Random Forest model for precise cooling load estimation in optimized and non-optimized form
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Lei Wang
und
Qingqing Zhang
Abstract
Energy is vital for life and human development, with global warming due to activities such as the combustion of fossil fuels and deforestation emitting dangerous greenhouse gases, changing the climate of the Earth. Global energy demand is increasing, with developed nations viewing buildings as major energy consumers. Due to the long lifespan of buildings, it is important to evaluate their suitability to future climate change and possible changes in energy consumption. Appraisal of the cooling loads in each building is now required due to rising energy costs and the need to reduce the impacts of climate change caused by energy consumption from fossil fuels in buildings. This paper aims to apply Random Forest Regression (RF) and Support Vector Regression (SVR), well-known machine learning algorithms to predict building cooling loads. It utilizes the Jellyfish Search Optimizer (JSO) and Transit Search Optimization Algorithm (TSOA) to enhance accuracy and minimize overall error in Cooling Load (CL) estimation. The investigation suggests two high-performance schemes, applies two optimizers for hybrid schemes, and utilizes an ensemble approach for accurate appraisal. Moreover, the SHAP method is utilized to compare the effectiveness of the parameters. The research proves to be insightful in constructing CL projection and suggests that a RFJS-based model is the most effective way to optimize energy consumption. The hybrid model attained an R 2 of 0.994 at its best and RMSE of 0.744. Other than this, the following effective ensemble approach was RSJS, whose R 2 and RMSE were 0.989 and 0.985, accordingly. The third best-performing model was SVJS with R 2 and RMSE values of 0.972 and 1.583, accordingly.
Acknowledgments
I would like to take this opportunity to acknowledge that there are no individuals or organizations that require acknowledgment for their contributions to this work.
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Research ethics: Research involving Human Participants and Animals: The observational study conducted on medical staff needs no ethical code. Therefore, the above study was not required to acquire ethical code.
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Informed consent: This option is not neccessary due to that the data were collected from the references.
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Author contributions: All authors contributed to the study’s conception and design. Data collection, simulation and analysis were performed by “Lei Wang, Hongmei Gu, and Qingqing Zhang”. The first draft of the manuscript was written by “Hongmei Gu” and all authors commented on previous versions of the manuscript. All authors have read and approved the manuscript.
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Use of Large Language Models, AI and Machine Learning Tools: During the preparation of this work, the authors used Large Language Models, AI, and Machine Learning tools for tasks such as language refinement, data analysis, or figure generation, with all outputs being reviewed and validated by the authors to ensure accuracy and originality. After using these tools/services, the authors reviewed and edited the content and take full responsibility for the content of the published article.
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Conflict of interest: The authors declare no conflict of interest.
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Research funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
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Data availability: The authors do not have permissions to share data.
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© 2025 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Numerical investigation of superheating secondary flow on performance of steam ejector by considering non-equilibrium condensation in renewable refrigeration cycle
- Analysis of pressure drop, energy requirements, and entropy generation in natural gas pipelines at dense and pseudo-dense phases: a CFD study
- Random Forest model for precise cooling load estimation in optimized and non-optimized form
- Energy recovery from mechanical energy of high-pressure natural gas pipeline: a case study simulation
- A numerical simulation of nucleate boiling of water on inclined and rough surfaces
- Optimization and modelling of process parameters for single pass plasma arc welded steel using response surface methodology
- Forecasting gasification process results via radial basis function optimization schemes
- Machine learning approaches for predicting syngas production in biomass gasification
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Numerical investigation of superheating secondary flow on performance of steam ejector by considering non-equilibrium condensation in renewable refrigeration cycle
- Analysis of pressure drop, energy requirements, and entropy generation in natural gas pipelines at dense and pseudo-dense phases: a CFD study
- Random Forest model for precise cooling load estimation in optimized and non-optimized form
- Energy recovery from mechanical energy of high-pressure natural gas pipeline: a case study simulation
- A numerical simulation of nucleate boiling of water on inclined and rough surfaces
- Optimization and modelling of process parameters for single pass plasma arc welded steel using response surface methodology
- Forecasting gasification process results via radial basis function optimization schemes
- Machine learning approaches for predicting syngas production in biomass gasification
