Predicting long-term monthly electricity demand under future climatic and socioeconomic changes using data-driven methods: A case study of Hong Kong
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AbstractData-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 RCP8.5-2090s 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.
Acceptance Date10/04/2021
All Author(s) ListSheng Liu, Aaron Zeng, Kevin Lau, Chao Ren, Pak-wai Chan, Edward Ng
Journal nameSustainable Cities and Society
Volume Number70
Article number102936
LanguagesEnglish-United States
KeywordsLong-term prediction, Electricity demand, Climate change, Data-driven methods, Machine learning methods

Last updated on 2021-16-06 at 23:52