OS12-4 Machine Learning Approach to Predict Cooling Load for Existing Buildings

Title:	OS12-4 Machine Learning Approach to Predict Cooling Load for Existing Buildings
Publication:	ICAROB2024
Volume:	29
Pages:	333-336
ISSN:	2188-7829
DOI:	10.5954/ICAROB.2024.OS12-4
Author(s):	Makoto Ohara, Hideo Isozaki
Publication Date:	February 22, 2024
Keywords:	Cooling Load Prediction, Air Conditioning System, Existing Building Data, Machine Learning
Abstract:	The objective of this study is to predict air conditioning loads for existing buildings using operational data, weather forecasts and visitor forecasts. The proposed prediction method is based on a neural network approach. However, it is important to note that the proposed method does not learn the entire loads. Loads are divided into factors which can be predicted by traditional thermodynamics and factors which are subject to machine learning. The proposed method has been applied to an example instance using operational data from an underground mall in Kobe, and its validity has been confirmed.
PDF File:	https://alife-robotics.co.jp/members2024/icarob/data/html/data/OS/OS12-4.pdf
Copyright:	© The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/