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Energies 2017, 10(11), 1701; doi:10.3390/en10111701

The Fuzzy Logic Method to Efficiently Optimize Electricity Consumption in Individual Housing

Research Group on Materials, Microelectronics, Acoustics, and Nanotechnology, University of Tours, 37000 Tours, France
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Received: 28 September 2017 / Revised: 12 October 2017 / Accepted: 24 October 2017 / Published: 25 October 2017
(This article belongs to the Special Issue Decentralised Energy Supply Systems)

Abstract

Electricity demand shifting and reduction still raise a huge interest for end-users at the household level, especially because of the ongoing design of a dynamic pricing approach. In particular, end-users must act as the starting point for decreasing their consumption during peak hours to prevent the need to extend the grid and thus save considerable costs. This article points out the relevance of a fuzzy logic algorithm to efficiently predict short term load consumption (STLC). This approach is the cornerstone of a new home energy management (HEM) algorithm which is able to optimize the cost of electricity consumption, while smoothing the peak demand. The fuzzy logic modeling involves a strong reliance on a complete database of real consumption data from many instrumented show houses. The proposed HEM algorithm enables any end-user to manage his electricity consumption with a high degree of flexibility and transparency, and “reshape” the load profile. For example, this can be mainly achieved using smart control of a storage system coupled with remote management of the electric appliances. The simulation results demonstrate that an accurate prediction of STLC gives the possibility of achieving optimal planning and operation of the HEM system. View Full-Text
Keywords: demand side management; electricity consumption prediction and management; fuzzy logic algorithm; individual housing demand side management; electricity consumption prediction and management; fuzzy logic algorithm; individual housing
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Bissey, S.; Jacques, S.; Le Bunetel, J.-C. The Fuzzy Logic Method to Efficiently Optimize Electricity Consumption in Individual Housing. Energies 2017, 10, 1701.

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