Interpretable and scalable data-driven methodologies providing high granularity baseline predictions of energy use in buildings are essential for the accurate measurement and verification of energy renovation projects and have the potential of unlocking considerable investments in energy efficiency worldwide. Bayesian methodologies have been demonstrated to hold great potential for energy baseline modelling, by providing richer and more valuable information using intuitive mathematics. This paper proposes a Bayesian linear regression methodology for hourly baseline energy consumption predictions in commercial buildings. The methodology also enables a detailed characterization of the analyzed buildings through the detection of typical electricity usage profiles and the estimation of the weather dependence. The effects of different Bayesian model specifications were tested, including the use of different prior distributions, predictor variables, posterior estimation techniques, and the implementation of multilevel regression. The approach was tested on an open dataset containing two years of electricity meter readings at an hourly frequency for 1578 non-residential buildings. The best performing model specifications were identified, among the ones tested. The results show that the methodology developed is able to provide accurate high granularity baseline predictions, while also being intuitive and explainable. The building consumption characterization provides actionable information that can be used by energy managers to improve the performance of the analyzed facilities.

Renewable Energy Communities (RECs) have significant potential for the energy transition. Since RECs rely on citizen participation, their success heavily depends on the level of engagement among citizens. However, if users are not provided with transparent and accurate information, they may abandon the project when outcomes fail to align with unrealistic expectations. To address this challenge, this paper presents a thorough comparative study which identifies recommendations and actionable guidance, providing crucial information to prevent failures. We compared scenarios defined by fixed energy allocation parameters, based on installation costs and number of participants, and variable ones which consider user patterns, economic agreements, and allocation criteria. This approach enabled a comprehensive analysis of how different characteristics affect key outcomes, such as payback difference and surplus. To ensure the scenarios were realistic, the building blocks were obtained using clustering techniques on a real dataset of residential users. This allowed us to simulate 240 scenarios of different REC configurations. The methodology is designed for applicability in countries where RECs are governed by energy allocation coefficients, such as Spain and France. This paper aims to highlight both the limitations and the best possible outcomes, setting clear expectations and helping to curb the spread of disinformation, thereby facilitating RECs’ decision-making.