Building Energy Optimization Research Articles

Building energy management model integrating rule-based control algorithm and genetic algorithm

Energy is a crucial material foundation for the development of human society. Building energy consumption accounts for a significant proportion of global energy consumption. Optimizing building energy management is of great significance for achieving sustainable development. A building energy management model that integrates rule-based control algorithm and genetic algorithm is proposed, aiming to optimize building energy utilization and reduce operating costs. Mathematical models for different devices in the building energy system are established, and the rule-based control algorithm is used to provide system decision support. Then, the genetic algorithm is integrated to address the complexity and uncertainty of energy optimization problems. The comparative test results showed that the proposed fusion algorithm had higher fitness values and faster convergence speed. The root mean square errors of the algorithm in the training and testing sets were 43.6544 and 43.6844, with the lowest error and highest accuracy among the four algorithms. The simulation experiment results showed that the building energy management model integrating rule-based control algorithm and genetic algorithm had energy expenditures of 788.3 yuan and 967.6 yuan for two types of buildings, respectively. Taking Building 1 as an example, compared with Supervisory Control and Data Acquisition (SCADA), Beetle Antennae Search and Particle Swarm Optimization (BAS-PSO) algorithm, and Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN) algorithm, the proposed model reduced the cost of energy consumption optimization by 39.30%, 28.32%, and 20.20%, respectively. Overall, the proposed building energy management model effectively reduces operating costs, utilizes building energy, and contributes to daily building energy management and decision support.

Optimizing Building Energy Efficiency: A Novel BIM-Based Approach to Quantification of Thermal Bridges

Optimizing Building Energy Efficiency: A Novel BIM-Based Approach to Quantification of Thermal Bridges

Building Information Modeling (BIM) for Energy Optimization in Green Buildings: A Case Study of a Residential Villa in the UAE

This paper explores the application of Building Information Modeling (BIM) in evaluating and optimizing energy efficiency in green buildings, focusing on a residential villa in Sharjah, UAE. The study assesses thermal bridges, energy consumption, and the potential energy savings from applying insulation. The villa’s energy performance was modeled using Revit and IES-VE software, and actual energy consumption was measured for comparison. The results show that applying insulation could reduce the villa's energy consumption by up to 17.91 MWh annually, confirming the accuracy of BIM in simulating energy performance. Additionally, this paper discusses how improvements in insulation and thermal management can significantly reduce the energy demands of residential buildings in hot climates.

Building Energy Consumption Prediction and Optimization

As the proportion of building energy consumption in global energy consumption is significant and continues to rise, accurate prediction and optimization of building energy consumption are of great significance. This research aims to develop high-precision prediction models and innovative optimization strategies to address the problems of insufficient accuracy of existing prediction methods and poor optimization effects. By collecting and integrating data through multiple channels, a hybrid model architecture combining LSTM, CNN, and the attention mechanism is selected, and an automatic search and optimization method for hyperparameters is adopted. In terms of data processing, multi-source heterogeneous data fusion, data cleaning and enhancement based on spatiotemporal features, and innovative feature engineering are carried out. In the experiments, the dataset is divided considering the dynamic use of buildings and environmental changes, and indicators related to energy costs are introduced for evaluation. The research successfully constructed a model for accurately predicting building energy consumption, proposed effective optimization strategies, reduced energy consumption, and ensured comfort, providing a basis for practical applications through sensitivity analysis and robustness tests.

Sinergym – A virtual testbed for building energy optimization with Reinforcement Learning

Simulation has become a crucial tool for Building Energy Optimization (BEO) as it enables the evaluation of different design and control strategies at a low cost. Machine Learning (ML) algorithms can leverage large-scale simulations to learn optimal control from vast amounts of data without supervision, particularly under the Reinforcement Learning (RL) paradigm. Unfortunately, the lack of open and standardized tools has hindered the widespread application of ML and RL to BEO. To address this issue, this paper presents Sinergym, an open-source Python-based virtual testbed for large-scale building simulation, data collection, continuous control, and experiment monitoring. Sinergym provides a consistent interface for training and running controllers, predefined benchmarks, experiment visualization and replication support, and comprehensive documentation in a ready-to-use software library. This paper 1) highlights the main features of Sinergym in comparison to other existing frameworks, 2) describes its basic usage, and 3) demonstrates its applicability for RL-based BEO through several representative examples. By integrating simulation, data, and control, Sinergym supports the development of intelligent, data-driven applications for more efficient and responsive building operations, aligning with the objectives of digital twin technology.

AI-driven decarbonization of buildings: Leveraging predictive analytics and automation for sustainable energy management

The decarbonization of buildings is a pivotal strategy in addressing global climate change, and artificial intelligence (AI) is increasingly recognized as a powerful tool in this process. This paper explores the application of AI in optimizing building energy consumption, focusing on predictive analytics, real-time energy monitoring, and smart energy systems. By leveraging machine learning algorithms, AI enhances energy efficiency through precise automation, particularly in heating, ventilation, and air conditioning (HVAC) systems. AI-powered demand response solutions dynamically adjust energy use based on factors like occupancy, weather patterns, and energy prices, further minimizing carbon footprints. Additionally, the integration of renewable energy sources such as solar and wind with AI-driven energy management systems enables smarter utilization of clean energy. The paper examines both new construction projects and the retrofitting of existing infrastructures, offering insights into AI’s role in reducing carbon emissions and improving energy sustainability. Case studies highlight successful implementations, underscoring AI’s ability to drive significant energy savings and carbon reduction. The study also discusses challenges such as data privacy, the high cost of AI technology, and the regulatory framework required for widespread adoption. The findings present AI as an essential component of the future of sustainable, carbon-neutral buildings.

Integrating Machine Learning and Genetic Algorithms to Optimize Building Energy and Thermal Efficiency Under Historical and Future Climate Scenarios

As the global energy demand rises and climate change creates more challenges, optimizing the performance of non-residential buildings becomes essential. Traditional simulation-based optimization methods often fall short due to computational inefficiency and their time-consuming nature, limiting their practical application. This study introduces a new optimization framework that integrates Bayesian optimization, XGBoost algorithms, and multi-objective genetic algorithms (GA) to enhance building performance metrics—total energy (TE), indoor overheating degree (IOD), and predicted percentage dissatisfied (PPD)—for historical (2020), mid-future (2050), and future (2080) scenarios. The framework employs IOD as a key performance indicator (KPI) to optimize building design and operation. While traditional indices such as the predicted mean vote (PMV) and the thermal sensation vote (TSV) are widely used, they often fail to capture individual comfort variations and the dynamic nature of thermal conditions. IOD addresses these gaps by providing a comprehensive and objective measure of thermal discomfort, quantifying both the frequency and severity of overheating events. Alongside IOD, the energy use intensity (EUI) index is used to assess energy consumption per unit area, providing critical insights into energy efficiency. The integration of IOD with EUI and PPD enhances the overall assessment of building performance, creating a more precise and holistic framework. This combination ensures that energy efficiency, thermal comfort, and occupant well-being are optimized in tandem. By addressing a significant gap in existing methodologies, the current approach combines advanced optimization techniques with modern simulation tools such as EnergyPlus, resulting in a more efficient and accurate model to optimize building performance. This framework reduces computational time and enhances practical application. Utilizing SHAP (SHapley Additive Explanations) analysis, this research identified key design factors that influence performance metrics. Specifically, the window-to-wall ratio (WWR) impacts TE by increasing energy consumption through higher heat gain and cooling demand. Outdoor temperature (Tout) has a complex effect on TE depending on seasonal conditions, while indoor temperature (Tin) has a minor impact on TE. For PPD, Tout is a major negative factor, indicating that improved natural ventilation can reduce thermal discomfort, whereas higher Tin and larger open areas exacerbate it. Regarding IOD, both WWR and Tin significantly affect internal heat gains, with larger windows and higher indoor temperatures contributing to increased heat and reduced thermal comfort. Tout also has a positive impact on IOD, with its effect varying over time. This study demonstrates that as climate conditions evolve, the effects of WWR and open areas on TE become more pronounced, highlighting the need for effective management of building envelopes and HVAC systems.

Artificial Neural Network to Predict Curvature Light Shelf Design Related Daylighting Optimization on Office Spaces

Energy Optimization in building design field now has been revolutionized due to AI and machine learning applications. Leveraging daylight to reduce artificial lighting consumption holds promise for significant energy savings, yet the nonlinear nature of daylight patterns poses challenges in prediction and optimization. This study proposes a novel approach to automated light shelf design using machine learning algorithms, specifically artificial neural networks (ANNs) such as recurrent neural networks (RNN) by long short term memory (LSTM), to accelerate daylighting simulation and optimization processes. The methodology employed two distinct approaches: Firstly, we employed the theoretical-analytical approach to explore methods for utilizing machine learning in natural lighting and light shelf parameters. Second, the practical and applied method involved creating a predictive model for designing the light shelf using appropriate AI and ML techniques. This model is based on an office geometry model at the El Arish weather file in Egypt, four-dimensional training room models with three internal zones-oriented south. Rhinoceros and Grasshopper, two parametric simulation tools, are used to normalize and optimize light shelf parameters like geometry cross-section, curvature surface, width, height position, depth, tilt angle, and reflectance materials. Then, the Galapagos plug-in and Colibri2 are used for dataset creation and optimization. The results demonstrate that automated light shelf operation has a significant impact on internal daylighting quality. RNNs enable rapid prediction of optimization models, reducing time consumption in the early design phase. ML facilitates decision-making by generating evaluative criteria from user-selected design options. RNNs were classified as good and bad and used LSTM to enhance prediction accuracy for efficient illuminance values metric in zones 1 and 2. Challenges include increasing the simulation procedure's efficiency. The results of model accuracy reached 99%. Hence, future research should prioritize resolving the previously identified concerns. In conclusion, this study underscores the importance of ML-driven approaches in early design phases to optimize building energy consumption and pave the way for sustainable architectural practices.

Comparison of ANN and XGBoost surrogate models trained on small numbers of building energy simulations.

Surrogate optimisation holds a big promise for building energy optimisation studies due to its goal to replace the use of lengthy building energy simulations within an optimisation step with expendable local surrogate models that can quickly predict simulation results. To be useful for such purpose, it should be possible to quickly train precise surrogate models from a small number of simulation results (10-100) obtained from appropriately sampled points in the desired part of the design space. Two sampling methods and two machine learning models are compared here. Latin hypercube sampling (LHS), widely accepted in building energy community, is compared to an exploratory Monte Carlo-based sequential design method mc-intersite-proj-th (MIPT). Artificial neural networks (ANN), also widely accepted in building energy community, are compared to gradient-boosted tree ensembles (XGBoost), model of choice in many machine learning competitions. In order to get a better understanding of the behaviour of these two sampling methods and two machine learning models, we compare their predictions against a large set of generated synthetic data. For this purpose, a simple case study of an office cell model with a single window and a fixed overhang, whose main input parameters are overhang depth and height, while climate type, presence of obstacles, orientation and heating and cooling set points are additional input parameters, was extensively simulated with EnergyPlus, to form a large underlying dataset of 729,000 simulation results. Expendable local surrogate models for predicting simulated heating, cooling and lighting loads and equivalent primary energy needs of the office cell were trained using both LHS and MIPT and both ANN and XGBoost for several main hyperparameter choices. Results show that XGBoost models are more precise than ANN models, and that for both machine learning models, the use of MIPT sampling leads to more precise surrogates than LHS.

Optimizing building energy efficiency with a combined cooling, heating, and power (CCHP) system driven by boiler waste heat recovery

In light of increasing challenges related to fossil fuel consumption, global warming, and rising energy costs, optimizing energy systems has become essential. This study introduces a multi-purpose system designed to capture and utilize waste heat from boiler flue gases at the Tous power plant for simultaneous power generation, cooling, and heating. The system integrates an Organic Rankine Cycle (ORC) for power generation and an Absorption Refrigeration Cycle (ARC) for cooling, with the added function of recovering waste heat to preheat natural gas for the boiler and power plant. The system's performance was rigorously analyzed through energy, exergy, and exergoeconomic assessments. To determine the optimal operating conditions, a multi-objective optimization was conducted using the TOPSIS technique, focusing on critical parameters such as flue gas exit temperature, natural gas inlet temperature, and turbine inlet pressures. The results identified optimal conditions with a flue gas exit temperature of 532.5 K, a natural gas inlet temperature of 285 K, and turbine inlet pressures of 2.54 MPa and 14.62 MPa for the first and second turbines, respectively. Under these conditions, the system achieved an exergy efficiency of 41.1 %, cooling capacity of 133.1 kW, power generation of 210.4 kW, heat transfer to natural gas of 979.6 kW, and operational costs of 8.53 $/h. This study highlights the potential of the proposed system to significantly enhance energy efficiency and reduce operational costs in practical applications, offering a sustainable solution for energy management.

A sustainable design approach of a mega hostel: a study based at NIT, Srinagar, Kashmir, India

ABSTRACT In a sustainable building design, energy, water and resource optimization are key considerations, along with minimizing impacts on the ecology and environment. In this case study, water-related issues are considered from a sustainability perspective. The main objective of this study is to provide a sustainable design for a mega-hostel at the National Institute of Technology (NIT) Srinagar, with the aim of achieving an eco-friendly design which minimizes its dependence on external sources of energy. Rainwater harvesting, greywater reuse and the installation of a biogas plant are the three main techniques considered in this project. Rainfall data was collected and water demands for the mega-hostel and greywater produced by the mega-hostel were estimated. Potential rainwater harvesting, dimensions of a storage tank, first flush volume, discharges and dimensions of gutters have been calculated in this study. Greywater quantity has been calculated and a membrane bioreactor (MBR) to treat greywater is suggested, because it is space-optimized. Dimensions of a biogas plant and the quantity of gas released have also been estimated based on the amount of food waste generated by the mega-hostel.

Advancing building energy modeling with large language models: Exploration and case studies

The rapid progression in artificial intelligence has facilitated the emergence of large language models like ChatGPT, offering potential applications extending into specialized engineering modeling, especially physics-based building energy modeling. This paper investigates the innovative integration of large language models with building energy modeling software, focusing specifically on the fusion of ChatGPT with EnergyPlus. A literature review is first conducted to reveal a growing trend of incorporating large language models in engineering modeling, albeit limited research on their application in building energy modeling. We underscore the potential of large language models in addressing building energy modeling challenges and outline potential applications including simulation input generation, simulation output analysis and visualization, conducting error analysis, co-simulation, simulation knowledge extraction and training, and simulation optimization. Three case studies reveal the transformative potential of large language models in automating and optimizing building energy modeling tasks, underscoring the pivotal role of artificial intelligence in advancing sustainable building practices and energy efficiency. The case studies demonstrate that selecting the right large language model techniques is essential to enhance performance and reduce engineering efforts. The findings advocate a multidisciplinary approach in future artificial intelligence research, with implications extending beyond building energy modeling to other specialized engineering modeling.

Exploring automated energy optimization with unstructured building data: A multi-agent based framework leveraging large language models

The building sector is a significant energy consumer, making building energy optimization crucial for reducing energy demand. Automating energy optimization tasks eases the workload on engineers and hastens energy savings. More than 85% of building data is unstructured and diverse, concealing energy insights that demand laborious extraction. We propose an LLM-based multi-agent framework to explore automated tasks using these data. The framework includes three stages: building information processing, performance diagnosis, and retrofit recommendation, where LLMs injected with domain expertise act as agents for the roles of planner, researcher and advisor. We develop knowledge databases with retriever tools to inject knowledge and validate through experiments. In case studies, our framework delivered reliable results with only $5.15, effectively handling diverse inputs and tasks across cases. This demonstrates its potential to significantly reduce repetitive human labor and costs. We also discuss the potential of LLM-based multi-agent systems as trustworthy, generalized automated task solvers.

Multi-objective optimization for energy-efficient building design considering urban heat island effects

Building energy performance (BEP) associated with climate change and urban heat island effects (UHI) play an important role in urban sustainable development. To predict and optimize BEP under various socioeconomic scenarios, a new framework combining the physical simulation modeling integrated explainable machine learning and multi-objective optimization is proposed in this study. A Grasshopper-based simulation model incorporates BO-LGBM (Bayesian optimization-LightGBM) is developed to construct a solid prediction system, which tends to tune the hyperparameters accurately and explain more details with the aid of SHapley Additive explanation (SHAP). Two major aspects, including the building energy use intensity and indoor thermal comfort, are modeled by considering the different Shared Socioeconomic Pathways (SSPs) climate change scenarios in the near and far future. A multi-objective optimization method is employed to find an optimal solution for energy-efficient building design under constraints or uncertainties. Key findings include a 54% improvement in the Pareto front for building energy optimization and a significant impact of SSP585 scenarios on future energy consumption. The main novelty lies in the incorporation of machine learning into a physical model to achieve energy-efficient building design in urban contexts by considering UHI effects and climate change, offering actionable strategies for BEP assessment and promoting sustainable city planning.

DESIGN AND OPTIMIZATION OF A COST-EFFECTIVE NET-ZERO ENERGY MOSQUE LOCATED IN A HOT AND DRY CLIMATE

ABSTRACT This research paper presents a comprehensive analysis to develop an energy-efficient and net-zero-energy (NZE) model for a proposed hypothetical mosque model located in Riyadh, Saudi Arabia. The study employs a sequential optimization analysis using BEopt software (Building Energy Optimization Tool) to explore various energy conservation measures (ECMs) from three main categories: HVAC, lighting, and building envelope. The ECMs are evaluated based on energy savings and life-cycle cost, with the most cost-effective options selected for the proposed energy-efficient design package. Furthermore, a passive downdraught evaporative cooling (PDEC) system is introduced as a passive cooling strategy to further reduce the cooling energy consumption. Computational fluid dynamics (CFD) analysis is utilized to optimize the airflow around the building and PDEC tower, determining the optimal PDEC dimensions for enhanced ventilation performance. The PDEC system is found to contribute 12% to cooling energy savings, which further enhances the energy efficiency of the proposed mosque. A rooftop solar PV system is incorporated into the optimal energy-efficient design to achieve a NZE mosque. The results demonstrate that the NZE model achieves 80% energy savings, with the remaining 20% offset by the solar PV system. Moreover, life-cycle cost analysis reveals that the NZE model offers the lowest cost, making it the most cost-effective design option. This research provides valuable insights into designing sustainable mosques in hot and dry climates, such as Riyadh, offering a comprehensive solution to significantly reduce energy end-use and promote sustainable building practices.

Investigation of double-PCM based PV composite wall for power-generation and building insulation: Thermal characteristics and energy consumption prediction

The integration of phase change material (PCM) with building-integrated photovoltaic (BIPV) presents a compelling approach to enhance solar energy utilization and mitigate indoor thermal loads, contributing to energy-efficient and low-carbon building development. Traditional BIPV-PCM structures, however, struggle to balance PV efficiency and thermal insulation, particularly with varying PCM wall positions. To address this situation, this study introduces a novel double-PCM BIPV composite envelope (BIPV-dPCM). An experimentally validated dynamic heat transfer model was developed and used to perform a comparative simulation analysis with three reference systems to quantify the energy-saving potential of the BIPV-dPCM, focusing on PV output and wall insulation effectiveness metrics. Further dimensionless parametric analysis were carried out to investigate the systematic performance of the two PCMs at different relativities. In addition, the coupled working mechanism of the BIPV-dPCM system concerning the power generation performance and thermal insulation performance under transient variations is explored. It was found that the BIPV-dPCM showcases superior thermoelectric coupling performance compared to three alternative enclosures. Incorporating two PCMs significantly enhances electrical exergy efficiency by 11.66 % and thermal exergy efficiency by 1.54 %, surpassing other reference systems. The increase in PCM latent heat ratio has a limited effect on performance gain. Notably, as the PCM thickness ratio exceeds 1, the decline in P value decelerates, for every 0.5 increment in the g, the P value diminishes by merely 0.2 %. The ideal h is identified between 1 and 1.5, with 1.5 being optimal for energy conservation objectives. Additionally, the self-sufficiency coefficient (SSC) of the BIPV-dPCM remains robust, sustaining a range of 55 % to 65 % over prolonged periods. This study offers novel perspectives and serves as a design reference for optimizing building energy systems and enhancing cooling efficiencies in subtropical climates.

An improved transient search optimization algorithm for building energy optimization and hybrid energy sizing applications

In this paper, a new algorithm named the improved transient search optimization algorithm (ITSOA) is utilized to solve classical test functions, optimize the consumption of building energy, and optimize hybrid energy system production. The conventional TSOA draws inspiration from the fleeting behavior of electrical circuits with energy storage components. Rosenbrock’s direct rotation technique is used to improve the traditional TSOA performance against exploration and exploitation unbalance. First, the ITSOA performance is investigated in solving 23 classical benchmark functions, and the outcomes have shown the superior capability of the recommended algorithm in comparison with the conventional TSOA, DMO, SHO, GA, MRFO, and PSO methods. Also, the ITSOA proficiency is verified in solving the building energy optimization (BEO) problem for minimizing the energy usage of two simple and detailed buildings. The optimization results showed that the optimized solutions of ITSOA in single and multi-objective optimizations compared to conventional TSOA, DMO, SHO, GA, MRFO, and PSO obtained a lower value of the cost function. Also, the superiority of ITSOA has been confirmed to solve the BEO compared to previous methods. Moreover, the multi-objective optimization results have shown that ITSOA is able to determine the ultimate solution among the Pareto front set based on the fuzzy decision-making approach and building energy utilization decisions.

Green Strategies During Lower of Occupancy: The Best Practices of Facilities Management for Energy Optimization in Commercial Office Buildings

Green Strategies During Lower of Occupancy: The Best Practices of Facilities Management for Energy Optimization in Commercial Office Buildings

Achieving Net-Zero Energy Buildings: Analyzing and Optimizing Strategies Using Sensitivity Analysis

Enhancing building energy efficiency and incorporating renewable energy technologies are crucial for combating climate change and promoting sustainability. This study employed a sensitivity analysis to evaluate the influence of various parameters on building costs and energy consumption. Standardized Regression Coefficients (SRC) were used to measure the sensitivity of each variable. The type of glass, wall construction, window-to-wall ratios, and shading elements were identified as having the most significant impact on building costs and electricity usage. SRC values provided a numerical representation of the strength and direction of the relationship between these factors and the output variables. Furthermore, the study quantified the energy savings achieved through optimization methods. The data indicated an average reduction in consumption of 22%, with variations between 21% and 23% across different floors. These results highlighted the effectiveness of optimizing variables and applying energy-efficient design principles. The findings of this investigation enhance our understanding of the role of sensitivity analysis in optimizing building energy efficiency. They can serve as a reference for making decisions on integrating renewable energy technologies into buildings and designing them. These strategies can help reduce environmental impact, promote sustainable construction practices, and achieve significant energy savings.

Exploring the effective distance of 3D urban compact form correlated with building energy consumption and optimization strategies

ABSTRACT With the acceleration of urbanization, building energy consumption carbon emissions (BECCE) were increasing, weightily influencing global warming and the social-economic developments in cities. The detailed exploration of the most effective distance of urban three-dimensional (3D) compact form on BECCE guides urban space optimization. In this study, 135 People’s Banks of China (PBC) were taken as samples. Based on the banks building features, socioeconomic conditions, macroclimate, and urban 3D compact form at different buffers, the most effective distance on the BECCE was determined. According to Partial least-squares regression (PLSR), the results show that the most effective distance of the urban compact form on the BECCE was 150 m. The study divided the normalized 3D compactness index (NVCI) values from 135 buildings for the distance of 150 m into five categories and used the geographic detector to recognize statistically obvious differences between the subregions. The natural breakpoint method was used to categorize the study area into five classes, specifically: low, medium-low, medium, medium-high, and high compact form. Based on the geographic detector method result, the compact form of medium, medium high, and high compact form was optimized to low, medium low. The optimization can effectively reduce the BECCE due to changing the compact form of the building by 65.2% or 65.7%. The study combined mathematical statistics with spatial analysis approaches to identify the most effective distance for reducing energy consumption. Our results will contribute toward considerable reductions in the BECCE.