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Hybrid Modelling of PV Power Generation for Enhanced Forecasting
S. Theocharides, G. Makrides, M. Theristis, M. Kynigos, G.E. Georghiou
Performance, Photovoltaic (PV), Artificial Neural Network, Forecasting, Power
PV Systems and Storage – Modelling, Design, Operation and Performance
Subtopic: Solar Resource and Forecasting
Event: 37th European Photovoltaic Solar Energy Conference and Exhibition
Session: 5BO.7.5
1256 - 1260
ISBN: 3-936338-73-6
Paper DOI: 10.4229/EUPVSEC20202020-5BO.7.5
0,00 EUR
Document(s): paper, presentation


Accurate photovoltaic (PV) production forecasting is an important feature that can assist utilities and plant operators in the direction of energy management and dispatchability planning. Although numerous forecasting models have been reported in literature, the challenge of improved accuracy remains unsolved. In this work, a day-ahead PV power model utilising a hybrid approach is derived to feed into an Artificial Neural Network (ANN) and a linear regression model trained for PV power forecasting. The study focuses on improving the forecasting accuracy by employing machine learning and linear regression models that could record the behaviour of the PV system. The performance of the hybrid model was assessed against a single ANN model using a historical test set. The results showed that the hybrid model outperformed the single ANN model exhibiting a normalised mean square error (nRMSE) of 7.05%.