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This paper has reviewed the field of Data Science and how Data Science techniques can be applied to building energy management. Specifically, we have focused on building operation, energy load prediction, and identification of consumption patterns. Our experiments show that Big Data technologies can solve the computational problems that appear when processing of large amounts of data, which are likely to have an increasing relevance with the advent of the Internet of Things –with smart meters and appliances fully connected to the Internet. However, the applications to real-world scenarios are still scarce. In our experience, one of the most important aspects to improve is achieving a greater involvement of the building managers in the data analysis process. To do this, future research work should explore two complementary directions, namely, showing the potential of Data Science to building managers, and developing more user-friendly algorithms and tools. In this way, we expect that new approaches will be less opaque, easier to use, more customizable, and above all other features, more engaging. ; Tesis Univ. Granada. ; Spanish Ministries of Science, Innovation and Universities (TIN2017-91223-EXP) ; Economy and Competitiveness (TIN2015-64776-C3-1-R) ; Energy IN TIME project from the EU 7o Framework Programme (grant agreement No. 608981) ; COPKIT project from the EU 8 Framework Programme (H2020) research ; Innovation programme (grant agreement No 786687) ; University of Granada (Programa de Proyectos para la incorporación de jóvenes doctores a nuevas líneas de investigación) ; Spanish Government (TIN2012-30939 project)

Despite the increasing capabilities of information technologies for data acquisition and processing, building energy management systems still require manual configuration and supervision to achieve optimal performance. Model predictive control (MPC) aims to leverage equipment control-particularly heating, ventilation, and air conditioning (HVAC)-by using a model of the building to capture its dynamic characteristics and to predict its response to alternative control scenarios. Usually, MPC approaches are based on simplified linear models, which support faster computation but also present some limitations regarding interpretability, solution diversification, and longer-term optimization. In this paper, we propose a novel MPC algorithm that uses a full-complexity grey-box simulation model to optimize HVAC operation in non-residential buildings. Our system generates hundreds of candidate operation plans, typically for the next day, and evaluates them in terms of consumption and comfort by means of a parallel simulator configured according to the expected building conditions (weather and occupancy). The system has been implemented and tested in an office building in Helsinki, both in a simulated environment and in the real building, yielding energy savings around 35% during the intermediate winter season and 20% in the whole winter season with respect to the current operation of the heating equipment. ; This work was supported in part by the Universidad de Granada under Grant P9-2014-ING, in part by the Spanish Ministry of Science, Innovation and Universities under Grant TIN2017-91223-EXP, in part by the Spanish Ministry of Economy and Competitiveness under Grant TIN2015-64776-C3-1-R, and in part by the European Union (Energy IN TIME EeB.NMP.2013-4), under Grant 608981.