Yeganeh Salar Babakhani
Automatic Selection of Predictive Models for Building Energy Demand.
Rel. Alfonso Capozzoli. Politecnico di Torino, Corso di laurea magistrale in Georesources And Geoenergy Engineering, 2025
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Abstract
Buildings account for more than one-third of total energy demand in many countries worldwide, positioning them as one of the major contributors to greenhouse gas emissions. Effective prediction of building energy demand is essential for optimizing electricity consumption and mitigating power shortages or waste. The importance of this task is increasing due to two significant transitions: the electrification of heating and transportation and the growing dependence on renewable energy sources. The former refers to the widespread adoption of electric vehicles and electric heating systems, such as heat pumps. The latter involves the expansion of renewable energy generation, which introduces challenges in balancing supply and demand, as electricity from sources like wind and solar is inherently intermittent and not always available when consumption peaks.
The principal objective of this project, building further on previous work at KU Leuven University, Belgium, was to develop machine learning models to create building energy demand forecasts for the future (day-ahead time scale)
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