A methodology based on Artificial Neural Networks (ANN) and an Analog Ensemble (AnEn) is presented to generate 72-hour deterministic and probabilistic forecasts of power generated by photovoltaic (PV) power plants using input from a numerical weather prediction model and computed astronomical variables. ANN and AnEn are used individually and in combination to generate forecasts for three solar power plants located in Italy. The computational scalability of the proposed solution is tested using synthetic data simulating 4450 PV power stations. The NCAR Yellowstone supercomputer is employed to test the parallel implementation of the proposed solution, ranging from 1 node (32 cores) to 4450 nodes (141,140 cores). Results show that a combined AnEn + ANN solution yields best results, and that the proposed solution is well suited for massive scale computation.
Cervone, Guido & Clemente-Harding, Laura & Alessandrini, Stefano & Delle Monache, Luca. (2017). Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble. Renewable Energy. 108. 10.1016/j.renene.2017.02.052.This material is based upon work supported by the National Science Foundation under Grant No. 1639707. Opinions, findings, conclusions or recommendations expressed are those of the authors and do not reflect the views of the NSF.