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Solar and wind forecasting by NARX neural networks
doi: 10.1051/rees/2016047
handle: 20.500.14243/304527
The nonlinear autoregressive network with exogenous input (NARX) is used to perform hourly solar irradiation and wind speed forecasting, according to a multi-step ahead approach. Temperature has been considered as the exogenous variable. The NARX topology selection is supported by a combined use of two techniques: 1. a genetic algorithm (GA)-based optimization technique and 2. a method that determines the optimal network architecture by pruning (Optimal Brain Surgeon (OBS) strategy). The considered variables are observed at hourly scale in a seven year dataset and the forecasting is done for several time horizons in the range from 8 to 24 hours-ahead.
- National Academies of Sciences, Engineering, and Medicine United States
- Institute of Intelligent Systems for Automation Italy
- National Research Council Italy
- National Research Council United States
Solar energy, NARX, wind energy, TJ807-830, forecasting, Energy conservation, neural networks, TJ163.26-163.5, Renewable energy sources
Solar energy, NARX, wind energy, TJ807-830, forecasting, Energy conservation, neural networks, TJ163.26-163.5, Renewable energy sources
