarbonization and carbon footprint by 20%. Likewise, to reduce the heat flow, a crucial element is evident, since transmittance in thermal bridges significantly affects the heat transfer in these low thermal conductivity systems with structural reinforcement elements.

Research on the design of prefabricated curved structure production capacity residential energy system: a case study of an entry of 2022 China International Solar Decathlon Competition- 'Solar Ark 3.0'

Under the severe situation of global warming, the problem of high energy consumption and carbon emissions in the construction industry need to be addressed urgently, and the method to promote the development of the construction industry in the direction of low-carbon sustainability has become a problem to be solved. Taking ‘Solar Ark 3.0’, the entry of Southeast University United Team in the Third China International Solar Decathlon Competition (SDC2022), as an example. This study proposes the wind and solar complementary capacity design, multi-mode energy storage design, intelligent energy use design and production, storage and use of multi-energy complementary intelligent energy management methods through the exploration of the integrated design and construction of curved structure housing and energy system. These designs of ‘Solar Ark 3.0’ improve the utilization efficiency of renewable energy in buildings and also provide methods for the integrated design of curved structural housing and energy systems.

A calibration framework for outdoor thermal comfort index and neutral range: Application in subtropical hot-humid campus

Outdoor thermal comfort affects occupants’ well-being and building energy use. A reliable thermal index tailored to diverse climates is crucial for improving outdoor conditions. Current calibration methods for the neutral thermal range typically set a Thermal Sensation Vote threshold between −0.5 and 0.5. However, this threshold was derived from the indoor-focused Predicted Mean Vote model, which may not be applicable to outdoor conditions. To address this limitation, a revised calibration framework for outdoor scenarios is proposed, taking subtropical hot-humid campus as a case study. The key aspect of this framework is refining the neutral Thermal Sensation Vote interval for outdoor settings. First, an evaluation of the original outdoor thermal comfort index identified the necessity for recalibration. Next, the correlation between Thermal Comfort Vote and Thermal Sensation Vote was established to accurately localize context-specific neutral Thermal Sensation Vote interval. Finally, the proposed framework was applied to UTCI calibration, yielding a modified neutral UTCI range of 4.5–27.2°C through localized regression analysis. This study also highlights the impact of spatial configurations on outdoor thermal comfort, finding higher thermal tolerance within natural landscapes. It provides insights into creating outdoor thermal settings in subtropical hot-humid locations, especially for campus construction or renovation.

Overall Thermal Transfer Analysis of Glazing Facade Design for Passive Building Energy Efficiency

The global warming incremental impacts such as temperature, precipitation, rise in sea level, and extreme weather events are indeed being observed globally. In recent decades, energy demand and greenhouse gas emissions have increased due to buildings being designed with active cooling and heating solutions, despite global attempts to reduce energy consumption. About 50 percent of all energy use is attributed to buildings. There has been a debate for Decades on building active and passive design, but very limited studies have been carried out to confirm the Overall Thermal Transfer Value (OTTV) during the operation phase of the building. This paper highlights the analysis of OTTV in the Passive Design Strategies using several conditions of glazing facades. The passive design of glazing facade strategies includes the variation in opaque wall Colour with different values of the coefficient of solar absorption, change in glazing type (U-Value and Shading Coefficient), and the decrease in the size of the openings. Building parameters were collected and OTTV was determined using the equation in Malaysian Standard MS 1525 for Energy Efficiency. The OTTV was then compared to the recommended value for Malaysia’s tropical climate. Results showed that different paint Colours improved OTTV by up to 23.05%, changing glazing type reduced OTTV from 76.93 W/m² (Base case) to 64.12 W/m² (Double Low-E, e2=.1 Tint green), and reducing glass area by 10% lowered OTTV to 62.24 W/m².Whereas, by combining the Type of Glazing and White facade Colour the OTTV was reduced to 39.68%. It is concluded that this OTTV analysis enhances building energy efficiency and reduces cooling loads.

Hybrid building energy modeling method with parameterized prototype models and rapid calibration

Building energy models were widely used in building energy performance analysis and efficiency improvement, such as building demand response assessment and energy system optimization. However, building energy modeling was a time-consuming and complex process. Therefore, this paper proposed a hybrid building energy modeling method based on parameterized prototype models and rapid calibration. The main feature of the method was the rapid generation of target building energy models using the prototype building energy performance database. The parameterized prototype building energy model considered 13 uncertainty parameters. Based on the range of 13 uncertainty parameters, such as wall U-value and solar heat gain coefficient, 1000 simulations were performed by Monte Carlo sampling to generate the prototype building energy performance database. The target building energy model was quickly calibrated by learning from this database. It filtered the models that met the requirements based on the actual building measurement data. Based on this hybrid modeling method, the rapid modeling tool AutoBPS-Hybrid was developed in a ruby environment. Shopping mall buildings located in three different climate zones were selected as case studies for this study. The results showed that if only one building energy model meeting the percentage errors was needed, only 3–4 simulations were required. If it was necessary to match the real uncertainty parameter distributions, an average of about 53 simulations was needed. The building energy models were applied to plug load and lighting control in buildings. The shopping mall buildings in Harbin, Beijing and Chengdu could reduce energy consumption by 10.18–13.33 kWh/m2, 14.43–18.15 kWh/m2 and 11.54–14.69 kWh/m2 per year, respectively.

Integrated Blockchain and Digital Twin Framework for Sustainable Building Energy Management

The building sector remains a major contributor to increasing energy consumption and emissions. Meanwhile, the energy system is becoming more complex due to the transition to clean energy sources. Current tools and policies struggle to manage this complexity, as the existing infrastructure was not designed for such large dynamic distributed energy resources. This creates an urgent need to adopt emerging technologies for enhancing building energy management systems. The objective of this research is to develop a framework that integrates Blockchain and Digital Twin technologies to provide an efficient and trusted energy management platform that supports smart cities communities and to effectively contribute to enhancing the progress of UN Sustainability Development Goals (SDGs), specifically SDG11 and SDG13. The proposed framework comprises four main elements: Blockchain platform, Digital Twin platform, Application Program Interfaces (APIs), and building energy model. Blockchain platform automates energy billing by utilizing digital currency and smart contracts with pre-set pricing tiers and feed in tariffs. Digital Twin platform provides interactive communication and visualization with physical assets. APIs enables seamless interconnectivity between both platforms. The building energy model acts as a prediction tool, and the simulation results are fed to Digital Twin platform to alert system participants in case actual consumption deviates from optimum values. The viability of the proposed framework is demonstrated using a case study of a residential apartment.

A dual-optimization building energy prediction framework based on improved dung beetle algorithm, variational mode decomposition and deep learning

Accurately predicting building energy consumption is essential for enhancing energy utilization efficiency in buildings. However, the inherent volatility and noise in building energy data, caused by diverse user behaviors and potential sensor errors, make significant challenges to energy consumption prediction. To address these issues, a dual-optimization framework (IDBO-VMD-IDBO-BiLSTM) for building energy consumption prediction, which incorporates improved dung beetle optimization algorithm (IDBO), variational mode decomposition (VMD), and bidirectional long short-term memory network (BiLSTM), was proposed. In this framework, IDBO firstly optimizes the VMD by adaptively determining its optimal parameters to decompose the original building energy consumption series into multiple intrinsic modal functions (IMFs) with smoother characteristics, thereby the effect of mitigating data noise. Then, each IMF component is predicted using the BiLSTM model, with IDBO selecting the optimal hyperparameters for BiLSTM. Finally, the individual predictions of each IMF are superimposed and reconstructed to yield the final predictions. To verify the framework’s effectiveness, real energy consumption data from an office building in Shanghai was collected and analyzed in a comprehensive comparison with seven other comparative models. Experimental results suggested that the proposed framework outperformed the comparative models in terms of root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R2), showing both high predictive accuracy and strong robustness. Therefore, the proposed framework can be an effective tool for predicting building energy consumption

Combining geographic information and climate data to develop urban building energy prediction models in Taichung, Taiwan

Climate change in Taiwan has extended and intensified the summer season, leading to a notable surge in energy demand for cooling systems, especially in densely populated regions. Building energy usage is directly correlated with cooling degree hours (CDHs), representing the hourly temperature differential between indoors and outdoors. This study employed high-resolution Taiwan ReAnalysis Downscaling (TReAD) data to develop an urban energy prediction model focusing on localized cooling demand in central Taiwan's urban areas. Validated against actual electricity consumption data, the model achieved an R2 value of 0.76. The study reveals that urban areas exhibit a high cooling demand during the hot season, exceeding 25,000°C-h and with an annual energy consumption of 44-64 kWh/m2. Conversely, rural areas have a lower cooling demand – that is, below 8,000°C-h, with an annual energy consumption of less than 10 kWh/m2.Considering the IPCC's RCP8.5 warming scenario, October shows a 20-40% increase in cooling demand compared to July and May. This underscores the need to address rising energy consumption especially during the early and late stages of the hot season in response to climate change.

Beyond the g-Value: A comparative study of solar control coated glass and spectrally selective glass in terms of building energy efficiency

The thermal efficiency of transparent envelopes is a key factor in building energy consumption and indoor thermal comfort, with the g-value being a critical metric for evaluating these effects. Solar control film glass and low-emissivity (Low-E) glass, recognized for their distinct advantages, are extensively used in construction. However, comprehensive research on their photothermal properties, especially regarding spectral and angular dependencies, remains limited. In this study, a meticulous field experiment was conducted under six distinct conditions during both winter and summer to examine the thermal performance between solar control coated glass (SCCG) and spectrally selective glass (SSG). The results show that, despite having similar heat transfer and solar heat gain coefficients, their indoor peak temperatures can differ by 1.2 °C to 5.6 °C under typical sunny conditions, and variations in cooling and heating energy consumption range from 6.9 % to 14.4 %. Further analysis reveals that the standard g-value overestimates the solar heat gain of SSG by approximately 10 %, with significant discrepancies in the direct transmission heat and secondary heat transfer of 17.8 % and 37.4 %, respectively. In contrast, the standard g-value is more suitable for SCCG, with a measured g-value that is only 1.2 % higher than the standard g-value. The spectral and angular dependencies of both glasses were also compared in this study, and SSG was found to have a strong spectral dependence and a higher angular dependence than SCCG. These findings indicate that SCCG is more beneficial for energy conservation in cold climates when the thermal parameters are identical. Moreover, the research underscores the limitations of current standards and calculation methods for evaluating solar heat gain in transparent envelopes, particularly for glasses with spectral selectivity.

Energy performance and decarbonization evaluation of a novel positive energy building using solar PV

Positive Energy Buildings (PEBs) are vital for reducing energy consumption and promoting renewable energy. This study introduces a progressive method to evaluate the energy performance and decarbonization of a PEB warehouse in Central Taiwan, utilizing Building Integrated Photovoltaic (BIPV) façades and recycled coal ash concrete. The research analyzes building energy performance and renewable energy generation through energy modeling, considering different ventilation configurations and BIPV arrangements. The findings emphasize that a VRF cooling system saves 16.39% energy compared to a chiller system. The study also revealed that improving the arrangement of BIPV on the building façade significantly enhances solar energy generation efficiency even with fewer photovoltaics (PVs). The decarbonization aspect is evaluated by estimating the embodied carbon and CO2 emissions during the operational period. The PEB is projected to have a 70-year operational lifespan, functioning as a PEB for approximately 42 years, a Net Zero Energy Building (NZEB) for the following 10 years, and maintaining energy efficiency for an additional 20 years. The BIPV system enables the building to remain carbon-free for up to 50 years of operation and achieve substantial carbon savings, with 90% CO2 emissions reduction after 70 years, amounting to 243,111 kgCO2e/year. The study concludes that adequate maintenance and preservation can significantly enhance building performance and longevity.

RESEARCH ON OPTIMIZATION OF INDUSTRIAL BUILDING FACADES INTEGRATING WITH PHOTOVOLTAIC POWER

Industrial buildings are important production carriers in urban space, but they also have problems such as high production energy consumption, serious environmental pollution, and poor built interface, which have attracted much attention. Building environment optimization and energy structure adjustment have become the focus of industrial building research. In the context of the national carbon neutral policy, this paper studies the impact of industrial buildings on the urban environment, combines photovoltaic power generation technology, explores the integrated design strategy of CdTe generation glass and industrial building facades, and uses the southwest cement plant project in Lijiang, Yunnan as an example, a practical study was carried out, aiming to use industrial building facade resources to improve the aesthetics and functionality of the building facade at the urban interface of the park. Research shows that the strong plasticity of CdTe photovoltaic glass can not only provide necessary power energy for industrial buildings and optimize the building's energy structure, but its color and material ductility can also improve the aesthetics of industrial building facades. This article provides certain reference and reference significance for subsequent related research.

Optimized design and comparative analysis of double-glazed photovoltaic windows for enhanced light harvesting and energy efficiency in cold regions of China

This study investigates the daylighting performance and energy efficiency optimization strategies of double-glazed photovoltaic windows (DS-STPV) in cold regions of China. By conducting a comprehensive comparative analysis with traditional and energy-efficient window systems, this research aims to identify high-efficiency building solutions tailored to extreme climatic conditions. Amidst the escalating global energy demand and the pressing need for energy conservation and emission reduction, Building-Integrated Photovoltaic (BIPV) technology is increasingly recognized for its potential and value as a critical method for harnessing green energy. However, the widespread adoption of BIPV technology faces several challenges, including cost-effectiveness, conversion efficiency, system stability, and architectural aesthetic integration. Utilizing the T&A House from the 3rd International Solar Decathlon as an empirical case study, this research employs Ecotect and DesignBuilder simulation software to systematically evaluate the daylighting effect and energy performance of DS-STPV. The analysis considers various key design parameters, including photovoltaic cell coverage, window orientation, and window-to-wall ratio. Through refined modeling and multi-dimensional analysis, this study aims to identify the optimal design configurations of DS-STPV windows in cold regions, with the goal of simultaneously achieving superior natural lighting quality and significant building energy efficiency. The findings indicate that a south-facing DS-STPV window design with approximately 30% photovoltaic cell coverage and a window-to-wall ratio of 30% effectively balances daylighting requirements and energy efficiency in cold regions of China. This design strategy not only ensures an abundance of natural light in the room, but also significantly reduces the building’s energy consumption, proving the superior performance of DS-STPV windows in cold climates. In addition, the unique optical properties of DS-STPV windows reduce glare, further improving the overall quality of the indoor environment. In summary, this study provides a robust scientific foundation for the application of DS-STPV windows in cold regions, offering practical guidance and reference for optimizing energy efficiency and facilitating green transformation in future building design.

Accelerating chiller sequencing using dynamic programming

Effective management of heating, ventilation, and air conditioning (HVAC) systems is crucial for enhancing building energy efficiency. Chillers, a significant component of HVAC systems, are responsible for more than 50 % of energy usage of the whole cooling plant, underscoring the importance of optimizing chiller sequencing. Although Model Predictive Control (MPC) has demonstrated efficacy in enhancing chiller performance, its widespread adoption is hindered by its high computational complexity. This study introduces a dynamic programming approach coupled with a 2-step optimization method to expedite chiller sequencing optimization while upholding MPC’s real-time control capabilities. The MPC method implemented in this study integrates a load forecasting model with an artificial neural network (ANN) based chiller model. Through simulation tests utilizing historical HVAC operational data from a commercial building located in Shanghai, the proposed chiller sequencing strategy achieves a 9.73 % decrease in energy consumption. Moreover, the dynamic programming approach, especially in conjunction with the 2-step optimization process, significantly reduces the MPC solution time at each control step from 165 min to 3.61 s, making it a viable solution for real-time MPC deployment. Additionally, the research delves into the influence of the chiller model’s complexity, the optimization strategy, and control horizon on computational efficiency. This research provides a feasible solution to implement chiller sequencing in real buildings to pick up the low-hanging fruits in an effective way.

Incremental learning user profile and deep reinforcement learning for managing building energy in heating water

Deep reinforcement learning (DRL) has garnered growing attention as a data-driven control technique in the field of built environments. However, the existing DRL approaches for managing water systems cannot consider information from multiple time steps, are prone to overestimation, fall into the problem of locally optimal solutions, and fail to cope with time-varying environments, resulting in an inability to minimize energy consumption while considering water comfort and hygiene of occupants. Therefore, this study proposes an incremental learning user profile and deep reinforcement learning (ILUPDRL) method for controlling hot water systems. This study employs hot water user profiles to reflect the hot water demand (HWD) habits. The proposed ILUPDRL addresses the challenges arising from evolving HWD through incremental learning of hot water user profiles. Moreover, to enable the ILUPDRL to consider information from multiple time steps, this study proposes the recurrent proximal policy optimization (RPPO) algorithm and integrates the RPPO into the ILUPDRL. The simulation results show that the ILUPDRL achieves up to 67.53% energy savings while considering the water comfort and water hygiene of occupants.

Deciphering the nonlinear and synergistic role of building energy variables in shaping carbon emissions: A LightGBM- SHAP framework in office buildings

As the global demand for sustainable buildings continues to rise, accurately assessing and managing carbon emissions during the operational phase of buildings has become an urgent challenge. This study employs machine learning and Explainable Artificial Intelligence (XAI) to explore the impact and synergistic effects of energy variables on carbon emissions in office building operations. Initially, energy consumption data from 28 office buildings in China were collected to construct a dataset comprising 24 energy indicators across four dimensions. The Lightweight Gradient Boosting Machine (LightGBM) and Extreme Gradient Boosting (XGBoost) models were used to predict building carbon emission, PV carbon offset, and net carbon emission based on different energy parameters. The SHapley Additive exPlanations (SHAP) method was utilized to interpret the model results from both global and local perspectives. The study results indicate that: (1) The LightGBM model, optimized with GridSearchCV, shows better stability and reliability in predicting carbon emissions. (2) SHAP bar plots和SHAP beeswarm plots reveal that the WWR in the ES dimension and the PVArea in the EP dimension are the most critical variables affecting building carbon emission and PV carbon offset. (3) SHAP dependence plots show that the contributions of energy variables are not independent, with differentiated synergistic effects among variables in emission reduction. (4) SHAP force plots consider interactions among multiple variables, identifying key variable ranges to minimize building carbon emission and maximize PV carbon offset.

Impacts of Climate Change on Energy-Saving Sensitivity of Residential Building Envelope Design Parameters in Three Hot-Dry Cities

Warming climatic conditions are projected to intensify extreme heat events, especially in hot climates, affecting the energy-saving effectiveness of various building envelope design parameters. However, limited studies exist on the energy-saving sensitivity of building envelope design parameters for residential buildings in hot and dry climates under future climate change. This study aims to fill this gap by examining typical residential buildings in three cities in Iraq, classified as hot desert, hot semiarid, and Mediterranean. First, the most common type of residential building was selected as a case study, and its energy modelling was validated using measured energy bills. Second, future climatic weather files were constructed using the latest general circulation models to assess their impacts on building energy consumption. Third, the energy-saving sensitivity of applicable building envelope design parameters under different future climatic conditions was obtained through local and global sensitivity analysis methods. The results of global sensitivity analysis indicate that the solar heat gain coefficient of windows, the specific heat of exterior walls, and the projection of side fins are the most important factors, with standardized regression coefficients (SRC) ranging from -0.92 to 0.62. Notably, the SRC of windows' solar heat gain coefficient is expected to increase under future climate scenarios. In contrast, the significance of the specific heat of exterior walls depends on local climates. Although this study only included envelope design parameters and excluded economic analysis, it provides insights for policymakers and architects to prioritize building envelope design strategies that enhance the climate resilience of residential buildings.