Furuhashi, K.; Nakaya, T. Investigating the Effects of Parameter Tuning on Machine Learning for Occupant Behavior Analysis in Japanese Residential Buildings. Buildings2023, 13, 1879.
Furuhashi, K.; Nakaya, T. Investigating the Effects of Parameter Tuning on Machine Learning for Occupant Behavior Analysis in Japanese Residential Buildings. Buildings 2023, 13, 1879.
Furuhashi, K.; Nakaya, T. Investigating the Effects of Parameter Tuning on Machine Learning for Occupant Behavior Analysis in Japanese Residential Buildings. Buildings2023, 13, 1879.
Furuhashi, K.; Nakaya, T. Investigating the Effects of Parameter Tuning on Machine Learning for Occupant Behavior Analysis in Japanese Residential Buildings. Buildings 2023, 13, 1879.
Abstract
In this study, machine learning was used to predict and analyze the behavior of occupants in Gifu City residences during winter. Global warming is currently progressing worldwide, and it is important to control greenhouse gas emissions from the perspective of adaptation and mitigation. Occupant behavior is highly individualized and must be analyzed to accurately determine a building's energy consumption. The accuracy of heating behavior prediction has been studied using three different methods: logistic regression, support vector machine (SVM), and deep neural network (DNN). The generalization ability of the support vector machine and the deep neural network was improved by parameter tuning. Parameter tuning of the SVM showed that the values of C and gamma affected the prediction accuracy. The prediction accuracy improved by approximately 11.9 %, confirming the effectiveness of parameter tuning on SVM. Parameter tuning of the DNN showed that the values of layer and neuron affected the prediction accuracy. Although parameter tuning also improved the prediction accuracy of DNN, and the rate of increase was lower than that of SVM.
Engineering, Architecture, Building and Construction
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
