Energy Performance Optimization Research Articles

Building Information Modeling and AI Algorithms for Optimizing Energy Performance in Hot Climates: A Comparative Study of Riyadh and Dubai

In response to increasing global temperatures and energy demands, optimizing buildings’ energy efficiency, particularly in hot climates, is an urgent challenge. While current research often relies on conventional energy estimation methods, there has been a decrease in the efforts dedicated to leveraging AI-based methodologies as technology advances. This implies a dearth of multiparameter examinations in AI-driven extreme case studies. For this reason, this study aimed to enhance the energy performance of residential buildings in the hot climates of Dubai and Riyadh by integrating Building Information Modeling (BIM) and Machine Learning (ML). Detailed BIM models of a typical residential villa in these regions were created using Revit, incorporating conventional, modern, and green building envelopes (BEs). These models served as the basis for energy simulations conducted with Green Building Studio (GBS) and Insight, focusing on crucial building features such as floor area, external and internal walls, windows, flooring, roofing, building orientation, infiltration, daylighting, and more. To predict Energy Use Intensity (EUI), four ML algorithms, namely, Gradient Boosting Machine (GBM), Random Forest (RF), Support Vector Machine (SVM), and Lasso Regression (LR), were employed. GBM consistently outperformed the others, demonstrating superior prediction accuracy with an R2 of 0.989. This indicates that the model explains 99% of the variance in EUI, highlighting its effectiveness in capturing the relationships between building features and energy consumption. Feature importance analysis (FIA) revealed that roofs (29% in Dubai scenarios (DS) and 40% in Riyadh scenarios (RS)), external walls (19% in DS and 29% in RS), and windows (15% in DS and 9% in RS) have the most impact on energy consumption. Additionally, the study explored the potential for energy optimization, such as cavity green walls and green roofs in RS and double brick walls with VIP insulation and green roofs in DS. The findings of the paper should be interpreted in light of certain limitations but they underscore the effectiveness of combining BIM and ML for sustainable building design, offering actionable insights for enhancing energy efficiency in hot climates.

Performance assessments of ground source heat pump assisted by various solar panels to achieve zero energy buildings in cold climate conditions

This paper aims to perform a comprehensive performance analysis of a solar-assisted ground source heat pump (SAGSHP) system connected to various types of solar panels to enhance overall energy efficiency. While previous studies have examined integrating solar panels with heat pumps, comprehensive solutions like those presented here and performance evaluations in the zero energy building context have been lacking. This study developed various scenarios using three types of solar panels, photovoltaic (PV), photovoltaic-thermal (PVT), and solar thermal collectors (ST), to explore their simultaneous use. TRNSYS software was employed to analyze the complex system, comparing it to a conventional natural gas boiler system as a baseline. The findings revealed that the on-site energy use intensity (EUI) decreased from 78.6 kWh/m2 to 53.3 kWh/m2, a reduction of approximately 33 %, due to the SAGSHP's implementation. The combination of PVT and PV panels emerged as the most suitable option for the SAGSHP, aligning with the net zero energy concept. The SAGSHP system achieved a low EUI of 10.4 kWh/m2 in terms of primary energy. Additionally, the solar fraction, indicating the proportion of solar energy used, reached approximately 90 %. Thus, this study underscores the significance of evaluating various solar panel configurations during the preliminary design phase of SAGSHP systems to optimize energy performance and progress toward zero energy buildings.

Trends and challenges in LEED v4.1 healthcare certification: A comprehensive analysis of U.S. hospital scores in 2024

Introduction: This study evaluates the strengths and weaknesses in LEED-HC (Healthcare) version 2024 by analyzing the scorecards of 120 healthcare projects completed in the United States between 2020 and 2024. The research focuses on the relationship between achieved scores, LEED categories, and certification levels, providing insights for improving sustainable healthcare construction. Key findings include: Energy Efficiency Challenges: LEED-HC v.2024 places increased emphasis on energy efficiency, yet scores in the Energy and Atmosphere category remain low, indicating persistent challenges in optimizing energy performance. Strong Performance in Site and Water Efficiency: Healthcare projects consistently scored high in the Sustainable Sites and Water Efficiency categories, demonstrating effective integration of sustainability measures. Balancing Scores Across Categories: Projects compensated for lower energy scores by achieving higher marks in Indoor Environmental Quality and Innovation, helping maintain certification levels. Innovation and Regional Priority Trends: There is a growing trend of utilizing points from Innovation and Regional Priority categories to enhance overall scores. The study offers a benchmark for LEED-HC performance, guiding healthcare facility designers and policymakers in advancing energy-efficient and sustainable practices tailored to healthcare environments. The findings aim to support the development of greener healthcare facilities and inform future refinements of green building certification standards.

Daylighting and energy performance optimization of anidolic ceiling systems for tropical office buildings

A simple window is often unable to ensure standard illumination to the deep parts of large office spaces, and advanced daylighting systems are necessary to ensure the level of interior illuminance. Anidolic ceiling systems are one of the less popular daylighting strategies designed to redirect and redistribute daylight deeper, which can reduce the demand for artificial lighting and energy use for lighting purposes. This study shows how a parametric approach can be applied to optimize the daylighting and energy performance of an anidolic ceiling system in a tropical climate. Multi-objective optimization (MOO) techniques were applied to combine different design variables to establish multiple design alternatives and find the optimal design by comparing their performances. A simulation study of an anidolic ceiling system was conducted with test models primarily developed by Rhinoceros and ClimateStudio, and optimization of performance metrics was conducted using Grasshopper and Octopus. Data exportation was done using the TT toolbox, while data analysis was done through mathematical and statistical analyses and cross-checked by Design Explorer. By incorporating an optimum anidolic ceiling system into the case office space, spatial daylight autonomy (sDA) increased 20.51 %∼68.18 %, annual sunlight exposure (ASE) decreased 15.83 %∼8.52 %, mean illuminance increased 221 lux–3027 lux and energy use intensity (EUI) reduced 189.3 kWh/(m2y)∼174 kWh/(m2y) for different configurations of test models. The results demonstrated a MOO methodology that successfully finds the optimum solutions for the anidolic ceiling systems in a deep plan office space to enhance daylighting and energy performance.

Data-driven performance analysis of an active chilled beam air conditioning system: A machine learning approach for energy efficiency and predictive maintenance

This study aims to enhance energy performance estimation and maintenance planning for office buildings in Tehran. Machine learning and data mining techniques were employed, utilizing Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), Narrow Neural Networks, and optimizable SVMs. A comprehensive dataset of 2500 experimental samples was utilized with five input parameters (building size, occupancy patterns, environmental conditions, and ACB system parameters) and one output parameter (electricity consumption). GPR models performed better in estimating electricity consumption, achieving a Root Mean Square Error (RMSE) of 297.01 and an impressive R-squared value of 0.99. To optimize energy performance and design more energy-efficient buildings, this model accurately predicts energy consumption. Additionally, this study addressed maintenance planning for ACB systems, simulating maintenance issues with 2500 synthetic samples. The dataset included building age, size, number of floors, usage intensity, previous maintenance history, location, and environmental factors. Maintenance time was estimated using these features, and XGBoost was employed to predict ACB system maintenance time. Low MSE, RMSE, and R-squared values in the XGBoost model allow for accurate predictions. Using this model, facility managers can proactively plan maintenance activities, allocate resources, and minimize downtime. Through proactive measures, ACB performance and maintenance costs are optimized. The study accurately predicts ACB system energy consumption and maintenance time using the rational quadratic GPR model, applicable in Tehran and beyond. Additionally, MATLAB version R2022b was used for prediction, while DesignBuilder and EnergyPlus were utilized for technical analysis.

The impact of varying cold end diameter on the energy separation in Ranque-Hilsch vortex tube for different working gases: A CFD simulation and thermodynamic analysis

The vortex tube is a potential candidate for sustainable cooling due to its unique feature of energy separation without using any synthetic refrigerant. However, the investigation of energy separation and performance optimization always has been challenging due to its complex physics. In previous literature, the performance of vortex tube has been found affected by several parameters. In order to find the impact of varying cold end diameters with different gases, this paper investigates temperature separation in seven vortex tubes of different cold end diameters operating with three different working gases: He, N2, and CO2. A detailed thermodynamic analysis of their thermal separation performances at different cold mass fractions is presented for each working gas. Exergy analysis has also been conducted and discussed separately. As a result of this extensive investigation, it is found that the larger cold end diameter improves the performance of the vortex tube. The cold and hot temperature separation, cooling, and heating power at higher cold mass fractions are found to improve when operated with helium compared to other gas. However, the COP of cooling and COP of heating at higher cold mass fractions is found to be lower with helium. Improvements in the physical and kinetic exergies at the inlet and both outlets are observed with increasing cold end diameter, except for the kinetic exergy at the hot outlet. Helium gas is found to show more exergy at the inlet and both outlets among all three gases. The performance of vortex tube operating at higher cold mass fraction shows an improvement in exergies at outlets, except kinetic exergies at hot outlet. The total exergy efficiency decreases at cold outlet on increasing cold end diameter. However, the results of actual (physical) exergy efficiency shows an improvement at both outlet for larger cold end diameter. The actual exergy efficiency is calculated by considering the physical exergy alone because the kinetic exergy completely lost into the open atmosphere. Physical exergy efficiency at hot outlet is also found to increase on increasing cold mass fraction, while it decreases at cold outlet. Physical exergy efficiency of cooling and heating are found to more when operated with helium compared to nitrogen and carbon dioxide.

Recent Developments in AI and ML for IoT: A Systematic Literature Review on LoRaWAN Energy Efficiency and Performance Optimization.

The field of the Internet of Things (IoT) is dominating various areas of technology. As the number of devices has increased, there is a need for efficient communication with low resource consumption and energy efficiency. Low Power Wide Area Networks (LPWANs) have emerged as a transformative technology for the IoT as they provide long-range communication capabilities with low power consumption. Among the various LPWAN technologies, Long Range Wide Area Networks (LoRaWAN) are widely adopted due to their open standard architecture, which supports secure, bi-directional communication and is particularly effective in outdoor and complex urban environments. This technology is helpful in enabling a variety of IoT applications that require wide coverage and long battery life, such as smart cities, industrial IoT, and environmental monitoring. The integration of Machine Leaning (ML) and Artificial Intelligence (AI) into LoRaWAN operations has further enhanced its capability and particularly optimized resource allocation and energy efficiency. This systematic literature review provides a comprehensive examination of the integration of ML and AI technologies in the optimization of LPWANs, with a specific focus on LoRaWAN. This review follows the PRISMA model and systematically synthesizes current research to highlight how ML and AI enhance operational efficiency, particularly in terms of energy consumption, resource management, and network stability. The SLR aims to review the key methods and techniques that are used in state-of-the-art LoRaWAN to enhance the overall network performance. We identified 25 relevant primary studies. The study provides an analysis of key findings based on research questions on how various LoRaWAN parameters are optimized through advanced ML, DL, and RL techniques to achieve optimized performance.

Ontology-Based Data Representation Prototype for Indoor Air Quality, Building Energy Performance, and Health Data Computation

This study investigates the data integration of IoT-enabled sensor networks, emphasizing energy performance and personalized indoor air quality (IAQ) solutions to improve indoor environments, energy efficiency, and sustainability. Ontologies—structured frameworks that standardize data representation and enable interoperability—are the tools for interpreting complex IAQ data for optimal energy rate plans and health situations. Our methodology follows the well-established three-phase engineering approach. We present the design of a prototype with essential classes, which is proposed to integrate IAQ data with health conditions, enhancing real-time monitoring and automated decision making for optimal energy performance for smart buildings. Our research goal is to define the most essential classes, arranging them hierarchically to create a prototype for data computing covering IAQ, energy performance, and health aspects. This ontological framework, covering all three aspects, addresses a current research gap. Results demonstrate the minimum viable product with 78 classes for a smart home IoT system, providing tailored indoor climate control based on user health profiles and energy performance. This prototype represents a significant advancement in sustainable building and IAQ management, promising improved building energy performance, occupant health, and comfort. Future research will validate this framework through extensive testing in real-world environments.

Optimal Semi-Transparent Photovoltaic (STPV) window based on energy performance, daylighting quality, and occupant satisfaction – A case study of private office in Chengdu China

With the global warming, energy crisis, and the increasing application of glazing curtain walls, semi-transparent photovoltaic (STPV) systems have garnered significant attention for their excellent energy performance and daylight quality. The cell coverage ratio (CCR) of an STPV window impacts energy performance, daylight quality, and occupants’ satisfaction simultaneously, though these impacts can be conflicting. Therefore, this study investigates the optimal CCR of STPV for optimal daylight, energy performance, and occupants' satisfaction in Chengdu City, China through a case study. A private office room was selected as the base model. Energy performance, and daylighting quality according to the different CCR (10% to 50%) of STPV were analyzed using Honeybee in Grasshopper (GH), which is a Plugin for Rhinoceros. Occupants’ satisfaction was assessed through a survey conducted with ClimateStudio (CS) and Virtual reality (VR). The results indicated that the optimal CCR of STPV for this room is as follows: East (30%), West (10%), North (10%), and South (40%), based on energy performance, daylight quality, and occupants’ satisfaction.

Regulating cutting fluid parameters for optimal energy and economic performance: Methods for efficient and Low-Energy electrical machining

Scholars today have been dedicated to researching economically viable and efficient methods to enhance the energy efficiency of manufacturing, aiming for sustainable production. Wire Electrical Discharge Machining (WEDM) holds a significant position in current mechanical processing due to its unique operational principle, which results in notably higher energy consumption compared to conventional processing methods. Various factors influencing machining performance are complex, among which the condition of the cutting fluid plays a crucial role. Effectively controlling the parameters of the cutting fluid to improve its discharge state during machining is vital for enhancing energy and economic efficiency. Specifically, this study analyzes the characteristics of the WEDM process, models its resource and energy consumption, and predicts its economy. A method for optimizing control of cutting fluid parameters is proposed. By correlating different cutting fluid parameters with machining performance using the selected machine tool, and recommending combinations of cutting fluid parameters that ensure quality while improving the material removal rate, the study aims to achieve optimal energy efficiency and minimal cost. The results show that the proposed models for energy efficiency and economic prediction have an average accuracy of 96.64 %. An optimized machining scheme selected reduces machining time and energy consumption by approximately 1255.6 s (about 19.14 %) and 2240112.92 J (about 17.48 %), respectively, and saves about ¥ 6.084 (approximately 14.90 %) in machining costs. Additionally, the energy consumption of the pulse system’s proportion of total energy use increased by 1.63 %. The research demonstrates a general method for specifying combinations of cutting fluid parameters aimed at optimal energy efficiency and economy during the process optimization design stage of machining, providing a reference for improving the energy efficiency of wire cutting operations.

From BIM to digital twin in BIPV: A review of current knowledge

Digital solutions, such as digital-twin (DT) technologies, can significantly improve the construction industry by addressing its inherent challenges, such as complex project management, delays, quality control, safety issues, and environmental impact. Building-integrated photovoltaics (BIPVs) is a promising system for building envelopes to harvest renewable solar energy onsite. Interest in the application of DT technologies in BIPV systems has grown, including the prediction and optimization of energy performance and accurate diagnosis of faults. Despite increased research on the integration of BIPV systems with DT technologies, a systematic, holistic analysis that encompasses all project phases for effective lifecycle management is still lacking. This study comprehensively analyzes the integration of BIPV systems and DT technology throughout the building lifecycle, including design, construction, operation, demolition, and recycling, and it underscores the potential of combining BIPV systems with DT technology as a strategic approach to enhance efficiency, safety, and reliability. This study investigates three primary objectives: 1) the current applications/attempts to apply DTs to BIPV systems from a lifecycle perspective; 2) the challenges of integrating DTs and BIPV; and 3) promising DT technologies/strategies for promoting BIPV development. This study offers significant insights by highlighting the use of DT technologies across the full lifecycle of BIPV systems. It details the application of these technologies across five crucial layers in BIPV systems, data technologies, modelling methods, and simulation techniques. Moreover, it elucidates current research gaps in this domain and proposes valuable recommendations for future research avenues.

Review of energy-efficient HVAC technologies for sustainable buildings

This review paper provides a comprehensive examination of energy-efficient Heating, Ventilation, and Air Conditioning (HVAC) technologies for sustainable buildings. The focus is on exploring the latest advancements, benefits, challenges, and future prospects in the realm of HVAC systems designed to enhance energy efficiency and promote sustainability in building operations. The review begins with an overview of the importance of energy-efficient HVAC technologies in the context of sustainable building design and operation. It underscores the critical role of HVAC systems in reducing energy consumption, minimizing environmental impact, and enhancing occupant comfort and well-being. The paper delves into various categories of energy-efficient HVAC technologies, including advanced heating and cooling systems, ventilation strategies, and smart controls. Each category is examined in detail, highlighting key features, performance metrics, and real-world applications. Furthermore, the review evaluates the benefits associated with energy-efficient HVAC technologies, such as significant energy savings, reduced operating costs, improved indoor air quality, and enhanced building resilience. It also discusses the potential challenges and barriers to adoption, such as high upfront costs, complexity of implementation, and retrofitting existing buildings. The paper explores emerging trends and innovations in the field of energy-efficient HVAC technologies, including the integration of renewable energy sources, smart sensors and actuators, and predictive maintenance algorithms. These advancements have the potential to further optimize energy performance, increase system reliability, and enable proactive management of HVAC systems. In conclusion, the review emphasizes the importance of energy-efficient HVAC technologies as a cornerstone of sustainable building practices. It underscores the need for continued research, innovation, and collaboration among stakeholders to overcome existing challenges and unlock the full potential of energy-efficient HVAC systems in contributing to a more sustainable built environment. Overall, this comprehensive review provides valuable insights into the current state of energy-efficient HVAC technologies, their applications, benefits, and challenges, while also highlighting opportunities for future advancements and their crucial role in achieving sustainability goals in the building sector.

Optimizing Energy Efficiency in Commercial Buildings through Advanced Building Strategies

The pressing issue of climate change and its correlation with greenhouse gas emissions has propelled a heightened emphasis on energy-efficient building practices, particularly within the commercial sector. This study delves into advanced design strategies aimed at maximizing energy efficiency while upholding quality and comfort standards. By scrutinizing a functioning commercial building in Gurugram, Haryana, data was gathered to construct an energy model using eQUEST. Analysis encompassed various building facets such as envelope components (e.g., walls, roofs, and windows), HVAC systems, lighting, and occupancy patterns. The outcomes furnish valuable insights into pivotal factors influencing energy efficiency and propose tailored strategies for integration into commercial building design. This methodical approach, informed by case evaluation, seeks to optimize energy performance and cost-effectiveness across diverse building types and climates.

Towards a Digital Representation of Building Systems Controls

Energy monitoring and performance optimization processes play a significant role in ensuring energy-efficient building operation. However, the current implementation of these processes requires substantial time and effort due to the fragmented and non-digital nature of technical building equipment information. To address these challenges, we demonstrate the application of the PLCont methodology within an ongoing monitoring process in a real building. PLCont utilizes Semantic Web Technologies and established RDF ontologies to describe the control topology. The digital representation of control functions aligns closely with the IEC standard 61131-3, and the PLCont ontology establishes a connection between the topology and the control functions. We compare the conventional approach and the PLCont methodol-ogy using the supply air temperature control of an Air Handling Unit (AHU) as a use case. A graphical user interface built upon the PLCont backend provides a comprehensive represen-tation of the system's functionality. It displays the system topology as a digital HVAC schema, the function code exported from the PLC, time-series data plots, and textual descriptions of implemented functions. We demonstrate how this approach enhances transparency and facil-itates the identification and elimination of faulty system behavior, ultimately contributing to the optimization and energy efficiency of building systems.

Energy and economic performance optimization of a window with variable transparency shape-stabilized PCM in hot summer and cold winter climate zone

Improving the thermal performance of windows is important for building energy efficiency. Filling windows with variable transparency shape-stabilized phase change materials (VTSS-PCM) improves the thermal inertia of windows while avoiding the leakage of PCM. In this paper, a new type of VTSS-PCM window was proposed, tested, simulated and optimized in hot summer and cold winter climate zone. A numerical model of the VTSS-PCM window was built, and the model was validated using experimental tests. On this basis, three key parameters of VTSS-PCM were investigated and optimized. Finally, the energy and economic performance of the optimized VTSS-PCM window were compared with a typical hollow glass window. The results showed that the total annual unfavourable heat transfer (TAHT) and the annual investment cost (AIC) of the VTSS-PCM window obtained from the optimization were 118.16 kWh/m2 and 8.53 CNY/m2, respectively. Compared with the hollow glass window, the VTSS-PCM window reduced the TAHT by 30.14% and the total annual cost by 28.39%. The VTSS-PCM window produced a better development potential in terms of energy performance and economic performance. This study provided a reference for the application of the VTSS-PCM window in hot summer and cold winter regions of China.

Advancing Sustainable Building Practices: Intelligent Methods for Enhancing Heating and Cooling Energy Efficiency

Our work is dedicated to enhancing sustainability through improved energy efficiency in buildings, with a specific focus on heating and cooling control and the optimization of thermal comfort of occupants. With an energy consumption of more than 60% in buildings, HVAC systems are the biggest energy users. By integrating advanced technology, data algorithms, and digital twins, our study aims to optimize energy performance effectively. We have developed a Neural Network-based Model Predictive Control (NNMPC) to achieve this goal. Leveraging technologies such as MQTT communication, Wi-Fi modules, and field-programmable gate arrays will enhance scalability and flexibility. Our findings demonstrate the efficacy of the NNMPC system deployed on the PYNQ board for reducing sensible thermal energy usage for both cooling and heating purposes. Compared to traditional On/Off control systems, the NNMPC achieved an impressive 40.8% reduction in heating energy consumption and a 37.8% decrease in cooling energy consumption in 2006. In comparison to the On/Off technique, the NNMPC demonstrated a 25.6% reduction in annual heating energy consumption and a 28.8% drop in annual cooling energy consumption in the simulated year of 2017. We observed that, across all strategies and platforms, there were no instances where the Predicted Mean Vote (PMV) fell below −0.5. However, a significant proportion of PMV values (ranging from 65% to 83%) were observed between −0.5 and 0.5, signifying a high level of occupant comfort. Additionally, for PMV values between 0.5 and 1.0, percentages ranged from 16% to 33% for both years. Importantly, the NNMPC exhibited notable efficiency in maintaining occupants’ comfort within this range, requiring less energy while ensuring highly satisfactory environments.

A neural network framework for optimizing parallel computing in cloud servers

Energy efficiency has become a major focus in optimizing hardware resource usage for Cloud servers. One approach widely employed to enhance the execution of parallel applications is thread-level parallelism (TLP) exploitation. This technique leverages multiple threads to improve computational efficiency and performance. However, the increasing heterogeneity of resources in cloud environments and the complexity of selecting the optimal configuration for each application pose a significant challenge to cloud users due to the massive number of possible configurations and the need to effectively harness TLP in diverse hardware setups to achieve optimal energy efficiency and performance. To address this challenge, we propose TLP-Allocator, an artificial neural network (ANN) optimization strategy that uses hardware and software metrics to build and train an ANN model. It predicts worker node and thread count combinations that provide optimal energy-delay product (EDP) results. In experiments using ten well-known applications on a private cloud with heterogeneous resources, we show that TLP-Allocator predicts combinations that yield EDP values close to the best achieved by an exhaustive search. It also improves the overall EDP by 38.2% compared to state-of-the-art workloads scheduling on cloud environments.

Multi-objective optimization of energy and daylight performance for school envelopes in desert, semi-arid, and mediterranean climates of Iran

Considering global warming as a critical challenge to human life, performance optimization of the building envelope can play a noticeable role to reduce a building's environmental footprint. Meanwhile, shading systems are considered as sustainable passive solutions to contribute to building energy efficiency, and daylight control in early-stage design. Nonetheless, a paucity of research has examined the various shading strategies in diverse climates, particularly arid desert and steppe regions, in conjunction with educational buildings. The present study aims to evaluate the role of fixed exterior shading systems (FESSs), and window-to-wall ratio (WWR) on building’ thermal and daylight performance in Iran with desert, semi-arid, and Mediterranean climates. Pareto Frontier and weighted-sum method for multi-objective optimization, and sensitivity analysis were used to study the optimum solutions, and the relationship between the design variables and the performance metrics. Three objective metrics including Spatial Daylight Autonomy (sDA), Annual Sunlight Exposure (ASE), and Energy Use Intensity (EUI), were defined as performance metrics. Shadings include vertical louver, horizontal louver, light shelf, overhang, and egg-crate. The results showed that EUI was reduced via FESS-integrated façade by 25.22%, 20.84%, 19.14%, 14.06%, and 13.47%, in Yazd, Bushehr, Kerman, Rasht, and Mashhad as selected studied cities, respectively. Based on ASE, the horizontal louver surpasses the other systems by 100% ASE value reduction in all climate zones except for Rasht. sDA level is reduced in all climate zones considering five studied FESSs excluding horizontal louver in Yazd and Rasht and overhang in Mashhad by 100% sDA level. Building energy performance simulation is validated by ASHRAE140-2020.

Occupancy modeling on non-intrusive indoor environmental data through machine learning

The primary drivers of energy consumption within buildings are the occupants. Non-intrusive Internet of Things (IoT) technology can be utilized to detect occupancy and optimize energy performance while preserving the privacy of building occupants. This study explores the suitability of various indoor environmental data for occupancy detection in office rooms. Data was collected utilizing an IoT sensory device, recording CO2 concentration, air temperature, relative humidity, indoor air quality, sound pressure level, and illuminance in two double-occupied office rooms at a campus building in Munich, Germany. Machine learning models, namely Random Forest, XGBoost, and dense neural networks were trained on the dataset, and the models’ feature importance was analyzed through post-hoc explainability testing. The best features were then incorporated in a sliding window of 15 min to consider temporal dependencies in the models. The study also compared two hyperparameter optimization techniques: Random and Bayesian optimization.The results of this study indicate that with six days of ground truth data of sound pressure level, CO2, and illuminance, accuracy and F1-score for occupancy detection of above 0.95 and 0.93 can be respectively achieved.

Thermal performance analysis of hollow bricks integrated phase change materials for various climate zones

AbstractThis comprehensive research addresses the significant challenge of building‐related energy consumption in Morocco. Our innovative approach involves integrating phase change materials (PCMs) into hollow bricks, strategically addressing the diverse climate zones prevalent in the country. The primary focus is on enhancing energy efficiency within structures. Leveraging detailed simulations and employing the enthalpy‐porosity approach, our study models the impact of PCMs on internal temperatures. Optimal outcomes are achieved by partially filling brick holes with PCMs in specific configurations, demonstrating the materials' ability to adapt to varying conditions. A noteworthy finding is the 2–3‐h phase shift observed in cold zones, indicating the potential for PCMs to effectively regulate temperatures. Equally significant is their capability to maintain a constant internal temperature of 26°C in hot zones, even amidst extreme external conditions reaching up to 47°C. This resilience underscores the novel and tailored thermal regulation potential of PCMs. Beyond the technical insights, our research highlights the paramount importance of considering regional climates in PCM applications' implementation. This awareness is crucial for optimizing energy performance in buildings and ensuring sustainability. In essence, this study contributes valuable knowledge and practical implications for the strategic deployment of PCMs to enhance building energy efficiency, emphasizing the need for context‐specific solutions in diverse environmental conditions.