Thinking of Green, Low Carbon, and Energy-Saving Designs Based on the Variable Ventilation of Natatoriums: Taking the Jiading Natatorium of Tongji University as an Example DOI Open Access
Feng Qian,

Zedao Shi,

Li Yang

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(11), P. 4476 - 4476

Published: May 24, 2024

With the increasing demand for sports activities, architecture is flourishing. Creating a comfortable and healthy fitness environment while reducing energy consumption has become focus architects. Taking Jiading Natatorium at Tongji University in Shanghai as an example, this study researched green variable ventilation of venues. The Autodesk Ecotect Analysis 2011 was used to conduct computational fluid dynamics (CFD) simulation analyses on four scenarios opening closing swimming pool’s roof, with velocity primary evaluation indicator assess each scenario. relationship between ratio roof buildings explored. results showed that when 37.5%, it achieves good effectiveness avoids excessive wind pressure. also summarized six common forms structures compared differences environments different forms. indicated shape decisive impact distribution indoor speed buildings. Six optimal ratios summer suitable site conditions were summarized, providing reference design selection pool roofs. Furthermore, types trend gradually becoming uniform increase area. However, position peak related form size opening. This research provides valuable references low carbon energy-efficient future

Language: Английский

Natural Ventilation and Energy Consumption Research for Dry Sports Halls Within National Fitness Centers in Cold Regions—Case Study of Qingdao DOI Creative Commons

Wen Zhang,

Lingling Li,

Yu Li

et al.

Buildings, Journal Year: 2025, Volume and Issue: 15(5), P. 734 - 734

Published: Feb. 25, 2025

The lack of energy-saving design in national fitness centers has affected low-cost operation and indoor comfort. Existing studies mainly focus on the impact lighting heat energy consumption sports stadiums, highlighting need for comprehensive planning natural ventilation to improve efficiency. This study uses center Qingdao as a case study, collecting building environmental information through field measurements questionnaire surveys. Four elements were selected: window-to-wall ratio (WWR), proportion operable window area (OWAR), skylight (SAR), floor plan layout. Through utilization Ladybug Tools combination with Radiance EnergyPlus, an annual simulation under conditions was conducted using airflow network model. found that WWR significant lighting, ventilation, consumption. optimal configuration venue determined be 0.37 north facade, 0.26 east, 0.53 south, 0.41 west. Compared no cooling reduced by 18.02%, fan decreased 11.03%. effect when OWAR approximately 30%. When SAR reached 5%, significantly reduced, resulting lowest total also compared differences various layouts influence ventilation. research evaluates efficiency community centers, avoiding hidden transfer typical traditional single-objective optimization methods, improves energy-efficient approach centers.

Language: Английский

Citations

0
Research on building energy consumption prediction algorithm based on customized deep learning model DOI Creative Commons
Zheng Liang, Junjie Chen

Energy Informatics, Journal Year: 2025, Volume and Issue: 8(1)

Published: Feb. 25, 2025

Language: Английский

Citations

0
Machine learning application in building energy consumption prediction: A comprehensive review DOI

Jingsong Ji,

Hao Yu, Xudong Wang

et al.

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 112295 - 112295

Published: March 1, 2025

Language: Английский

Citations

0
Advanced computing to support urban climate neutrality DOI Creative Commons
Gregor Papa, Rok Hribar, Gašper Petelin

et al.

Energy Sustainability and Society, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 11, 2025

Language: Английский

Citations

0
A Systematic Review of Building Energy Consumption Prediction: From Perspectives of Load Classification, Data-Driven Frameworks, and Future Directions DOI Creative Commons
Guanzhong Chen,

Shengze Lu,

Shiyu Zhou

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(6), P. 3086 - 3086

Published: March 12, 2025

The rapid development of machine learning and artificial intelligence technologies has promoted the widespread application data-driven algorithms in field building energy consumption prediction. This study comprehensively explores diversified prediction strategies for different time scales, types, forms, constructing a framework this field. With process as core, it deeply analyzes four key aspects data acquisition, feature selection, model construction, evaluation. review covers three acquisition methods, considers seven factors affecting loads, introduces efficient extraction techniques. Meanwhile, conducts an in-depth analysis mainstream models, clarifying their unique advantages applicable scenarios when dealing with complex data. By systematically combing existing research, paper evaluates advantages, disadvantages, applicability each method provides insights into future trends, offering clear research directions guidance researchers.

Language: Английский

Citations

0
Green intelligent building design based on integrated photovoltaic/thermal building DOI Creative Commons
Tianchi Wang

International Journal of Renewable Energy Development, Journal Year: 2025, Volume and Issue: 14(3), P. 485 - 494

Published: March 19, 2025

With the increasingly prominent contradiction between energy consumption and environmental governance, integrated photovoltaic/thermal building system has broad development prospects in conservation. However, improper placement of photovoltaic solar thermal collectors results inability systems to maximize conversion. In order combine photoelectric photothermal technology with architectural design, realize efficient conversion utilization energy, reduce dependence on traditional sources, consumption, research based comprehensive building, designed an system, optimized for different light resources conditions collectors. The achieved zero operation when total winter was 798.92kW·h. cumulative power supply heat generation throughout were 214.63kW·h 79.68kW·h. This study uses replace roof coverings or insulation layers, which declines impact buildings, avoids duplicate investment cuts cost. can improve efficiency, meet heating needs, enhance resource pollution, promote sustainable construction industry

Language: Английский

Citations

0
Real Implementation and Testing of Short-Term Building Load Forecasting: A Comparison of SVR and NARX DOI Creative Commons
J.J. Hernandez,

Irati Zapirain,

Haritza Camblong

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(7), P. 1775 - 1775

Published: April 2, 2025

In self-consumption (SC) configurations, energy management systems (EMSs) are increasingly being implemented to maximise the ratio (SCR). Recent studies have demonstrated that prediction-based EMSs significantly enhance decision-making capabilities compared non-predictive EMSs. This paper presents design, implementation, and testing on a real system of two machine learning (ML)-type predictive models capable forecasting electricity consumption an individual building using small dataset. A nonlinear autoregressive with exogenous input (NARX) neural network model support vector regression (SVR) were designed compared. These predict day-ahead hourly forecasted meteorological data from Meteo Galicia (MG) occupancy data, both automatically obtained pre-processed. order compensate for lack recurrence SVR model, effect introducing additional input, time vector, was analysed. It is proved ML trained dataset able next day’s average power mean MAPE below 13.96% determination coefficient (R2) greater than 0.78. The most accurately predicts week SVR, which achieves R2 10.73% 0.85, respectively.

Language: Английский

Citations

0
A review of machine learning techniques for building electrical energy consumption prediction DOI Creative Commons

Yuyao Chen,

Wei Gong, Christian Obrecht

et al.

Energy and AI, Journal Year: 2025, Volume and Issue: unknown, P. 100518 - 100518

Published: May 1, 2025

Language: Английский

Citations

0
A high performance solar-assisted ejector expansion refrigeration cycle for residential air conditioning DOI

Seyedeh Zeinab Sajjadi,

Bijan Farhanieh, Hossein Afshin

et al.

Applied Thermal Engineering, Journal Year: 2024, Volume and Issue: 254, P. 123872 - 123872

Published: July 5, 2024

Language: Английский

Citations

3
A Novel Ensemble Deep Learning Model for Building Energy Consumption Forecast DOI Open Access
Mohammad Khodadadi,

ladan riazi,

Shahram Yazdani

et al.

International journal of engineering. Transactions C: Aspects, Journal Year: 2024, Volume and Issue: 37(6), P. 1067 - 1075

Published: Jan. 1, 2024

The issue of energy limitation has gained attention as a crisis faced by societies. Buildings play major role, in consumption making it crucial to accurately predict their usage. This prediction problem led researchers explore machine learning techniques the field efficiency. In this study we investigated performance used methods like Random Forest (RF) Multi Layer Perceptron (MLP) Linear Regression (LR) and deep for predicting building consumption. findings revealed that outperformed solving problem. To address proposed voting based solution combines three CNN models with structures Deep Neural Network (DNN) method. We applied our method WiDS Datathon dataset achieved promising results. Each provide suitable results finally, them is done averaging. Due fact obtains final result from regression high accuracy, considered robust model will be able against new data.

Language: Английский

Citations

2