Smart Solution for Energy Communities: Integrating Demand Response and Unsupervised Learning Evaluation Metrics

The reduction of CO2 emissions is a critical imperative in the pursuit of sustainable energy solutions. A viable avenue for mitigating CO2 emissions within the power sector is adopting energy communities, which empowers communities to achieve decentralization and sustainability. Furthermore, the convergence of energy communities and machine learning algorithms represents a crucial boundary in developing contemporary energy systems. In this context, this paper implements a demand response-based model to balance the energy community’s consumption and generation, considering the support of unsupervised learning algorithms and the active participation of the members in the planned demand response events. The planning of a demand response event is based on a ranking obtained by a set of unsupervised learning evaluation metrics that evaluate the members’ data. This work’s novelty consists of analyzing the impact of these metrics on the ranking of members and, consequently, on the demand response event’s planning. For that, different combinations of these metrics will be considered during the members’ ranking. The reliability of this approach was assessed using an energy community consisting of 50 buildings. The results show that an energy community could reduce 1.1 kg of CO2 emissions and increase sustainability by 13% through implementing the presented model.