A Prosumer Model Based on Smart Home Energy Management and Forecasting Techniques

Nikolaos Koltsaklis, Ioannis P. Panapakidis, David Pozo and Georgios C. Christoforidis
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Nikolaos Koltsaklis: Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece
Ioannis P. Panapakidis: Department of Electrical and Computer Engineering, University of Thessaly, 38221 Volos, Greece
David Pozo: Center for Energy Science and Technology, Skolkovo Institute of Science and Technology (Skoltech), 121205 Moscow, Russia
Georgios C. Christoforidis: Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece

Energies, 2021, vol. 14, issue 6, 1-32

Abstract: This work presents an optimization framework based on mixed-integer programming techniques for a smart home’s optimal energy management. In particular, through a cost-minimization objective function, the developed approach determines the optimal day-ahead energy scheduling of all load types that can be either inelastic or can take part in demand response programs and the charging/discharging programs of an electric vehicle and energy storage. The underlying energy system can also interact with the power grid, exchanging electricity through sales and purchases. The smart home’s energy system also incorporates renewable energy sources in the form of wind and solar power, which generate electrical energy that can be either directly consumed for the home’s requirements, directed to the batteries for charging needs (storage, electric vehicles), or sold back to the power grid for acquiring revenues. Three short-term forecasting processes are implemented for real-time prices, photovoltaics, and wind generation. The forecasting model is built on the hybrid combination of the K-medoids algorithm and Elman neural network. K-medoids performs clustering of the training set and is used for input selection. The forecasting is held via the neural network. The results indicate that different renewables’ availability highly influences the optimal demand allocation, renewables-based energy allocation, and the charging–discharging cycle of the energy storage and electric vehicle.

Keywords: demand response; forecasting; optimization; prosumer; smart home (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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