Journal of Applied Science and Engineering

Published by Tamkang University Press

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Yuanke ShenThis email address is being protected from spambots. You need JavaScript enabled to view it.

School Enterprise Cooperation and Employment Guidance Center, Zibo Vocational Institute, Zibo 255300, Shandong, China


 

 

Received: December 12, 2023
Accepted: March 14, 2024
Publication Date: May 22, 2024

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202503_28(3).0010  


Environmental issues, energy security, thermal comfort, and economic considerations are the primary driving forces motivating research efforts to curb energy consumption and the associated greenhouse gas emissions across various sectors of the economy. Analyzing the energy performance of buildings proves to be a complex undertaking due to its reliance on numerous variables linked to building attributes, weather conditions, equipment and systems, occupant behavior, and sociological factors. This paper presents a non-parametric, Bayesian machine learning approach for analyzing and forecasting building heating load. The foundational technique in this approach is the utilization of the Gaussian Process Regression model. In pursuit of optimization goals, two meta-heuristic algorithms, namely Prairie Dog Optimization and the Arithmetic Optimization algorithm, are incorporated. This results in the development of three models: one for the singular GPR model and two for the hybridized versions of the model. Together, these models are employed to estimate the heating load of buildings. The study underscores the significance of identifying model input variables and conducts a thorough performance comparison among various machine learning methods and measured heating demand. Offering valuable insights into the forecasting of building heating loads, the research puts forth the GPAO (Combination of GPR and AOA) model as the optimal choice for achieving optimized energy production, distribution, and consumption in building systems. The obtained results show that Gaussian Process Regression, enhanced with an Arithmetic optimization algorithm, is a more suitable machine learning method with a maximum coefficient of determination of 0.991 and minimum performance errors of 0.955.


Keywords: Heating Load Demand; Prediction Models; Building Energy Consumption; Gaussian Process Regression; Metaheuristic Optimization Algorithms


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