Energy-efficient Strategy Research Articles

Optimal design guidelines for net zero energy residential buildings in cooling-dominated climates: Case study of Ghana

Net zero energy buildings (NZEBs) are regarded as an achievable solution for reducing energy use and carbon emissions in the built environment and addressing climate change. However, the ultimate objective of NZEBs is to enhance the energy performance of buildings by changing how they are designed and constructed. Consequently, identifying cost-effective energy efficiency strategies at the early design stages of buildings has become imperative and often required in several countries. This study describes a systematic analysis approach to optimize the design of residential facilities considering both energy efficiency and renewable energy systems. The design optimization is based on life cycle cost (LCC) analysis, which considers capital and energy costs over the lifecycle of the building. The analysis consistently shows that LCC-based optimal designs significantly reduced the building's annual energy consumption, achieving 34–35% energy-saving across the various climates compared to the baseline case. The most cost-effective energy efficiency measures that proved effective across all the climate zones include north orientation of the building at 180 azimuth degrees, natural ventilation, adequate window-to-wall ratio, minimal façade air leakage (i.e., about 5-7 ACH @50 Pa), and high efficient fans, air conditioning, and dehumidification. The integration of the PV system indicates that the net zero energy (NZE) design for the building is technically feasible and cost-effective across the climate zones considered. The sensitivity analysis further shows that the cost of integrating PV systems is even more cost-effective over a more extended period of the building's lifecycle. The study provides an optimal path for designing NZEBs and offers guidelines to achieve NZE across the building's lifecycle.

Utilisation of Machine Learning in Control Systems Based on the Preference of Office Users

Reducing energy consumption is vital to save natural resources and contribute to the sustainable development in any sector of society. In the building sector, there are many well-known energy efficiency strategies currently being applied. However, considering the advances in technology and in comfort studies, it is possible to see that the current building sector scenario demands new energy efficiency strategies. Such strategies need to be capable of identifying and assuring comfortable environments according to users’ perceptions. Machine learning techniques can be a useful alternative to identify users’ preferences and control lighting and heating, ventilation and air-conditioning systems in buildings. This paper shows a systematic literature review on the use of machine learning algorithms on preference identification and environmental adequacy according to users’ demands. Its contribution is to explore beyond the performance and configurations of the algorithms, addressing users’ preference aspects as well. The strategies found in the literature provided promising results. The most used approach was supervised learning because data can be treated as categories. In general, the control systems have shown good performance, and so have the algorithms. Users were mostly satisfied with environmental conditions. Situations of dissatisfaction were associated with the occupant’s willingness to use the system more than with the control system’s performance. Furthermore, it is also possible to ally user-centred control and energy savings but this relies on occupants’ characteristics and the control strategies used. We underline the importance of identifying whether the users are willing to deal with an automatic control system before making any decision, even if the operation of the system is based on their preferred environmental conditions.

Balancing preservation and utilization: Window retrofit strategy for energy efficiency in historic modern building

In modern historic buildings, the decline in energy efficiency due to aging necessitates both the preservation of historical value and the need for energy retrofits. However, the windows cannot be altered due to the historical significance of the building's windows in terms of design and materials. Retrofitting windows in modern historic buildings require a more multifaceted improvement than other envelopes. This research examines the energy efficiency of a retrofit strategy that adds double-glazed windows to existing exterior windows. Through the application of retrofit technology, the performance of the windows and their impact on indoor temperature and relative humidity were analyzed. The research conducted an energy performance analysis using building energy simulations to assess energy savings. To ensure accurate energy efficiency evaluation, on-site indoor temperature and relative humidity monitoring, as well as non-destructive thermal performance assessments, were performed. The results revealed a 35 % improvement in airtightness with window retrofit. Based on these results, simulation results showed a 17 % reduction in annual cooling energy consumption and a 24 % reduction in annual heating energy consumption following the implementation of retrofit strategies. Thermal performance improvements proved effective in reducing cooling energy consumption, while airtightness improvements were more effective for heating energy consumption reduction. Consequently, for buildings requiring both heating and cooling, the double-glazed windows retrofit strategy emerged as an energy effective retrofit approach capable of enhancing both airtightness and thermal performance. This retrofit will unlock new possibilities for the preservation and revitalization of modern historic buildings, allowing them to continue their legacy.

Maximizing UAV fog deployment efficiency for critical rescue operations: A multi-objective optimization approach

In disaster scenarios and high-stakes rescue operations, integrating Unmanned Aerial Vehicles (UAVs) as fog nodes has become crucial. This integration ensures a smooth connection between affected populations and essential health monitoring devices, supporting the Internet of Things (IoT). Integrating UAVs in such environments is inherently challenging, where the primary objectives involve maximizing network connectivity and coverage while extending the networks lifetime through energy-efficient strategies to serve the maximum number of affected individuals. In this paper, we decomposed our problem into two subproblems. Connectivity, coverage subproblem and network lifespan optimization subproblem. For the Connectivity and coverage subproblem, the optimal connectivity and coverage are optimized by strategically deploying UAV fog nodes in a manner that maximizes network coverage, thoughtfully considering the distinctive constraints that emerge in these high-pressure situations where lives are at stake. We shape our UAV fog deployment problem as a multi-objective optimization and introduce a specialized UAV fog deployment algorithm tailored specifically for UAV fog nodes deployed in rescue missions. For the network lifespan subproblem, after determining the optimal connectivity and coverage of UAV nodes and users within the entire network, the network lifespan optimization subproblem is greatly simplified and efficiently solved via a one-dimensional swapping method. After conducting thorough experiments using our proposed architecture across diverse scenarios, our method consistently surpasses existing approaches. It effectively tackles issues like restricted connectivity and potential node failures. This advancement in deployment efficiency notably enhances rescue operations, enabling us to aid the maximum number of affected individuals swiftly during critical and time-pressing situations.

Development and optimization of an energy saving strategy for social housing applications by water source-heat pump integrating photovoltaic-thermal panels

Residential buildings account for 84 % of Italy's built environment, playing a pivotal role in the EU's aim to cut GHG emissions by 55 % through enhanced energy efficiency and climate adaptation. This necessitates comprehensive energy retrofit initiatives, especially in sectors like social housing, which has been relatively overlooked in terms of energy efficiency strategies. This study focuses on a multi-story building from the 1980s in Rome, implementing an innovative energy system proposed by the RESHeat European project. This system, aimed at standardizing energy retrofits for late 20th-century social housing, leverages the underexplored potential of water-source heat pump (WSHP) systems. The novelty of this research extends to its examination of multi-family housing, a sector that has seen less attention compared to public spaces and smaller residential buildings. Through experimental validation and annual dynamic simulations using TRNSYS and Simulink, the research compares the existing heating system with a proposed upgrade that includes a WSHP and Photovoltaic-Thermal (PVT) panels. This upgrade demonstrated a significant efficiency improvement, achieving an annual COP of 6.1 for the WSHP and a 36 % Primary Energy Savings (PES) from the PVT panels, showcasing the effectiveness of these technologies in enhancing the energy profile of multi-family residential buildings.

Hybrid optimization approach for energy minimization in wireless sensor networks leveraging XGBoost and random forest

Wireless Sensor Networks (WSNs) have garnered significant attention from both the academic and industrial communities. However, the limited battery capacity of WSN nodes imposes a set of restrictions on energy dissipations, which has compelled researchers to seek ways to save and minimize energy consumption. This paper presents a hybrid optimization model to minimize energy dissipation in Wireless Sensor Networks (WSNs). Employing linear programming and a combination of XGBoost and Random Forest algorithms, it effectively predicts internode distances and network lifetime. The results demonstrate significant energy savings in WSN deployments, outperforming traditional methods. This approach contributes to the field by offering a practical, energy-efficient strategy for WSN configuration planning, highlighting the model’s applicability in real-world scenarios, where energy conservation is critical.

Analyzing energy consumption due to occupant interaction with manual and automatic window blinds in multiple climates

Proper daylight management through automated or manual blind systems can enhance user comfort and energy conservation. Furthermore, the influence of weather conditions, coupled with occupant behavior, significantly impacts energy consumption, rendering weather forecasts invaluable in conducting individual building energy simulations to identify energy-efficient strategies for the future. Also, I wonder if it is possible to move some items from room 308 until you can provide items from other resources. Future weather conditions and their impact on building performance can be evaluated using Representative Concentration Pathways (RCP) scenarios provided by the Intergovernmental Panel on Climate Change (IPCC). This research aimed to compare the energy usage of manual and automated window blinds with internal blinds in mid-size office buildings across four main climate zones (2B Hot-Dry; 2A Hot-Humid; 7 Very Cold; 4A Mixed-Humid) in the USA under current and future climate change scenarios (RCP 4.5, 8.5). Utilizing simulation as the methodology and comparing the energy use resulting from the RCP 4.5 and 8.5 scenarios revealed that the most significant decrease in heating energy usage was happening in the Hot-Humid zone with Automated blinds, which is a more efficient alternative than manual Blinds. In contrast, the Very Cold zone experienced almost no changes in either heating or cooling energy consumption. In Climate Zones 2B Hot-Dry and 7 Very Cold, rising temperatures lead to increased cooling energy requirements and reduced heating demands due to climate changes. Climate Zone 4A Mixed-Humid saw reduced energy consumption for cooling but increased usage for heating, particularly under RCP 8.5. In Zone 2A Hot-Humid, a high demand for heating energy was found.

Energy Efficient Transmission Strategy for Mobile Edge Computing Network in UAV-Based Patrol Inspection System

Energy Efficient Transmission Strategy for Mobile Edge Computing Network in UAV-Based Patrol Inspection System

Simulation-Based Exploration with Energyplus as an Energy Efficiency Strategy

In countries with tropical or subtropical climates, such as Indonesia, the use of air conditioning is becoming increasingly important for comfort and productivity. However, high energy use in air conditioning systems has significant environmental and economic impacts, including increased greenhouse gas emissions and high operational costs. This research aims to analyze the Energy Consumption Index (ECI) in the Joint Lecture Building, State University of Malang using EnergyPlus software simulation. Research data consists of building data, weather data, building construction data, operating schedules, equipment specifications, and energy usage data. Furthermore, the data analysis technique used ECI calculations based on EnergyPlus software simulation. The research results show that based on the Energy Consumption Index value it is 201,943 kWh/m2/year, but with a savings scenario it becomes 161,964 kWh/m2/year (an increase of 19,797%) so it is classified as efficient. The energy-saving scenario implemented is to replace the Constant Air Volume (CAV) air conditioning system with Variable Air Volume (VAV) and the types of luminaires that were originally fluorescent will be replaced with Light Emitting Diodes (LEDs). The comparison results obtained an energy-saving value of 19,797%. The research results show that using VAV and LEDs technology can increase energy efficiency in the Joint Lecture Building, State University of Malang. The scenario attempts to reduce environmental impact through energy efficiency in commercial buildings and shows that the EnergyPlus simulation method is an effective approach for designing energy savings strategies.

Assessment and Forecasting of Energy Efficiency in Economic Sectors in Poland

The material developed focuses on the analysis of energy efficiency trends in Poland, utilising ODEX indicators for the sectors of industry, transport, households and in general, from 2011 to 2021. The objective of the study is to assess the progress made in energy efficiency and to forecast future trends in these sectors. The methods employed are based on statistical modelling of time series, taking into account sector-specific energy consumption dynamics. The following techniques were employed: linear regression, cluster analysis to identify patterns of change, statistical hypothesis testing for energy efficiency and simplified autoregressive models. The results demonstrated significant improvements in energy efficiency in the industrial sector, stability of the ODEX indicator in the transport sector and gradual improvements in households and overall. The prediction indicates an upward trend in the ODEX indicator in the short term, suggesting an increase in energy demand. However, it also predicts a decline in the long term, which may indicate the effectiveness of future energy efficiency strategies and investments. Consequently, the necessity for continued efforts to increase energy efficiency and further research into the factors influencing energy efficiency in different economic sectors is emphasised.

A general Energy-Efficient strategy for optimizing CO2 Capture: Designing and harnessing the rapid adsorption kinetics of Amine-Impregnated adsorbents

This study aims to optimize the performance of amine-impregnated CO2 adsorbents for both direct air capture (DAC) and point source CO2 capture (PSC) applications. The main objective of this study is to investigate the relationship between the weight fraction of amine loading and the efficiency of CO2 uptake on the designated support, aiming to find a straightforward method to design effective amine support materials. To this end, a composite material denoted as PEI/CNT@ZIF-8 has been developed. This composite is comprised of polyethylenimine (PEI), multi-walled carbon nanotubes (CNTs), and zeolitic imidazolate frameworks (ZIF-8). CNTs offer several advantages for enhancing CO2 uptake, including rearranging the ZIF-8 particles to facilitate PEI loading, creating strong electrostatic interaction for amine anchoring, and establishing CO2 diffusion channels to improve CO2 capture kinetics. In DAC scenarios, with a CO2 concentration of 400 ppm balanced with N2, the 25.0 wt% PEI/CNT(1)@ZIF-8(5) demonstrates superior CO2 uptake, achieving 4.08 mmol g−1 within a one-hour interval by harnessing the rapid kinetic region and exhibiting a low desorption energy of 55.8 kJ mol−1 for releasing CO2. In PSC scenarios, with a CO2 concentration of 15 vol% under both dry and wet conditions, the 37.5 wt% PEI/CNT(1)@ZIF-8(5) exhibits excellent CO2 uptake of 2.21 and 2.58 mmol g−1, respectively, at 50 ℃. The findings suggest that adjusting the balance between CO2 uptake and adsorption kinetics by considering the amine weight fraction can optimize the performance of solid amine CO2 adsorbents across diverse operational conditions. Furthermore, rather than relying on a single support material, this study reveals the potential of combining CNTs with materials such as SiO2 nanoparticles, Al2O3, or metal–organic frameworks (MOFs) to enhance CO2 capture performance. This approach holds promise as an effective and straightforward strategy.

Energy Efficiency Evaluation in Automotive Industry with AHP Method and Best&Worst Method

Energy is one of the most important elements for countries to exist in the competitive market in social, economic, technological and international areas. With the developing technology and increasing energy consumption, the importance of energy efficiency comes to the fore. In this context, the automotive industry, which is an important part of the industrial sector where energy is used intensively, was preferred in the study. Besides the automotive industry's need for energy, its interaction with other industries is the reason for preference. In the study, Energy Efficiency Strategy criterion weights in the Automotive Industry were calculated by using Multi-Criteria Decision Making methods. As a result of the literature research, nine main criteria and thirty-three sub-criteria were determined. The criteria weights of the Energy Efficiency Strategy in the automotive industry are analyzed separately with AHP, which is frequently used in the literature, and BEST & WORST, which is newly introduced to the literature. Comparing the solution results of the two methods is the aim of the study. In the light of the results obtained, it has been determined that the AHP and BEST & WORST methods give close results.

Cost Benefits of Net Zero Energy Homes in Australia

This paper presents a systematic analysis of energy savings and cost benefits associated with several options for integrating energy efficiency and renewable energy technologies. The primary goal of this study is to assess the cost-effectiveness of achieving optimal net-zero energy (NZE) designs for residential buildings in Australia. Specifically, the analysis combines a series of sensitivity analyses and multi-objective optimizations to account for a wide range of design strategies for detached homes in four cities representing different Australian climates. The results indicate that not only are NZE designs technically feasible for all the considered Australian cities, but they are also highly cost-effective. This cost-effectiveness is attributed to the lower installation costs of rooftop PV systems as well as the beneficial interactive effects of proven energy efficiency strategies. Indeed, it is found that the deployment costs of rooftop PV systems can be recovered in less than 4 years. Moreover, the addition of thermal insulation in walls and ceilings can reduce both HVAC capacities and annual energy end-use by up to 59%. Based on an optimization-based design, NZE homes in Australia can have lower construction costs and, ultimately, lower life cycle costs than dwellings built to meet current energy efficiency standards based primarily on stringent building envelope thermal performance.

Synergy in Action: Integrating Environmental Monitoring, Energy Efficiency, and IoT for Safer Shared Buildings

Shared public buildings have become centers of innovation, integrating advanced technologies to meet evolving societal needs. With a heightened emphasis on occupants’ health and well-being, these buildings serve as hubs for technological convergence, facilitating seamless connectivity and intelligent data analysis and management. Within this context, environmental monitoring emerges as a foundational element, pivotal to all aspects of building management. This article provides findings from the nationally funded RE-START project, which focuses on shared public buildings, with special regard to educational and medical facilities. The project explores enhanced indoor air quality monitoring, focusing on CO2 concentration that is directly correlated with occupancy, as a fundamental element for developing health and safety protocols, energy efficiency strategies, the integration of smart building technologies, and data-driven energy management. The intersection of environmental monitoring, energy efficiency, security, and IoT technologies in in indoor spaces is relevant. The outcomes of the study reveal the delicate nature of all the involved components, which need to be carefully developed in an integrated manner.

An Efficient Metaheuristic Technique for Wireless Sensor Network Integrated Micro Grid Environment Using Improved Whale Optimization Algorithm (Iwoa)

Wireless Sensor Network (WSN) nodes require careful power management to maintain long term operation without regular battery changes. Battery replacement can be challenging and expensive in a microgrid scenario where sensors may be installed in remote or i naccessible areas. To extend the life of WSN nodes, energy efficient design and power management strategies should be used. A potential strategy for effectively monitoring, controlling, and managing the distributed energy resources (DERs) inside a microgri d is theincorporation of WSNs with microgrids. This research work uses Improved Whale Optimization Algorithm (WOA) for optimizing the routing and performance. The humpback whale's hunting style served as the inspiration for the WOA, a metaheuristic optimization algorithm. WOA is an optimization technique that is used with MG and WSNs. During the optimization phase, WOA establishes a balance between exploitation and exploration. To increase the lifespan of a WSN in a microgrid, effective energy management i s frequently needed. When considering the dynamic energy availability from renewable sources and the energy requirements of the microgrid components, WOA is used to optimise energy allocation and routing in WSNs. This may lead to increased energy efficiency and longer network longevity. The IWOA attains 97.5% PDR that is higher than the existing state of art techniques.

Decoding Energy Usage Predictions: An Application of XAI Techniques for Enhanced Model Interpretability

The growing complexity of machine learning models has heightened the need for interpretability, particularly in applications impacting resource management and sustainability. This study addresses the challenge of interpreting predictions from sophisticated machine learning models used for building energy consumption predictions. By leveraging Explainable AI (XAI) techniques, including Permutation Importance, SHapley Additive exPlanations (SHAP), and Local Interpretable Model-Agnostic Explanations (LIME), we have dissected the predictive features influencing building energy usage. Our research delves into a dataset consisting of various building characteristics and weather conditions, applying an XGBoost model to predict Site Energy Usage Intensity (Site EUI). The Permutation Importance method elucidated the global significance of features across the dataset, while SHAP provided a dual perspective, revealing both the global importance and local impact of features on individual predictions. Complementing these, LIME offered rapid, locally focused interpretations, showcasing its utility for instances where immediate insights are essential. The findings indicate that 'Energy Star Rating', 'Facility Type', and 'Floor Area' are among the top predictors of energy consumption, with environmental factors also contributing to the models' decisions. The application of XAI techniques yielded a nuanced understanding of the model's behavior, enhancing transparency and fostering trust in the predictions. This study contributes to the field of sustainable energy management by demonstrating the application of XAI for insightful model interpretation, reinforcing the significance of interpretable AI in the development of energy policies and efficiency strategies. Our approach exemplifies the balance between predictive accuracy and the necessity for model transparency, advocating for the continued integration of XAI in AI-driven decision-making processes.

Digital workflow for nearly zero-energy high-rise office building design optimization at the district scale in Mediterranean context

While the implementation of sustainable urban planning has proven to be one of the primary goals to reduce the climate change impact, the rapid adoption of nearly Zero Energy Buildings (nZEB) concept in the building sector is inevitable to reach that objective. Following this trend, this article focuses on the implementation of a parametric digital workflow to evaluate the energy performance of a nearly zero energy high-rise 23-storey office building in the climatic and urban contexts of Casablanca at the early design stage.In the scope of this study, Grasshopper-based digital workflow permits to investigate the impact of 147 parametric building designs, which are generated by varying the building's shape factor and orientation on thermal cooling and heating demand and global solar energy production. The outcomes of this holistic methodology highlight the design trade-offs between energy efficiency strategies and energy performance of building-integrated photovoltaic (bipv) and photovoltaic (pv) systems, aiming to reach optimized nZEB. Moreover, the results of the study suggest that it is possible to reach an annual load match equivalent to 29.68 %. The findings also underscore the significant role of the BIPV systems in shifting towards the goal of net zero energy, accounting for up to 64.43 % of the total solar energy output and contributing in total up to 17.62 % to the yearly self-sufficiency. In addition, the energy balance evaluation, when assessed on an hourly basis, reveals that the BIPV system significantly improves the daily load cover factor, achieving a value of 12.45 %, and increases up to 20.62 % when considering also the rooftop PV, particularly during spring season. Finally, the capacity credit factor is improved by up to 31.27 %, which is a significant share of grid connection reduction compared to the same building relying totally on the grid for its energy needs.

Polyoxometalate-derived bi-functional crystalline/amorphous interfaces with optimized d-electron configuration for efficient self-powered hydrazine-seawater splitting

Hydrazine-assisted water electrolysis is a promising energy-efficient strategy for hydrogen production. Nonetheless, developing high-performance bi-functional electrocatalysts towards both hydrogen evolution reaction (HER) and hydrazine oxidation reaction (HzOR) remains great challenges, especially for hydrazine-seawater splitting. Herein, we report a polyoxometalate-assisted in-situ synthesis protocol for constructing bi-functional crystalline/amorphous Pt/MoO3-x interfaces toward both efficient HER and HzOR. The polyoxometalate-derived crystalline/amorphous Pt/MoO3-x interfaces deliver working potentials of only –23 and −41 mV at 10 mA cm−2 for HER and HzOR in seawater electrolyte, respectively. Meanwhile, ultrahigh mass activities of 38.39 AmgPt-1 for HER at −100 mV potential and 23.84 AmgPt-1 for HzOR at 50 mV potential are also achieved, over 30.2 and 52.9 times higher than those of commercial Pt/C. As bi-functional electrocatalysts for overall hydrazine-seawater splitting, the electrolyzer requires a cell voltage of only 55/238 mV at 10/100 mA cm−2, 1.584/1.543 V lower than that of the seawater splitting system. Moreover, a proof-of-concept self-powered hydrazine-seawater electrolysis system driven by direct hydrazine fuel cell is further demonstrated, which achieves a hydrogen production rate of 0.29 mL cm−2min−1. DFT calculations verify that, the crystalline/amorphous interfaces endow Pt/MoO3-x an optimized d-electron configuration with favorable H* adsorption and promoted dehydrogenation kinetics of *N2H4 to *N2H3 in the potential-determining step. This work provides a novel strategy and inspiration toward the design of efficient bi-functional electrocatalysts for hydrazine-assisted hydrogen production from seawater.

Foreign experience in designing energy-efficient hostel buildings

The article discusses the reasons for changing the vector of development of the hotel industry to environmental friendliness and sustainability, reducing energy consumption, reducing greenhouse gas emissions and introducing energy-efficient technologies. Examples of foreign experience in implementing energy-efficient strategies in hostels are provided. Environmental friendliness has become a driving force in the hotel industry, with leading hotel chains trying to meet the demands of consumers who are increasingly paying attention to the climate and are concerned about their environmental impact. According to most definitions, the term "hotel industry" includes not only hotels but also motels, guest houses and hostels. When designing hostels, energy conservation and energy efficiency will save energy resources. Thanks to technology and energy-efficient materials, architectural, planning, and space solutions are being improved. The location and orientation also have an impact on energy saving. The author concludes that in the era of heightened climate awareness, the problem of negative environmental impact is relevant. Tourism alone accounts for a certain percentage of greenhouse gas emissions, which is mostly attributable to the hotel industry. It is necessary to take measures and create conditions to reduce and eventually eliminate the harmful impact on the climate. We see that Western countries are aware of the problem and possible negative consequences, and therefore government institutions are involved in solving this problem by creating relevant regulations, businesses implementing environmental programs, and ordinary hotel visitors who are beginning to prefer more environmentally friendly travel options. However, this influence must be spread to other countries, and this is why information campaigns should be used. Communities, including the scientific community, should pay attention not only to continuing the greening of facilities through the use of energy-efficient technologies, which is a necessary step, but also to finding new options for energy saving and creating new energy-efficient strategies.

Development of a rapid assessment tool for integrating thermal comfort in early design stage of energy-efficient office buildings

Comprehending and predicting energy performance of existing buildings and new constructions is crucial towards decarbonization. Instead of utilizing simulation software, multiple regression models are utilized to predict building energy consumption while ensuring indoor thermal comfort to speed up this process. However, previous predictive models prioritize reducing energy demand, with limited focus on thermal comfort. This study aims to support decision-making during retrofitting and new construction planning though developing a prediction model. An air-conditioned office building served as a reference building for simulation. 21 design parameters were analyzed, including aspects of weather, building envelope, internal loads, ventilation, and temperature settings. Stepwise regression results unveiled the crucial variables in the final model, with 8, 9, 13, and 6 variables remaining for peak cooling load, annual cooling load, overheating hours (WE), and Environmental Quality Index for thermal comfort (EQITC) in the perimeter zones, and 5, 6, 8, and 5 variables for the core zones, respectively. Furthermore, insights into the important variables regarding cooling load and thermal comfort were respectively provided. Weather- and envelope-related variables, such as cooling degree-days, global solar radiation, solar heat gain coefficient (SHGC), and U-value, have the highest impacts on cooling load. For thermal comfort, variables including temperature setpoint, occupant activity level, and factors related to window sunlight transmission performance, such as SHGC, window area ratio, and overhang projection ratio, proved to be influential. Overall, this study provided accurate models for assessing optimal strategies for energy efficiency and thermal comfort during the early design phases, advancing building performance practices.