How the decision to accept or reject PV affects the behaviour of residential battery system adopters. (15th July 2022)

• Record Type: Journal Article
• Authors: Alipour, Mohammad; Taghikhah, Firouzeh; Irannezhad, Elnaz; Stewart, Rodney A.; Sahin, Oz
• Is Part Of: Applied energy. Volume 318(2022)
• Publication Date: 2022-07-15
• Subjects: Attitude change -- Solar photovoltaic (PV) system -- Battery energy storage system -- Artificial intelligence -- Technology diffusion; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2022.119194
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Decision on PV system uptake as the 'past behaviour' of battery adoption behaviour. Accepting or rejecting PVs may pose two attitudinal changes: regret and despair. Application of machine learning methods to identify predictors of attitude change. Behavioural beliefs, peers and personality traits drive the attitude change. Abstract: When residential rooftop solar photovoltaic (PV) systems are widely accepted across society, the uptake of home battery energy storage systems is closely tied to the PV-status quo and the behaviour previously taken by households. This study proposes that a decision of acceptance or rejection of PV systems is the past behaviour of the battery adoption decision. This antecedent role of PV behaviour may spark two attitudinal changes: (a) feelings of regret, which may occur among PV adopters, stemming from a positive experience of using the system, or among non-adopters due to their rejection of the system, and (b) feelings of despair, which may arise among current PV users due to dissatisfaction and discontent with the system. While regret positively changes consumers' attitude towards battery adoption expediting an earlier purchase, despair could preclude battery installation. Through a survey of 557 households in South East Queensland, Australia, this study investigated factors driving attitude change. Instead of traditional statistical methods, machine learning algorithms were adopted to derive data-driven models of attitude change, allowing for higher prediction accuracy and a determination of the latent causalities. The main findings indicate that perceived attitudes towards financial and non-financial benefits, followed by informal peers, best estimate the attitude change, whereas traditional sociodemographic factors, knowledge and affordability may not engender a shift. Leveraging from this new paradigm can encourage current PV consumers to take another step and become earlier battery adopters. Failure to recognise these dynamics may breed despair and turn current innovators and early adopters of PVs into late adopters or rejectors of battery systems.