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Appl. Sci. 2017, 7(11), 1142;

Simulation of Wind-Battery Microgrid Based on Short-Term Wind Power Forecasting

ERA Chair (*Holder) ‘Net-Zero Energy Efficiency on City Districts, NZED’ Unit, Research Institute for Energy, University of Mons, Rue de l’Epargne, 56, 7000 Mons, Belgium
Department of Industrial Technologies, University of Deusto, Avda de las Universidades 24, 48007 Bilbao, Spain
Wireless Network Systems Division, INTRACOM Telecom S.A., 19.7 km Markopoulo Ave., Peania, 19002 Athens, Greece
Author to whom correspondence should be addressed.
Received: 13 October 2017 / Revised: 26 October 2017 / Accepted: 30 October 2017 / Published: 6 November 2017
(This article belongs to the Special Issue Applications of Artificial Neural Networks for Energy Systems)
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The inherently intermittent and highly variable nature of wind necessitates the use of wind power forecasting tools in order to facilitate the integration of wind turbines in microgrids, among others. In this direction, the present paper describes the development of a short-term wind power forecasting model based on artificial neural network (ANN) clustering, which uses statistical feature parameters in the input vector, as well as an enhanced version of this approach that adjusts the ANN output with the probability of lower misclassification (PLM) method. Moreover, it employs the Monte Carlo simulation to represent the stochastic variation of wind power production and assess the impact of energy management decisions in a residential wind-battery microgrid using the proposed wind power forecasting models. The results indicate that there are significant benefits for the microgrid when compared to the naïve approach that is used for benchmarking purposes, while the PLM adjustment method provides further improvements in terms of forecasting accuracy. View Full-Text
Keywords: artificial neural network; energy management; microgrid; Monte Carlo simulation; wind power forecasting artificial neural network; energy management; microgrid; Monte Carlo simulation; wind power forecasting

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Genikomsakis, K.N.; Lopez, S.; Dallas, P.I.; Ioakimidis, C.S. Simulation of Wind-Battery Microgrid Based on Short-Term Wind Power Forecasting. Appl. Sci. 2017, 7, 1142.

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