Building User Research Articles

Integrating aesthetics and sustainability: evaluating the aesthetic perception of photovoltaic installation on the apartment building façade in Jordan

PurposeThis study evaluates the aesthetic perception of photovoltaic (PV) systems situated at various locations on an apartment building facade, comparing them with the original facade. It also aims to understand how aesthetic dimensions influence the perception of PV installations in diverse building locations. Moreover, it aims to create a framework that will guide for installing PV installations considering both their functionality and aesthetics.Design/methodology/approachThe study uses a mixed-method approach, including qualitative and quantitative approaches. It includes a literature review and a questionnaire. 418 participants evaluated different PV-embedded facades using a Likert scale across various aesthetic variables.FindingsThe findings indicate that aesthetic perceptions of PV vary by the location of installation. It also shows that all aesthetic dimensions affect PV installation aesthetics, with location-specific preferences. For original elevation, compatibility and simplicity are given precedence over blending and coherence for windows, creativity and harmony for facades, functionality and harmony for balconies, and innovation potential and simplicity for roofs.Research limitations/implicationsThis study focuses on a single building type; further investigation is required to examine other building types. It also examined one PV technology with common visual properties, but future studies can examine others. Additional research is needed to compare the participating groups and the effect of their sociodemographic factors, using on-site surveys and interviews.Originality/valueFew studies have investigated how PV systems affect apartment building users' architectural aesthetic perception. The results of this study make a valuable contribution to the field of sustainable architecture by providing practical guidance for architects, engineers, stakeholders, and researchers who are interested in integrating aesthetic, user-centric considerations into renewable energy solutions.

Variations in Gender Perceptions of Summer Comfort and Adaptation in Colonial Revival-Style Homes

Past investigations have assessed gender variations in thermal comfort and adaptation in different buildings. However, no reported study has evaluated differences in gender perceptions of the thermal environment in US Colonial Revival-style homes. As a result, this study aims to provide an understanding of variations in gender perceptions of summer comfort and adaptation in the buildings. The study evaluated data collected during the field studies of thermal comfort (FSTC), including physical measurements of environmental variables and subjects’ votes. In this study, 67% females and 33% males participated in the field surveys in summertime. The mean neutral temperature of 25.5 °C and preferred temperature of 24.9 °C were noted for females. For males, the mean neutral and preferred temperatures of 25.8 °C and 25.6 °C were observed. Females feel neutral and prefer “no change” to the thermal environment at lower temperatures than males. A difference of 0.3 °C was noted between the average heat indices for females and males. Male respondents who feel warm perceive less air quality more than females. Females and males who perceive being thermally comfortable also perceive good air quality. Females and males who are thermally comfortable rated air humidity to be acceptable. There are similarities and differences between gender perceptions of the thermal environment. The investigation suggests interventions that can help users regulate their skin temperatures and use control measures that are sensitive to clothing insulation. The research also calls for policies that can promote building users’ pivotal adaptive measures to improve their indoor thermal environment. The study enhances our understanding of sustainable indoor environments and how to improve living conditions and adaptive modifications among different groups of occupants in buildings.

Utilize the Prediction Results from the Neural Network Gate Recurrent Unit (GRU) Model to Optimize Reactive Power Usage in High-Rise Buildings

The growing urbanization and the construction sector, efficient use of electric energy becomes important, especially the use of reactive power. If excessive use causes decreased efficiency and increased operational costs. Decreased efficiency contributes to increasing exhaust gas volumes and greenhouse emissions. Efficient energy can achieved if planning and predictions are correct. This research applies the GRU neural network method with grid search initialization as a novelty predictive model for energy-use high-rise buildings in form fast training without multiple iterations because optimal hyperparameters are obtained. Experimental show the MAE and RMSE performance metrics of the GRU better than LSTM in predicting energy consumption data peak loads, off-peak loads and reactive power. The accuracy of GRU predictions can optimize the use of energy to contribute to saving the environment from exhaust emissions and the greenhouse effect in urban systems. Experimental results demonstrate the superiority of GRU over LSTM, proof of the much lower MAE and RMSE values. This metric shows the accuracy of GRU in generalizing data both during peak and off-peak hours, as well as in reactive power usage. By Utilizing GRU's capabilities, building management can manage reactive power usage effectively, allocate reactive power resources appropriately, and mitigate peak load times and the power factor within the threshold, thus avoiding additional costs and electrical system efficiency and contributing to reducing the carbon footprint and gas emissions greenhouse. Research on GRU is widely open in the high-rise building sector, including its integration with sensors to automatically control energy use.

Measured performance of energy efficiency measures for zero-energy retrofitting in residential buildings

Retrofitting existing buildings to Nearly Zero Energy Buildings (NZEBs) can significantly reduce their energy consumption and environmental impact. However, practitioners and policymakers still face the challenge of providing effective and economic energy efficiency measures (EEMs) to interested parties, due to the limited data on the actual performance of available EEMs. This study addresses the data deficiency by analyzing the actual energy and cost performance of a retrofitted single-family house towards a NZEB. Itemized electricity consumptions, including power consumption of heat pump, water pump, fan, and other accessories, as well as whole building usage, are measured before and after the renovation. New metrics are devised for effectiveness assessment – estimating energy savings upon degree day, cost and change in envelope thermal resistance R-value. The contribution of each EEM to the achieved energy savings is identified and compared. For energy consumption of air-water source heat pump, exterior wall retrofit yields the greatest savings: 1.02 kWh/dd (22.2 %) in winter and 0.64 kWh/dd (33.1 %) in summer, followed by roof retrofit. When the savings are analyzed in terms of per unit increment of R-value, windows/doors retrofit demonstrates the highest priority: 1.66 kWh/dd per unit increment of R-value in winter and 0.62 kWh/dd per unit increment of R-value in summer. Considering costs and energy saving contribution together, the optimal retrofit measure is roof retrofit. This study also contributes to the topic by adding new data to the field and extending retrofit practices to locations outside of North America and Europe.

The significance of indoor thermal comfort on occupants’ perception: in university buildings in Auckland, New Zealand

ABSTRACT Any building creates a unique micro-environment. Educational buildings tend to offer a wide range of purposes, with more operational functions than most other buildings. This paper focuses on the occupant’s experiences of Indoor Thermal Comfort (TC) in educational buildings in Auckland, New Zealand. The TC can impact the occupant’s experience and thus affects student learning. This survey-based study examines users’ experience of the educational building’s comfort levels. The collected data from educational building users (n = 109) was analysed to evaluate the relationship between the perceived experience and the building’s function. The key findings of the survey were: (a) Identifying TC as the most significant factor that directly impacts the occupants’ mood or mental state, even when compared to more cognitive factors; (b) Identifying the lecture room as the most critical space for occupants’ thermal perception; and (c) the deviation between the preferred and experienced mental state, in the selected campus were relatively matching. This study contributes to the existing knowledge of educational buildings by quantifying the impact of TC on the occupant’s experience.

An Innovative Retrofitting Technique of an Industrial Prefabricated Building Without Evacuation

This paper presents a seismic retrofit of an industrial-type precast reinforced concrete building structure using friction dampers. The project consisted of retrofitting two precast reinforced concrete buildings, one of which will be presented in this paper. Precast concrete is one of the industrial structures' most preferred construction methods due to its low cost, fast construction, and availability in rural areas. Unfortunately, most structures constructed before “Specification for Buildings to be Built in Seismic Zones(TBDY 2018)[5]” are not sufficiently engineered and are expected to perform poorly when exposed to a significant seismic event. The general characteristic of precast reinforced concrete buildings built before TBDY 2018 is their relatively high concrete quality (compared to cast-in-place reinforced concrete buildings) and better reinforcement workmanship. But they have some characteristic weaknesses as well. Weak beam-column connections, high drift ratios due to heavy beams, instability problems, and lack of frame behavior can be considered primary weaknesses. General classical retrofitting techniques include increasing beam/column sections or adding new reinforced concrete or steel members to the system or FRP(fiber-reinforced polymer) wrapping. Mostly classical retrofitting needs additional foundations. All these techniques require a long construction period, causing evacuation and stopping building usage. Because stopping the production cost can be tolerated by the owners, a retrofitting approach that can be assembled during the normal function of the industrial structures is required. As a result of this requirement, friction dampers are selected as the supplemental energy dissipation device. ASCE 41-17 [10] is employed for the building's damper design and performance evaluation. Before the damper study, some instability and weak connection problems are solved by traditional strengthening measures. The most effective damper configuration and capacities are selected after an iterative trial-and-error linear study using the simplified methods developed by PROMER. Nonlinear push-over analyses are conducted after linear prestudies. Finally, a nonlinear time-history analysis is performed for confirmation of the results. , It is shown that the proposed retrofit scheme satisfies the desired performance goals for both DBE and MCE events. Overall, it is considered that the proposed retrofit scheme with dampers provides the optimal solution for the Project stakeholders from a performance, design, constructability, and economic point of view. Some application photos will be presented, and methods will be explained in detail in the paper. The friction damper behavior is based on the rotational friction hinge concept. The dampers increase building damping, causing a decrease in earthquake demand. As a result, dampers provide passive energy dissipation and protect buildings from structural and nonstructural damage during moderate and severe earthquakes.

Guidelines for data collection on energy performance of higher-education buildings in Egypt: a case study

ABSTRACT Reliable energy analysis of buildings relies heavily on high-quality data leading to proper indicators. Previous studies have highlighted the importance of data quality in analyzing energy usage in residential and non-residential buildings in order to transform declarations to actions, optimise energy efficiency policies and monitor progress and failures in countries. Collected data must adhere to national and international standards for energy performance in buildings. This study aims to provide practical guidelines for effectively collecting and preparing data suitable for evaluating energy performance in Egyptian higher-education (HE) buildings. The guidelines are developed based on a comprehensive case study, considering data availability in typical educational facilities. Architectural and civil engineering drawings, construction specifications, and occupancy details are accessible. However, actual monthly electrical and natural gas consumption data for individual buildings are lacking. To address this, the study proposes the creation of detailed datasheets for each building, encompassing all energy sources and their electrical and power specifications, such as equipment, machinery, and HVAC systems. These datasheets were utilized to calculate energy consumption and energy usage indicators (EUI). The findings demonstrate that the datasheets enable adequate assessment of energy usage in various spaces within educational buildings, including staff rooms, lecture halls, and laboratories. This facilitates the identification of areas in need of targeted energy efficiency improvements. Notably, the study reveals that electricity consumption in the Faculty of Engineering building is significantly influenced by PCs, laboratories, lighting, and air conditioning.

The Effect of the Temperature Increase on Heat Demand Calculation for Building in the Polish Climatology

Heat demand calculation procedures for buildings are currently being conducted based on the Polish Standard grounded on the European Standard for heat losses methodology. Poland was divided into several climatic zones with given calculations and annual average external air temperature values. In each zone, the technical committee gives the temperature that must be considered. The data included in the Polish Standard preparation concern several years backwards. However, throughout the previous years, we have observed the tendency of the temperature to increase throughout the year, not only in Poland. The phenomenon provides scientists with the conclusion that the temperatures will continue to increase yearly. The low temperatures in the winter season will no longer occur. Therefore, it is necessary to analyse the possibility of lowering the heat demand calculation, including the actual prevailing temperature values. The study was conducted regarding possible economic benefits for the building users due to the lower temperature difference between the internal and external temperatures. Three types of heating systems were analysed. The calculations were conducted for air to water heat pump, pellet boiler and gas condensing boiler, proving that regarding the actual meteorological data, the average annual cost reduction will be in the range of 858 PLN to 1,035 PLN.

The Implementation of “WE R GREEN” Program in ADHI Construction Projects for CO2 Emission Efficiency

As the leading state-owned construction company in Indonesia, PT Adhi Karya (Persero) Tbk (ADHI) has taken certain steps to address sustainability issues, especially when it comes to improving the environmental quality of infrastructure projects. "WE R GREEN" is an environmental program that emphasizes water conservation (W), energy efficiency (E), waste management (R, which stands for remove, reduce, reuse, and recycle), and tree planting activity (GREEN). This study used a construction project in Depok as the sample, where every action has a close relationship with the environment. The purpose of this study is to determine the CO2 efficiency of some project activities following the implementation of the "WE R GREEN" initiatives. The data consists of water consumption, project office building usage, electricity consumption, solid waste production, and planted tree specification. According to the data, total CO2 emissions were 110,62 tons CO2e year prior to program implementation, and the emission has dropped to 73,59 tons CO2e annually following the initiatives. This concludes that the total of 37,03 tons CO2e per year, or 33%, are reduced. Assuming the same number of people and project activities throughout all 109 ADHI projects to put the program into practice. The estimated annual CO2 output would thus decrease 4.036,06 tons CO2e.

The Plegma dataset: Domestic appliance-level and aggregate electricity demand with metadata from Greece

The growing availability of smart meter data has facilitated the development of energy-saving services like demand response, personalized energy feedback, and non-intrusive-load-monitoring applications, all of which heavily rely on advanced machine learning algorithms trained on energy consumption datasets. To ensure the accuracy and reliability of these services, real-world smart meter data collection is crucial. The Plegma dataset described in this paper addresses this need bfy providing whole- house aggregate loads and appliance-level consumption measurements at 10-second intervals from 13 different households over a period of one year. It also includes environmental data such as humidity and temperature, building characteristics, demographic information, and user practice routines to enable quantitative as well as qualitative analysis. Plegma is the first high-frequency electricity measurements dataset in Greece, capturing the consumption behavior of people in the Mediterranean area who use devices not commonly included in other datasets, such as AC and electric-water boilers. The dataset comprises 218 million readings from 88 installed meters and sensors. The collected data are available in CSV format.

Economic operation of an agent-based virtual storage aggregated residential electric-heating loads in multiple electricity markets

The increasing penetration of intermittent renewable energy sources leads to a higher demand for flexible balancing service in the power system. Consequently, demand-side management techniques, such as utilizing flexible resources from residential electric-heating loads, provide a promising solution. However, due to various uncertainties such as stochastic weather and market prices, it is challenging to develop an economically efficient strategy. Hence, this paper proposes an economic operation optimization approach for residential electric-heating loads to develop optimal bidding and scheduling strategy in the multi-stage market. First, the dynamic characteristics of aggregated electric-heating loads are modeled as the virtual energy storage systems (VESS) to quantify the flexibility potential arising from the combination of building thermal inertia, user tolerance and thermal energy storage (TES). Subsequently, the joint energy and balancing service market is introduced to exploit the operating flexibility of VESS. A multi-agent deep reinforcement learning (MADRL) optimization framework is proposed to address the multi-objective problem at different decision frequency, which involves achieving lower economic costs while maintaining indoor comfort. Finally, we combine the DRL with imitation learning (IL) by introducing the behavior cloning mechanism to mitigate training process instability. It reduces the computational costs and eliminates the need for redundant additional structure. Comparative simulation experiments demonstrate the efficiency of data-driven optimization approach, which provides an adaptive, economic cost-effective and general solution for multi-type aggregators to activate their flexibility in dynamic markets.

Pengaruh Fitur Dan Kemudahan Penggunaan Terhadap Kepuasan Pengguna Aplikasi Si Rekap KPU

This study aims to examine the effect of features and ease of use on user satisfaction of the Si Rekap KPU Application used in supporting the vote counting and recapitulation process in the 2024 Election. The research method uses a quantitative approach with an experimental design. Data was collected through a questionnaire distributed to 75 KPPS officers from the East Java region who had carried out the vote counting and recapitulation process in the 2024 Election using the Si Rekap KPU Application. The questionnaire was used to obtain data on perceived ease of use, usefulness, and acceptance of application information technology. The results showed that the ease of use and usefulness of application features had a significant positive effect on user satisfaction, with a contribution of 70% and 73%, respectively. Acceptance of application information technology by users also has a significant positive effect on user satisfaction with a contribution of 75%. Based on these results, it can be concluded that feature improvements, ease of use, and building user acceptance are needed to increase user satisfaction with the Si Rekap KPU Application in supporting the election process. Application improvements based on evaluation results are expected to optimize its performance.

Forecasting the total building energy based on its architectural features using a combination of CatBoost and meta-heuristic algorithms

This research examines the overall energy usage in residential buildings, focusing on architectural characteristics. The study utilizes a combination of the CatBoost method and meta-heuristic algorithms for analysis. The main approach of this research is based on the accuracy defects of individual models, which leads to the employment of CatBoost as a group model. Due to the lack of enough examinations while utilizing CatBoost method, this model and its hyperparameters are optimized using various meta-heuristic methods, including Phasor Particle Swarm Optimization (PPSO), Slime Mould Algorithm (SMA), Sparrow Search Algorithm (SSA), Ant Lion Optimizer (ALO), Artificial Bee Colony (ABC), and Grey Wolf Optimizer (GWO). Eventually, the performance of all models is compared by conduction of a case study, using diverse statistical examination indexes divided by the dwelling types i.e., (1) Standard efficiency upgraded dwellings (D1), (2) High efficiency upgraded dwellings (D2), and (3) Ultra high efficiency upgraded dwellings (D3). The results show that the hybrid proposed method has a proper ability to investigate the total site energy. The results show that for the D1 dwelling and according to the test dataset, the integrated CatBoost-SMA model indicates the most desired performance in predicting the total site energy. But for D2 and D3 dwellings and referring to the test dataset, the statistical evaluation indexes emphasize that the integrated CatBoost-PPSO method shows the most reliable performance.

Synergies between digital construction technologies in smart buildings and smart city development to meet building users’ expectations

In smart buildings, digital construction technologies can support more efficient management of data and information related to building components. This paper aims to draw a robust linking mechanism between digital construction technologies that support smart buildings and smart city development to satisfy building users' expectations. Data was attained using a qualitative approach via secondary data from literature and primary data in the context of case study with building users. The study suggests the importance of recognising single/multi-purposed data to support better synergy between digital construction technologies and in smart buildings and smart city development to satisfy building users’ expectations.

Investigation of an automobile air-conditioner with a liquid-suction heat exchanger using R134a and R1234yf

Air-conditioning systems (ACs) are essential in hot and humid climates to ensure acceptable ambient air quality as well as thermal comfort for buildings users. It is essential to improve refrigeration system performance without increasing the effects of global warming potential (GWP) and ozone depletion potential (ODP). The main objective of this study is to evaluate the performance of an air conditioning system that operates with a liquid suction heat exchanger (LSHX) through implementing refrigerants with zero OPD and low GWP (i.e., R134a and R1234yf). Liquid suction heat exchanger (LSHX) was added to an automobile air conditioning system (AACS).When Liquid suction heat exchanger was added to the cycle, primary results indicated that an enhancement in the cycle coefficient performance (COP) by 25.2 %and 17.3% for R134a and R1234yf respectively, and decreasing in mass flow rate of the refrigerants used (m ̇_r). Also increasing in refrigeration effect (RE) by 4.2% and 2.3% for R134a and R1234yf respectively. Presence of LSHX caused increasing in heat rejected by condenser (Qcond.) according to increasing in sub-cooling degree and decreasing in compressor work (Wcomp.), increasing in condenser exergy destruction (X. des. cond.) and thermostatic expansion device (TXV) exergy destruction, but decreasing in compressor and evaporator. Summary of previous experimental and numerical studies is presented as well.

Exploring the influence of building form parameters on three-dimensional ventilation potential in urban centers: A case study of Nanjing, China

In urban areas, variations in the functional usage of buildings or their clusters directly influence the 2D/3D morphology of buildings which can lead to changes in the ventilation potential (VP) at different heights, but there is a lack of refined quantitative relationship analysis between 3D building form parameters (BFPs) and VP at different heights within a certain urban area. The objective of our study is to obtain simulated wind field data from different heights, considering distances from roads, and transverse and longitudinal sections. We employ partial correlation analysis, simple correlation analysis, and Geographic detectors to uncover the extent and mechanisms of the influence of BFPs on the ventilation effectiveness of neighborhoods in a major urban center in China, specifically Nanjing. The findings demonstrate that, at all heights, there is an inverse relationship between wind speed and building density (BD), with the location of higher wind speed generally aligning with the intersection points. The correlation coefficients between BD and simulated wind speed are 0.040 (1.5 ​m), −0.475 (10 ​m), and −0.314 (30 ​m), while the correlation coefficients between building height (BH) and simulated wind speed are 0.237. However, no significant correlation is observed between floor area ratio (FAR) and simulated wind speed. Geographic detector analysis reveals differences in the correlation between BFPs and simulated wind speed at different heights within various building functional areas. Our study provides some practical information for urban planners and designers to carry out wind sensitivity and adaptation planning and urban design.

Environmental Quality bOX (EQ-OX): A Portable Device Embedding Low-Cost Sensors Tailored for Comprehensive Indoor Environmental Quality Monitoring.

The continuous monitoring of indoor environmental quality (IEQ) plays a crucial role in improving our understanding of the prominent parameters affecting building users' health and perception of their environment. In field studies, indoor environment monitoring often does not go beyond the assessment of air temperature, relative humidity, and CO2 concentration, lacking consideration of other important parameters due to budget constraints and the complexity of multi-dimensional signal analyses. In this paper, we introduce the Environmental Quality bOX (EQ-OX) system, which was designed for the simultaneous monitoring of quantities of some of the main IEQs with a low level of uncertainty and an affordable cost. Up to 15 parameters can be acquired at a time. The system embeds only low-cost sensors (LCSs) within a compact case, enabling vast-scale monitoring campaigns in residential and office buildings. The results of our laboratory and field tests show that most of the selected LCSs can match the accuracy required for indoor campaigns. A lightweight data processing algorithm has been used for the benchmark. Our intent is to estimate the correlation achievable between the detected quantities and reference measurements when a linear correction is applied. Such an approach allows for a preliminary assessment of which LCSs are the most suitable for a cost-effective IEQ monitoring system.

Impact of Integrating Photovoltaic-Shading Systems in Hotel Hospitality Design in Oniru Beach Lagos By IJISRT

Globally, energy efficiency has been a threat to building sustainability, therefore alternative energy sources tend to overtake the consumption of power usage in buildings. This study delves into comparing the effects of incorporating photovoltaic shading systems (PVSS) into luxury hospitality design. with a particular emphasis on sustainability, energy efficiency, and customer satisfaction. Through a thorough examination of case studies and literature surrounding PVSS integration, reviewing journals on this subject this article elucidates the advantages, challenges, and ramifications for luxury hotel properties. By conducting a comprehensive analysis, it underscores the pivotal role that PVSS play in shaping the future landscape of sustainable hospitality architecture. This essay delivers valuable insights into the current energy usage and the integration of PVSS in buildings. The findings of this investigation underscore the potential of PVSS to enhance environmental performance, operational efficiency, and guest satisfaction within luxury hotel properties and drive overall business performance. It is recommended to encourage collaboration between architects, developers, and energy professionals to optimize PVSS integration in luxury hospitality design, Educate stakeholders about the benefits of PVSS integration and provide incentives or subsidies to promote sustainable design practices in luxury resort development.

ЕНЕРГОСПОЖИВАННЯ ШКОЛИ В УМОВАХ НЕПОВНОЇ ЗАЙНЯТОСТІ ДЛЯ РІЗНИХ РЕЖИМІВ ОПАЛЕННЯ ПРИМІЩЕНЬ

The challenges facing the Ukrainian education system, particularly in light of demo-graphic changes and the impact of war, are examined in this research. It highlights the poten-tial for optimizing the operation modes of school buildings under conditions of partial occu-pancy to reduce energy consumption and ensure comfortable conditions for students. Energy modeling analysis using DesignBuilder software shows that the configuration of classrooms significantly impacts the energy efficiency of buildings. The best results for energy efficiency were achieved with rooms stacked on top of each other, outperforming the variant with three rooms on the middle floor by 22.3%, and mixed configurations by 8.5% and 16%. This efficiency is due to minimized contact with unheated premises and maximized solar heat gains. However, without appropriate building upgrades, the use of limited spaces may not provide adequate comfort conditions in colder periods. For the rooms studied (45-48m^2), the required heating device capacity to maintain comfortable conditions varies from 7.5 to 10.5 kW, depending on the level of insulation, which is 3-5 times more than the standard capacity for such spaces. Providing comfortable conditions without upgrading the heating system in cold periods is unfeasible, even with high levels of building insulation and a constant heating schedule. Therefore, implementing partial use of school buildings, despite their high potential for en-ergy savings, cannot satisfy student comfort needs without corresponding improvements. Future research could provide a foundation for developing comprehensive strategies for managing school spaces, considering variable building usage conditions and ensuring an optimal combination of energy efficiency, cost-effectiveness, and comfort for students and staff. The findings can be useful for school administrations, local educational authorities, and policymakers in education and energy efficiency. Further studies are suggested to explore the impact of internal wall insulation, increased heating device capacity, and heating uncondi-tioned rooms to a certain temperature level on energy efficiency and comfort conditions in occupied spaces.

A New Model for Building Energy Modeling and Management Using Predictive Analytics: Partitioned Hierarchical Multitask Regression (PHMR)

Buildings are major consumers of energy, accounting for a significant proportion of total energy use worldwide. This substantial energy consumption not only leads to increased operational costs but also contributes to environmental concerns such as greenhouse gas emissions. In the United States, building energy consumption accounts for about 40% of total energy use, highlighting the importance of efficient energy management. Therefore, accurate prediction of energy usage in buildings is crucial. However, accurate prediction of building energy consumption remains a challenge due to the intricate interaction of indoor and outdoor variables. This study introduces the Partitioned Hierarchical Multitask Regression (PHMR), an innovative model integrating recursive partition regression (RPR) with multitask learning (hierML). PHMR adeptly predicts building energy consumption by integrating both indoor factors, such as building design and operational variables, and outdoor environmental influences. Rigorous simulation studies illustrate PHMR’s efficacy. It outperforms traditional single-predictor regression models, achieving a 32.88% to 41.80% higher prediction accuracy, especially in scenarios with limited training data. This highlights PHMR’s robustness and adaptability. The practical application of PHMR in managing a modular house’s Heating, Ventilation, and Air Conditioning (HVAC) system in Spain resulted in a 37% improvement in prediction accuracy. This significant efficiency gain is evidenced by a high Pearson correlation coefficient (0.8) between PHMR’s predictions and actual energy consumption. PHMR not only offers precise predictions for energy consumption but also facilitates operational cost reductions, thereby enhancing sustainability in building energy management. Its application in a real-world setting demonstrates the model’s potential as a valuable tool for architects, engineers, and facility managers in designing and maintaining energy-efficient buildings. The model’s integration of comprehensive data analysis with domain-specific knowledge positions it as a crucial asset in advancing sustainable energy practices in the building sector.