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Appl. Sci. 2018, 8(3), 408;

Model Predictive Control Home Energy Management and Optimization Strategy with Demand Response

Centre for Mechanical and Aerospace Science and Technologies (C-MAST), University of Beira Interior, 6201-001 Covilhã, Portugal
Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland
The Research Unit on Governance, Competitiveness and Public Policies (GOVCOPP), University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
Institute for Systems and Computer Engineering, Technology and Science (INESC TEC) and the Faculty of Engineering of the University of Porto, 4200-465 Porto, Portugal
Instituto de Engenharia de Sistemas e Computadores-Investigação e Desenvolvimento (INESC-ID), Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal
Authors to whom correspondence should be addressed.
Received: 9 February 2018 / Revised: 23 February 2018 / Accepted: 27 February 2018 / Published: 9 March 2018
(This article belongs to the Special Issue Smart Home and Energy Management Systems)
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The growing demand for electricity is a challenge for the electricity sector as it not only involves the search for new sources of energy, but also the increase of generation capacity of the existing electrical infrastructure and the need to upgrade the existing grid. Therefore, new ways to reduce the consumption of energy are necessary to be implemented. When comparing an average house with an energy efficient house, it is possible to reduce annual energy bills up to 40%. Homeowners and tenants should consider developing an energy conservation plan in their homes. This is both an ecological and economically rational action. With this goal in mind, the need for the energy optimization arises. However, this has to be made by ensuring a fair level of comfort in the household, which in turn spawns a few control challenges. In this paper, the ON/OFF, proportional-integral-derivative (PID) and Model Predictive Control (MPC) control methods of an air conditioning (AC) of a room are compared. The model of the house of this study has a PV domestic generation. The recorded climacteric data for this case study are for Évora, a pilot Portuguese city in an ongoing demand response (DR) project. Six Time-of-Use (ToU) electricity rates are studied and compared during a whole week of summer, typically with very high temperatures for this period of the year. The overall weekly expense of each studied tariff option is compared for every control method and in the end the optimal solution is reached. View Full-Text
Keywords: energy optimization; model predictive control; home energy systems; photovoltaic microgeneration; demand response energy optimization; model predictive control; home energy systems; photovoltaic microgeneration; demand response

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Godina, R.; Rodrigues, E.M.G.; Pouresmaeil, E.; Matias, J.C.O.; Catalão, J.P.S. Model Predictive Control Home Energy Management and Optimization Strategy with Demand Response. Appl. Sci. 2018, 8, 408.

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