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).

Computational intelligence techniques applied to the day ahead PV output power forecast: PHANN, SNO and mixed

Ogliari, Emanuele;Niccolai, Alessandro;Leva, Sonia;Zich, Riccardo E.
2018

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).
Artificial Neural Network; Computational intelligence; Day-ahead forecast; Five parameters model; Social Network Optimization; Solar power; Renewable Energy, Sustainability and the Environment; Energy Engineering and Power Technology; Energy (miscellaneous); Control and Optimization; Electrical and Electronic Engineering
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1079635
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