Startseite Energy efficiency in cooling systems: integrating machine learning and meta-heuristic algorithms for precise cooling load prediction
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Energy efficiency in cooling systems: integrating machine learning and meta-heuristic algorithms for precise cooling load prediction

  • Kunming Xu EMAIL logo
Veröffentlicht/Copyright: 1. Juli 2024
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Chemical Product and Process Modeling
Aus der Zeitschrift Chemical Product and Process Modeling Band 19 Heft 4

Abstract

Since cooling load estimation directly impacts air conditioning control and chiller optimization, it is essential for increasing the energy efficiency of cooling systems. Machine learning outshines traditional regression analysis by efficiently managing vast datasets and discerning complex patterns influenced by various factors like occupancy, building materials, and meteorology. These capabilities greatly enhance building management and energy optimization. The primary objective of this study is to introduce a framework based on ML algorithms to accurately predict cooling loads in buildings. The Decision Tree model was chosen as the core algorithm for this purpose. Furthermore, as an innovative approach, four metaheuristic algorithms – namely, the Improved Arithmetic Optimization Algorithm, Prairie Dog Optimization, Covariance Matrix Adaptation Evolution Strategy, and Coyote Optimization Algorithm – were employed to enhance the predictive capabilities of the Decision Tree model. The dataset which utilized in this study derived from previous studies, the data comprised of eight input parameters, including Relative Compactness, Surface Area, Wall Area, Roof Area, Overall Height, Orientation, Glazing Area, and Glazing Area Distribution. With an astonishing R 2 value of 0.995 and a lowest Root Mean Square Error value of 0.660, the DTPD (DT + PDO) model performs exceptionally well. These astounding findings demonstrate the DTPD model’s unmatched precision in forecasting the results of cooling loads and point to its potential for useful implementation in actual building management situations. Properly predicting and managing cooling loads ensures that indoor environments remain comfortable and healthy for occupants. Maintaining optimal temperature and humidity levels not only enhances comfort but also supports good indoor air quality.

Keywords: machine learning; prediction; building’ cooling load; Decision Tree; metaheuristic optimization
Corresponding author: Kunming Xu, Campus Planning and Construction Center, Changchun University, Changchun 130022, Jilin, China, E-mail:

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.

  1. 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.

  2. Informed consent: This option is not neccessary due to that the data were collected from the references.

  3. Author contributions: The author contributed to the study’s conception and design. Data collection, simulation and analysis were performed by “Kunming Xu”.

  4. Competing interests: The author declare no competing of interests.

  5. Research funding: No funding.

  6. Data availability: The author do not have permissions to share data.

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Received: 2024-01-30
Accepted: 2024-06-12
Published Online: 2024-07-01

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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  2. Review
  3. Computational chemistry unveiled: a critical analysis of theoretical coordination chemistry and nanostructured materials
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  6. Numerical investigation of discharge pressure effect on steam ejector performance in renewable refrigeration cycle by considering wet steam model and dry gas model
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  8. Energy efficiency in cooling systems: integrating machine learning and meta-heuristic algorithms for precise cooling load prediction
  9. A parametric study on syngas production by adding CO2 and CH4 on steam gasification of biomass system using ASPEN Plus
  10. Temperature optimization model to inhibit zero-order kinetic reactions
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https://doi.org/10.1515/cppm-2024-0006
Schlagwörter für diesen Artikel
machine learning; prediction; building’ cooling load; Decision Tree; metaheuristic optimization

Artikel in diesem Heft

  1. Frontmatter
  2. Review
  3. Computational chemistry unveiled: a critical analysis of theoretical coordination chemistry and nanostructured materials
  4. Research Articles
  5. Reducing sludge formation by enhancing biological decay of biomass: a mathematical model
  6. Numerical investigation of discharge pressure effect on steam ejector performance in renewable refrigeration cycle by considering wet steam model and dry gas model
  7. Natural pigment indigoidine production: process design, simulation, and techno-economic assessment
  8. Energy efficiency in cooling systems: integrating machine learning and meta-heuristic algorithms for precise cooling load prediction
  9. A parametric study on syngas production by adding CO2 and CH4 on steam gasification of biomass system using ASPEN Plus
  10. Temperature optimization model to inhibit zero-order kinetic reactions
  11. Multi-objective Bonobo optimisers of industrial low-density polyethylene reactor
  12. Assessment the thermal performance of square twisted double tube heat exchanger with Al2O3 nanofluid
  13. Short Communication
  14. Layouts and tips for a typical final-year chemical engineering graduation project
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