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

Emanuele Ogliari, Alessandro Niccolai, Sonia Leva and Riccardo E. Zich
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Emanuele Ogliari: Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy
Alessandro Niccolai: Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy
Sonia Leva: Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy
Riccardo E. Zich: Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy

Energies, 2018, vol. 11, issue 6, 1-16

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).

Keywords: solar power; computational intelligence; day-ahead forecast; Artificial Neural Network; five parameters model; Social Network Optimization (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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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