A study on forecasting electricity production and consumption in smart cities and factories |
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| Affiliation: | 1. Computer Science and Electrical Engineering Department, Lucian Blaga University of Sibiu, Romania;2. Department of Management Studies and Quantitative Methods, Parthenope University, Italy;3. Department of Informatics, University of Salerno, Italy;1. Department of Energy Conversion and Storage, Technical University of Denmark (DTU), Frederiksborgvej 399, Roskilde, Denmark;2. Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Zagreb, Croatia;3. Environmental Economics and Natural Resources Group, Wageningen University & Research, Hollandseweg 1, 6706, KN, Wageningen, The Netherlands;4. Department of Management Engineering, Technical University of Denmark (DTU), Produktionstorvet 426, Lyngby, Denmark;1. Faculty of Information Technology, University of Jyväskylä, Finland;2. Institute of Information Systems and Marketing, Karlsruhe Institute of Technology, Germany;3. Gamification Group, Faculty of Information Technology and Communication Sciences, Tampere University, Finland;4. Gamification Group, Faculty of Humanities, University of Turku, Finland;1. Department of Economy and Business Organization, Universitat Internacional de Catalunya, Barcelona, Spain;2. Department of Business Administration and Product Design, Universitat de Girona, Spain;3. Universidad Autónoma de Madrid, Facultad de Ciencias Económicas y Empresariales, Spain;1. Research Institute for Shenzhen, University of International Business and Economics, China;2. Anderson School of Management, University of New Mexico, USA;3. McLane College of Business, University of Mary Hardin-Baylor, USA |
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| Abstract: | The electrical power sector must undergo a thorough metamorphosis to achieve the ambitious targets in greenhouse gas reduction set forth in the Paris Agreement of 2015. Reducing uncertainty about demand and, in case of renewable electricity generation, supply is important for the determination of spot electricity prices. In this work we propose and evaluate a context-based technique to anticipate the electricity production and consumption in buildings. We focus on a household with photovoltaics and energy storage system. We analyze the efficiency of Markov chains, stride predictors and also their combination into a hybrid predictor in modelling the evolution of electricity production and consumption. All these methods anticipate electric power based on previous values. The main goal is to determine the best method and its optimal configuration which can be integrated into a (possibly hardware-based) intelligent energy management system. The role of such a system is to adjust and synchronize through prediction the electricity consumption and production in order to increase self-consumption, reducing thus the pressure over the power grid. The experiments performed on datasets collected from a real system show that the best evaluated predictor is the Markov chain configured with an electric power history of 100 values, a context of one electric power value and the interval size of 1. |
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| Keywords: | Electricity prediction Markov chains Photovoltaics Energy storage Energy management system |
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