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The architecture, engineering, construction, and operations (AECO) sector exerts a considerable influence on energy consumption and CO2 emissions released into the atmosphere, making a notable contribution to climate change. It is therefore imperative that energy efficiency in buildings is prioritized in order to reduce environmental impacts and meet the targets set out in the European 2030 Agenda. In this context, renewable energy communities (RECs) have the potential to play an important role, promoting the use of renewable energy at the local level, optimizing energy management, and reducing consumption by sharing resources and advanced technologies. This paper introduces an open tool (OT) designed for the configuration of energy systems dedicated to RECs. The OT considers several inputs, including thermal and electrical loads, energy consumption, the type of building, surface area, and population size. The OT employs artificial intelligence (AI) algorithms and machine learning (ML) techniques to generate forecast optimized scenarios for the sizing of photovoltaic systems, thermal, and electrical storage, and the estimation of CO2 emission reductions. The OT features a user-friendly interface, enabling even non-experts to obtain comprehensive configurations for RECs, aiming to accelerate the transition toward sustainable and efficient district energy systems, driving positive environmental impact and fostering a greener future for communities and cities.
Piras et al. (Fri,) studied this question.