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Energies 2018, 11(6), 1487; https://doi.org/10.3390/en11061487

Computational Intelligence Techniques Applied to the Day Ahead PV Output Power Forecast: PHANN, SNO and Mixed

Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy
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Received: 29 April 2018 / Revised: 1 June 2018 / Accepted: 4 June 2018 / Published: 7 June 2018
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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Abstract

An accurate forecast of the exploitable energy from Renewable Energy Sources is extremely important for the stability issues of the electric grid and the reliability of the bidding markets. This paper presents a comparison among different forecasting methods of the photovoltaic output power introducing a new method that mixes some peculiarities of the others: the Physical Hybrid Artificial Neural Network and the five parameters model estimated by the Social Network Optimization. In particular, the day-ahead forecasts evaluated against real data measured for two years in an existing photovoltaic plant located in Milan, Italy, are compared by means both new and the most common error indicators. Results reported in this work show the best forecasting capability of the new “mixed method” which scored the best forecast skill and Enveloped Mean Absolute Error on a yearly basis (47% and 24.67%, respectively). View Full-Text
Keywords: solar power; computational intelligence; day-ahead forecast; Artificial Neural Network; five parameters model; Social Network Optimization solar power; computational intelligence; day-ahead forecast; Artificial Neural Network; five parameters model; Social Network Optimization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Ogliari, E.; Niccolai, A.; Leva, S.; Zich, R.E. Computational Intelligence Techniques Applied to the Day Ahead PV Output Power Forecast: PHANN, SNO and Mixed. Energies 2018, 11, 1487.

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