Use of satellite data to improve solar radiation forecasting with Bayesian Artificial Neural Networks

Abstract

Solar forecasting has become an important issue for power systems planning and operating, especially in islands grids. Power generation and grid utilities need day ahead, intra-day and intra-hour Global Horizontal solar Irradiance (GHI) forecasts for operations. In this paper, we focus on intra-day solar forecasting with forecast horizons ranging from 1 h to 6 h ahead. An Artificial Neural Networks (ANN) model is proposed to forecast GHI using ground measurement data and satellite data (from Helioclim-3) as inputs. In order to compare the forecasting results obtained by the proposed ANN model, we also include in this work a simple naïve model, based on the persistence of the clear sky index (smart persistence model), as well as another reference model, the climatological mean model. The models were trained and tested for two ground measurements stations in Gran Canaria Island, Pozo (south) and Las Palmas (in the north). Firstly, ANN was trained and tested only with past ground measurement irradiance and compared by means of relative metrics with naïve models. While this first step led to better performances, forecasting skills were improved by including exogenous inputs to the model by using GHI satellite data from surrounding area.