Optimizing Heating Load Prediction for Enhanced Energy Conservation and Emissions Reduction: Integration of KNearest Neighbour Model with Meta-Heuristic Algorithms

Yijia  Yuan; Kailun  Chen; Yujie  Wu; Jian  Wang; Yue  Sheng; Jiahui  Li; Baohua  Shen

doi:10.6180/jase.202505_28(5).0001

Optimizing Heating Load Prediction for Enhanced Energy Conservation and Emissions Reduction: Integration of KNearest Neighbour Model with Meta-Heuristic Algorithms

Computer Science and Information Engineering

Yijia Yuan, Kailun Chen, Yujie Wu, Jian Wang, Yue Sheng, Jiahui Li, and Baohua ShenThis email address is being protected from spambots. You need JavaScript enabled to view it.

Information Engineering College, Hangzhou Dianzi University, Hangzhou 311305, Zhejiang, China

Received: February 13, 2024
Accepted: May 26, 2024
Publication Date: July 9, 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.202505_28(5).0001

Energy conservation and emissions reduction are pivotal goals that hinge on precise energy consumption estimating and the assessment of retrofit options. Prioritizing energy-efficient practices in building management has gained significance in both theoretical research and practical applications. This ongoing research seeks to deliver a comprehensive solution by amalgamating advanced optimization algorithms with meticulous prediction of heating load (HL), addressing the pressing need for accuracy in this domain. The investigation delves into the intricate realm of HL systems, where the intricacies of energy optimization present diverse challenges necessitating thorough exploration and innovative problem-solving approaches. Two meta-heuristic techniques, the Adaptive Opposition Slime Mould Algorithm (AOSMA) and the Snake Optimizer (SO), have been combined to improve the accuracy of the K-Nearest Neighbour (KNN) model. These algorithms scrutinize HL data collected from various soil types via prior stability tests to validate the models. Three unique models are presented in the study: KNSO, KNAO, and an independent KNN model. All three models provide insightful information for accurate HL prediction. Notably, the KNAO model is a standout performance with an RMSE value of 0.8003 and an R² value of 0.993 which is remarkably low. These results underscore the effectiveness of the KNAO model in predicting HL outcomes with remarkable accuracy.

Keywords: Heating Load; K-Nearest Neighbor; Snake Optimizer; Adaptive Opposition Slime Mould Algorithm

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