Random Forest model for precise cooling load estimation in optimized and non-optimized form

Chemical Product and Process Modeling, Journal Year: 2025, Volume and Issue: unknown

Published: April 15, 2025

Abstract Energy is vital for life and human development, with global warming due to activities such as the combustion of fossil fuels deforestation emitting dangerous greenhouse gases, changing climate Earth. Global energy demand increasing, developed nations viewing buildings major consumers. Due long lifespan buildings, it important evaluate their suitability future change possible changes in consumption. Appraisal cooling loads each building now required rising costs need reduce impacts caused by consumption from buildings. This paper aims apply Random Forest Regression (RF) Support Vector (SVR), well-known machine learning algorithms predict loads. It utilizes Jellyfish Search Optimizer (JSO) Transit Optimization Algorithm (TSOA) enhance accuracy minimize overall error Cooling Load (CL) estimation. The investigation suggests two high-performance schemes, applies optimizers hybrid an ensemble approach accurate appraisal . Moreover, SHAP method utilized compare effectiveness parameters. research proves be insightful constructing CL projection that a RFJS-based model most effective way optimize attained R 2 0.994 at its best RMSE 0.744. Other than this, following was RSJS, whose were 0.989 0.985, accordingly. third best-performing SVJS values 0.972 1.583,

Language: Английский

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Energy Use Intensity Analysis of Office Buildings Using Green BIM-Integrated Interpretable Machine Learning

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 112760 - 112760

Published: April 1, 2025

Language: Английский

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Automatic channel selection using multi-objective prioritized jellyfish search (MPJS) algorithm for motor imagery classification using modified DB-EEGNET

Neural Computing and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 23, 2025

Language: Английский

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