Can smart energy information interventions help householders save electricity? A SVR machine learning approach. Issue 112 (October 2020)

• Record Type: Journal Article
• Authors: Wang, Andong; Lam, Jacqueline C.K.; Song, Shiguang; Li, Victor O.K.; Guo, Peiyang
• Is Part Of: Environmental science & policy. Issue 112(2020)
• Page Start: 381
• Page End: 393
• Publication Date: 2020-10
• Subjects: Smart energy monitors (SEMs) -- Smart energy management system (SEMS) -- Smart energy information interventions -- Electricity-saving behaviours -- Machine learning -- SVR; Environmental policy -- Periodicals; Environmental sciences -- Periodicals; Environnement -- Politique gouvernementale -- Périodiques; Sciences de l'environnement -- Périodiques; Environmental policy; Environmental sciences; Periodicals; Electronic journals; 363.70561
• DOI: 10.1016/j.envsci.2020.07.003
• Languages: English
• ISSNs: 1462-9011

Abstract

Highlights: A machine-learning approach for studying the e-saving effects of smart information interventions via SEMS- in HK. An investigation of the statistical correlation between the e-saving effects of three types of smart interventions and household characteristics in HK. A generic and scalable approach for SVR machine-learning-driven SEMS-induced e-consumption studies across different scales and sample sizes. Abstract: Smart energy monitors (SEMs), which enable householders to measure electricity usages of different appliances in real-time, have been widely deployed by utilities across many different countries. However, the actual electricity saving effects of smart information interventions via the SEM connected to the smart energy management system (SEMS) remain inconclusive, due to failures of the existing statistical models in capturing non-linear relationships. To address the non-linearity challenge and to observe the effects of smart information interventions on electricity savings among the public housing householders in Hong Kong, we initiate a longitudinal electricity consumption behavioural study in Hong Kong. We propose a machine-learning approach to capture any non-linearity identified from our SVR machine learning model. In particular, we identify the correlation between the different combinations of three smart information interventions and the percentage of electricity savings at the household-level in Hong Kong. Smart Energy Management System (SEMS), consisting of a smartphone app and a SEM installed respectively on the smartphone and the participant household of our participants in a public housing estate in Hong Kong, have been developed and deployed by the HKU AI-WiSe team. An innovative technological intervention cum environmental behavioural study was conducted on representative of 14 households residing in a public housing estate in Hong Kong, across a one-year period, from 2018 to 2019. Three types of smart information interventions were introduced to our household participants, including their (1) current electricity consumption profile (2) historical electricity consumption profile, and (3) ranking in electricity savings as compared to other participating households. Our study concludes that the overall average electricity savings across all 14 households is 7.1%. However, as different households have displayed different electricity consumption characteristics, the electricity savings vary significantly across 14 households, from slightly negative or almost zero savings, to significantly positive savings. Our results show that with respect to the three types of smart information interventions, Type (1) and Type (2) display a stronger electricity saving effect when compared to the ranking-based smart information intervention. We conclude our study by identifying the right electricity policies for the HKSAR Government to promote household electricity savings via SEMs and SEMS in HK. To the best of our understanding, our study represents the very first attempt to capture the non-linear statistical correlation between smart information interventions and household electricity savings via the machine-learning SVR approach. Our approach is generic and scalable; it can be applicable to other related electricity consumption experimental studies, across any geographical scale and sample size.