Evaluation of Machine Learning Methods on Large-Scale Spatio-temporal Data for Photovoltaic Power Prediction

Sauter, Evan

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Evaluation of Machine Learning Methods on Large-Scale Spatio-temporal Data for Photovoltaic Power Prediction

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The exponential increase in photovoltaic (PV) arrays installed globally, particularly given the intermittent nature of PV generation, has emphasized the need to accurately forecast the predicted output power of the arrays. Regardless of the length of the forecasts, the modeling of PV arrays is made difficult from their dependence on weather. Typically, the model projections are generated from datasets at one location across a couple of years. The purpose of this study was to compare the effectiveness of regression models in very short-term deterministic forecasts for spatio-temporal projections. The compiled dataset is unique given it consists of weather and output power data of PVs located at five cities spanning three and six years in length. Grated recurrent unit (GRU) generalized the best for same-city and cross-city predictions, while long short-term memory (LSTM) and ensemble bagging had the best cross-city and same-city predictions, respectively. The code and data are available at https://github.com/Zhang-VISLab?tab=repositories.

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