The Contribution of Bottom-Up Energy Models to Support Policy Design of Electricity End-Use Efficiency for Residential Buildings and the Residential Sector: A Systematic Review

Abstract

Bottom-up energy models are considered essential tools to support policy design of electricity end-use efficiency. However, in the literature, no study analyzes their contribution to support policy design of electricity end-use efficiency, the modeling techniques used to build them, and the policy instruments supported by them. This systematic review fills that gap by identifying the current capability of bottom-up energy models to support specific policy instruments. In the research, we review 192 publications from January 2015 to June 2020 to finally select 20 for further examination. The articles are analyzed quantitatively in terms of techniques, model characteristics, and applied policies. The findings of the study reveal that: (1) bottom-up energy models contribute to the support of policy design of electricity end-use efficiency with the application of specific best practices (2) bottom-up energy models do not provide a portfolio of analytical methods which constraint their capability to support policy design (3) bottom-up energy models for residential buildings have limited policy support and (4) bottom-up energy models’ design reveals a lack of inclusion of key energy efficiency metrics to support decision-making. This study’s findings can help researchers and energy modelers address these limitations and create new models following best practices.

Policy Design for Electricity Efficiency: A Case Study of Bottom-Up Energy Modeling in the Residential Sector and Buildings

“…The present set of best practices has been derived from prior research studies that have provided valuable insights into the potentialities within the field of BUEM design [16,55].…”

“…According to recent studies, there are not many publications that specifically address this subject, and there does not seem to be any consistency in the usage of scenarios or important metrics [16]. These findings demonstrate the urgent need to develop a cutting-edge strategy that would successfully guide decision-makers in developing energy efficiency initiatives.…”

Bottom-up assessment of household electricity consumption in dynamic cities of the Global South—Evidence from Kigali, Rwanda

“…To avoid any confusion, reference is made to this definition of electrical appliance ownership in the whole article. The literature also speaks of diffusion (rate), penetration (rate), or saturation (rate), whereby different definitions are used as values above 100% are possible (McNeil and Letschert, 2010;McLoughlin et al, 2012;Agyeman et al, 2015;Cabeza et al, 2018;Dominguez et al, 2019;Sanchez-Escobar et al, 2021). In addition to the ownership of electrical appliances per se, this article also analyses the categorization of these appliances.…”

Electricity Demand Side Management

“…Regarding end-uses, Talei et al [14] aimed to identify smart building inefficiencies using machine learning, applied to a single building's HVAC consumption data and building usage data in a highly efficient office building. Sanchez-Escobar et al [15] reviewed the contribution of bottom-up energy models to support policy design for electricity enduse efficiency in residential buildings. Hummel et al [16] measured the electric power demand of air compressors in industrial facilities, finding key influencing factors for the compressed air electric ratio.…”