The penetration of nonprogrammable renewable energy sources, namely wind and solar technology, has greatly increased in the last decades and nowadays the shift toward green energy sources represents a priority worldwide. The high variability of the primary source challenges the grid operators in ensuring the stability and reliability of the electric grid. Machine learning algorithms, and in particular artificial neural networks, are one of the most reliable methods for photovoltaic (PV) energy production forecast. This article proposes a new ensemble method based on the probabilistic distribution of the trials, the probabilistic ensemble method (PEM). The proposed method has been tested on a three-years real case study, where the available days have been clustered according to the solar irradiation forecast. The days where the worst performance, in terms of nRMSE, was recorded mostly belonged to the totally cloudy days class, that has been therefore selected for the analysis. The PEM has been compared with the ensemble based on the mean value, achieving an improvement in the nRMSE metric up to 4.79% in 2017 in the totally cloudy days class.

A New Probabilistic Ensemble Method for an Enhanced Day-Ahead PV Power Forecast

Pretto S.;Ogliari E.;Niccolai A.;Nespoli A.
2022

Abstract

The penetration of nonprogrammable renewable energy sources, namely wind and solar technology, has greatly increased in the last decades and nowadays the shift toward green energy sources represents a priority worldwide. The high variability of the primary source challenges the grid operators in ensuring the stability and reliability of the electric grid. Machine learning algorithms, and in particular artificial neural networks, are one of the most reliable methods for photovoltaic (PV) energy production forecast. This article proposes a new ensemble method based on the probabilistic distribution of the trials, the probabilistic ensemble method (PEM). The proposed method has been tested on a three-years real case study, where the available days have been clustered according to the solar irradiation forecast. The days where the worst performance, in terms of nRMSE, was recorded mostly belonged to the totally cloudy days class, that has been therefore selected for the analysis. The PEM has been compared with the ensemble based on the mean value, achieving an improvement in the nRMSE metric up to 4.79% in 2017 in the totally cloudy days class.
Artificial neural network (ANN)
Clouds
days clustering
energy forecast
ensemble
Forecasting
Gaussian distribution
normality analysis
photovoltaic (PV)
Photovoltaic systems
Probabilistic logic
Production
Radiation effects
renewable energy sources (RES)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1201654
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