Solar Forecasting Using Remote Solar Monitoring Stations and Artificial Neural Networks

Abstract

The need to accurately forecast available solar irradiance is a significant issue for the power industry and poses special challenges for utilities who serve customers in isolated regions where weather forecast data may not be abundant. This paper proposes a method to forecast two hour ahead solar irradiance levels at a site in Northwestern Alberta, Canada using real-time solar irradiance measured both locally and at remote monitoring stations. This paper makes use of an Artificial Neural Network (ANN) to forecast the solar irradiance levels and uses the genetic algorithm to determine the optimal array size and positioning of solar monitoring stations to obtain the most accurate forecast from the ANN. The findings of this paper are that it is possible to use as few as five remote monitoring stations to obtain a near-peak forecasting accuracy from the algorithm and that providing adequate geospatial separation of the remote monitoring sites around the target site is more desirable than clustering the sites in the strictly upwind directions.