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.

A Decision Making Centers (DMC) Environment facilitates the understanding of complex problems and simplifies the decision making process of policy makers and stakeholders by playing out several what if scenarios. One of the key elements in this environment is the management of Predictive Analytics and Data Mining (PADM) techniques required for real-time and accurate representation of data in scenarios. However, there has been a gap in understanding the best practices that facilitate the execution of PADM in environments such as a DMC. In this paper we aim to address this gap. Specifically, we apply process mining techniques to reveal: 1) process flow and executed activities in PADM projects and 2) The relationship and time distribution of activities within a PADM project execution. To this end two successful projects were analyzed in order to find relationships among activities and time metrics during execution. The present study aims to support managers in the execution of large scale PADM projects.

Energy models play a crucial role in the domain of energy policy by serving as essential instruments for decision-making. However, a significant limitation of numerous bottom-up energy models (BUEMs) is their empirical design, which hinders their ability to effectively inform policy design. This study presents a structured framework that can be used to improve the effectiveness of behavior, understanding, and engagement measures in the development of BUEMs for enhancing energy end-use efficiency. The model selected for this case study was provided by the Mexican Commission for the Efficient Use of Energy (CONUEE), and it examines the impact of regulatory instruments on the residential sector and residential buildings. The benefits of the proposed framework were successfully demonstrated through a quantitative comparison of real energy models, using and without using the said framework, revealing the advantages of its use. The framework significantly decreases the time required for model generation in various aspects by 59.43%. The obtained results highlight the effectiveness of the framework, and it could enhance the existing knowledge in the sector.