Long Term Forecasting of Solar Power Using Artificial Neural Network

The rapid growth of solar Photovoltaic (PV) technology has been very visible over the past decade. Such increase in the integration of solar generation has brought attention to the forecasting issues. This paper presents a new approach to tackle the long-term forecasting challenge and accordingly reduce the uncertainty of the PV forecast, which would accordingly help facilitate its integration into the electric power grid. This paper presents a solar power forecasting using artificial neural networks (ANNs). The neural network structures, namely, feed forward back propagation (FFBP), have been used to forecast a photovoltaic panel output power and approximate the generated power. The neural networks have four inputs and one output. The inputs are solar radiation, ambient temperature, humidity and wind speed; the output is the Solar power. The data used in this paper started from January 1,2013 ,until December 31,2017. The five years of data were split into two parts: 2006–2008 and 2009 2010; the first part was used for training and the second part was used for testing the neural networks. A mathematical equation is used to estimate the generated power.