Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble

AMS Citation:
Cervone, G., L. Clemente-Harding, S. Alessandrini, and L. Delle Monache, 2017: Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble. Renewable Energy, 108, 274-286, doi:10.1016/j.renene.2017.02.052.
Date:2017-08-01
Resource Type:article
Title:Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble
Abstract: A methodology based on Artificial Neural Networks (ANN) and an Analog Ensemble (AnEn) is presented to generate 72 h 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 National Center for Atmospheric Research (NCAR) Yellowstone supercomputer is employed to test the parallel implementation of the proposed solution, ranging from one 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. Published by Elsevier Ltd.
Peer Review:Refereed
Copyright Information:Copyright 2017 Elsevier.
OpenSky citable URL: ark:/85065/d75x2bv3
Publisher's Version: 10.1016/j.renene.2017.02.052
Author(s):
  • Guido Cervone - NCAR/UCAR
  • Laura Clemente-Harding
  • Stefano Alessandrini - NCAR/UCAR
  • Luca Delle Monache - NCAR/UCAR
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