Predicting long-term monthly electricity demand under future climatic and socioeconomic changes using data-driven methods: A case study of Hong Kong. (July 2021)

• Record Type: Journal Article
• Authors: Liu, Sheng; Zeng, Aaron; Lau, Kevin; Ren, Chao; Chan, Pak-wai; Ng, Edward
• Is Part Of: Sustainable cities and society. Volume 70(2021)
• Publication Date: 2021-07
• Subjects: Long-term prediction -- Electricity demand -- Climate change -- Data-driven methods -- Machine learning methods; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2021.102936
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: 40-years datasets are used to train and validate different data-driven models. The performance of different popular data-driven methods is compared. The proposed GBDT method outperforms other data-driven methods. Future electricity demand in Hong Kong is predicted by the GBDT model. Future climatic and socio-economic uncertainties are considered in the model. Abstract: Data-driven methods, such as artificial neural networks (ANNs), support vector regression (SVM), Gaussian process regression (GPR), multiple linear regression (MLR), decision trees (DTs), and gradient boosting decision trees (GBDTs), are the most popular and advanced methods for energy demand prediction. However, these methods have not been cross compared to analyze their performances for long-term energy demand predictions. Therefore, this paper aims to identify the best method among these data-driven methods for quantifying the impacts of climatic and socioeconomic changes on future long-term monthly electricity demand in Hong Kong. First, historical 40-year climatic, socioeconomic, and electricity consumption data are used to train and validate these models. Second, different representation concentration pathway (RCP) scenarios and three percentiles of 24 global circulation model outputs are adopted as future climatic changes, while five shared socioeconomic pathways are considered for future socioeconomic uncertainties. The results show that the GBDT method provides the best accuracy, generalization ability, and time-series stability, while ANN method exhibits the lowest accuracy and lower generalization ability. The monthly electricity demands in Hong Kong under the RCP8.5–2090 s scenario are predicted to increase by up to 89.40 % and 54.34 % in the residential and commercial sectors, respectively, when compared with 2018 levels.