Day-ahead Forecasting of Solar Power Output from Photovoltaic Systems Utilising Gradient Boosting Machines
Source2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC 34th EU PVSEC)
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Accurate day-ahead photovoltaic (PV) power output forecasting techniques are important both for grid and plant operators. In this work, a machine learning model was implemented based on gradient boosting machine (GBM), for accurate PV production forecasting. The accuracy of the developed model was experimentally verified on a test system installed in Cyprus. The basic methodology followed was to train and optimize different developed GBM PV production day-ahead forecasting models with acquired data-sets and construct relationships between the input and output features. The final optimal developed GBM model included 7 inputs, 1000 trees with 10 minimum observations on each node and a shrinkage level set to 0.001. The prediction results obtained when the test set was applied to the model, demonstrated that the nRMSE was 0.80 %, while some days were exhibiting accuracies close to 0.50 %. Finally, the forecasting performance assessment results obtained when the test set and numerical weather prediction (NWP) data were applied to the optimal designed model, showed a nRMSE of 7.9 % with 55 % of the test set days exhibiting nRMSE below 5 %. The error relative to the capacity of the system for all points during clear sky conditions was in most cases less than 0.1 W/Wp.