Journal Article

Predicting Diverse Behaviors of Occupants When Turning Air Conditioners on/off in Residential Buildings: An Extreme Gradient Boosting Approach

Aya Hagishima, Jiajun Lyu

2023

Overview

Occupant behavior (OB) has a significant impact on household air-conditioner (AC) energy use. In recent years, bottom-up simulation coupled with stochastic OB modeling has been intensively developed for estimating residential AC consumption. However, a comprehensive analysis of the diverse behavioral preference patterns of occupants regarding AC use is hampered by the limited availability of large-scale residential energy demand data. Therefore, this study aimed to develop a prediction model for the residential household’s AC usage considering various OB-related diversity patterns based on monitoring data of appliance-level electricity use in a residential community of 586 households in Osaka, Japan. First, individual operation schedules and thermal preferences were identified and quantitatively extracted as the two main factors for the diverse behaviors across the whole community. Then, a clustering analysis classified the target households, finding four typical patterns for schedule preferences and three typical patterns for thermal preferences. These results were used, with time and meteorological data in the summer seasons of 2013 and 2014, as inputs for the proposed prediction model using Extreme Gradient Boosting (XGBoost). The optimized XGBoost model showed a satisfactory prediction performance for the on/off state in the testing dataset, with an F1 score of 0.80 and an Area under the Receiver Operating Characteristic (ROC) Curve (AUC) of 0.845.

• Publisher: MDPI AG
• Subject: Air conditioners / Air conditioning / Algorithms / Appliances / Behavior / Building construction / Carbon / Cluster analysis / Clustering / clustering analysis / Energy consumption / Energy demand / Equipment and supplies / extreme gradient boosting method / Green buildings / Households / Influence / Investigations / Machine learning / Meteorological data / occupant’s behavior diversity / Prediction models / Preferences / residential air-conditioning usage / Residential areas / Residential buildings / Residential communities / Residential energy / schedule preference / Schedules / Statistical analysis / Support vector machines / TH1-9745 / thermal preference / Turning behavior