Hybrid Approach in Thermal Demand Forecasting of a Building

Energy consumption plays a significant role in contributing to the greenhouse effect, environmental degradation, and pollution. Developing energy-efficient buildings presents a promising approach to reduce dependency on non-renewable energy sources. However, transitioning to renewable energy sources poses unique challenges, as their utilization is highly influenced by weather conditions. Consequently, the implementation of demand forecasters becomes crucial in facilitating the integration of renewable energy sources. This paper aims to address the gap in research by investigating the modelling of demand forecasters in the absence of an implemented control strategy. The study focuses on a specific building located in Italy, providing a comprehensive analysis of the effectiveness and performance of the developed demand forecasting model. Through rigorous examination and analysis, the findings of this study demonstrate that the modelled demand forecaster achieved considerable results in accurately predicting energy demand. The implications of these findings suggest that the utilization of demand forecasters, even in the absence of an implemented control strategy, can contribute significantly to enhancing energy efficiency and promoting the integration of renewable energy sources. This research serves as a foundation for future studies aiming to optimize energy management strategies and advance sustainable practices in the built environment.