Contextual learning for energy forecasting in buildings

Abstract

Energy consumers are becoming active players in the power and energy system. However, their informed and real-time responsiveness to the variations of renewable-based generation and, consequently, energy prices, is not possible without decision support solutions. This paper proposes a novel contextual learning approach for energy forecasting, which supports the decisions of Building Energy Management Systems (BEMS). The proposed forecasting approach includes a contextual dimension that identifies different observed contexts and clusters them according to their similarity. The identification of such contexts is used by the learning process of state-of-the-art artificial intelligence-based forecasting methods to select and adapt the most relevant data that is used in the training phase in each context. Forecasts for energy consumption, generation, temperature, brightness and occupancy are used by the BEMS to provide recommendations to the consumers and to support automated control of building devices. Real consumption, generation and contextual data gathered from several sensors in a building are used to validate the results, which show that the proposed contextual learning model improves forecasts of energy consumption, generation and other relevant factors for energy management in buildings.