How Smart Meter Data Analysis Can Support Understanding the Impact of Occupant Behavior on Building Energy Performance: A Comprehensive Review

Abstract

The goal of this literature review was to outline the research currently conducted on smart meter (SM) adoption and its connection to building occupant behavior to better understand both SM technology and SM customers. We compiled our findings from the existing literature and developed a holistic understanding of the socio-demographic factors that lead to more or less energy use, the methods used to group and cluster occupants on the basis of energy use, how occupant energy use profiles are developed, and which socio-psychological determinants may influence SM adoption. Our results highlight 11 demographic variables that impact building energy use, find 9 methods commonly used to profile occupants on the basis of energy usage, and highlight 13 socio-psychological variables than can be utilized to better understand SM adoption intentions. The review findings two major deficiencies in the existing literature. First, this review highlights the lack of existing interdisciplinary research that combines occupant behavior with SM data and a clear socio-psychological framework. Second, this review underscores certain data limitations in existing SM research, with most research being conducted only on residential or office buildings and geographically in North America or Western Europe. Final policy recommendations center on increased need for interdisciplinary SM research and the need for an expanded understanding of occupant behavior and SM research across different geographies.

An electricity smart meter dataset of Spanish households: insights into consumption patterns

“…In addition to informing pricing and billing, and providing consumers with detailed reports on their energy consumption, the data collected by smart meters has proven to be extremely valuable for several purposes 1 , 2 , such as: Load profiling , by enabling the creation of specific load profiles that can be used for a variety of applications, including sizing photovoltaic systems 3 , 4 , evaluating energy management systems 5 , 6 , and designing electricity tariffs 7 , 8 . Energy efficiency , by identifying energy waste and inefficiencies in a building or facility 6 , 9 , 10 and informing energy-saving strategies 11 . Renewable energy integration , by monitoring the integration of renewable energy sources into the energy grid 12 and optimizing their use 13 , 14 .…”

“…Energy efficiency , by identifying energy waste and inefficiencies in a building or facility 6 , 9 , 10 and informing energy-saving strategies 11 .…”

“…Design of policies and programs to encourage energy conservation and promote the use of renewable energy sources, by understanding human behavior related to energy consumption 11 , 21 , 22 .…”

The SERL Observatory Dataset: Longitudinal Smart Meter Electricity and Gas Data, Survey, EPC and Climate Data for over 13,000 Households in Great Britain

“…energy performance [39], and fuel poverty detection [40]. The range of data analysis techniques applied to smart meter data is similarly broad and varied.…”

Implementing Smart City Technologies to Inspire Change in Consumer Energy Behaviour

“…Meanwhile, smart meters and in-home displays have a strong potential to raise consumer awareness on energy consumption patterns, energy management services cannot be enabled just by simply installing these devices. It appears that smart meters have not been as effective as originally intended mostly because consumer attitudes and opinions were not considered [22]. Hence, innovative software products and education programs are required to fully exploit smart metering potential contribution to energy efficiency and energy savings.…”