A hybrid machine learning approach for forecasting residential electricity consumption: A case study in Singapore

Abstract

Ensuring effective forecasting of buildings' energy consumption is crucial in establishing a greater understanding and improvement of buildings' energy efficiency. In Singapore, domestic electricity usage in public residential buildings takes up a significant portion of the country's annual energy consumption. Having effective forecasting approaches is thus important in supporting relevant strategies and policy making. In this research, we proposed a hybrid approach that was based on a combination of building characteristics and urban landscape variables to predict residential housing electricity usage in Singapore. XGboost was also incorporated inside the hybrid approach as the preferred machine learning approach for energy consumption predictions. To demonstrate our proposed approach's predictive strength, the performance of our proposed hybrid machine learning approach was compared with two other models, Geographically Weighted Regression (GWR) model and the Random Forest (RF) model. Results showed that our proposed hybrid model had outperformed these abovementioned approaches with higher accuracy (r² value of 0.9). The proposed approach had thus been effective in forecasting electricity consumption for public housing in Singapore, and it could also be utilised in other similar urban areas for future electricity consumption forecasting.