Photovoltaic Power Forecasting Model Based on Nonlinear System Identification

Solar photovoltaic (PV) energy sources are rapidly gaining potential growth and popularity compared with conventional fossil fuel sources. As the merging of the PV systems with existing power sources increases, reliable and accurate PV system identification is essential to address the highly nonlinear change in the PV system dynamic and operational characteristics. This paper deals with the identification of a PV system characteristic in the real-life environment in Kuwait. The studied PV system is located on the top of the Ministry of Electricity and Water and the Ministry of Public Works buildings. The identification methodology is discussed. A Hammerstein-Wiener model is identified and selected due to its suitability to capture the PV system dynamics. Measured input-output data are collected from the PV system to be used for the identification process. The data are divided into estimation and validation sets. Results and discussions are provided to demonstrate the accuracy of the selected model structure.