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Among the sectors with the highest energy consumption are transport, industries, and buildings.Buildings are responsible for the third part of energy consumption and almost 40% of CO2 emissions worldwide. The search to improve the comfort of the occupants inside the buildings has brought a consequence that buildings are increasingly equipped with devices that help to improve the thermal comfort, visual comfort, and air quality inside the buildings, causing more energy demand regardless of the type of building making buildings an untapped efficiency potential. This doctoral thesis presents a model for forecasting electricity demand in buildings based on machine learning for load-shifting strategies, which can be implemented in building energy management systems. The forecast model developed allows forecasting the electricity demand for the next 24 hours starting at any time of the day. To achieve this forecast, not only electricity consumption was considered, but also climatic variables, calendar variables, and past time values. Different types of machine learning algorithms were compared using performance measures resulting in tree decision and deep learning algorithms performing better with the developed model.

El gravamen de la energía eléctrica en España se caracteriza por una alta dispersión normativa y de instrumentos tributarios que son consecuencia del juego de competencias y poderes del Estado autonómico, así como por una generalizada falta de coherencia de estos instrumentos fiscales con los objetivos medioambientales que proclaman perseguir. Partiendo del déficit de tarifa que lleva arrastrando el sistema eléctrico español durante décadas y que explica el afán recaudatorio de muchas de estas figuras, el presente artículo revisa la configuración de los principales tributos que recaen sobre la energía eléctrica a nivel estatal y autonómico, terminando con una perspectiva a futuro con base en una posible Unión de la Energía y una necesaria reforma fiscal verde en España.