Resumen:
Recommender systems are crucial in optimizing building energy consumption by offering personalized advice based on data analytics and user preferences. However, the need for explainable systems is emphasized to justify recommendations, as many algorithms rely on opaque machine-learning techniques. This explainability is particularly important to raise awareness about smart grids and flexibility among end users, as highlighted in surveys. This research introduces two distinct indicator types with a threefold purpose: firstly, to identify flexible consumption behavior patterns through transparent and straightforward methods suitable for remote decision support (recommender) systems, thereby negating the necessity for extensive databases; secondly, to evaluate the feasibility of installing solar panels on building facades, roofs, and structures based on high resolution 3D models; and thirdly, facilitate a better understanding of the feasibility and suitability of integrating renewable energy sources, particularly photovoltaic systems. Flexible prosumers are scored by assessing their energy consumption level, consistency, and variability. Topology indicators are based on 3D models that evaluate angle, orientation and feasible exposures. This study has been confronted using actual consumption profiles and similar households’ buildings, showing how the indicators proposed help identify flexible-consumption users and the best solar locations to enhance the energy transition.
Resumen divulgativo:
Esta investigación introduce dos indicadores para identificar patrones de consumo flexible, evaluar la viabilidad de instalar paneles solares y facilitar una mejor comprensión de la integración de fuentes de energía renovables. Los indicadores utilizan métodos transparentes y modelos 3D de alta resolución.
Palabras clave: Indicators, Demand Side Management, Solar Energy, Decision support systems.
Fecha de Registro: 27/10/2023
IIT-23-411WP
