Forecasting the PV Panel Power Based on Image Processing and Historical Outputs

Solar PhotoVoltaic (PV) installment is expanding quickly around the world, but deeper integration into the electrical grid is hampered by solar power intermittency. A portion of the short-term PV fluctuation is caused by abrupt weather changes, such as cloud cover variations, which can drastically impact the PV panel output on time scales of minutes. Images of the sky can be used to provide information on the present and upcoming cloud cover to increase the accuracy of the PV power forecast. Convolutional Neural Network (CNN) is used in this study so as to link the solar panels’ power with the current sky photos. In addition, the PV panel’s historical output is utilized to enhance forecasting accuracy. Also, the sensitivity of the proposed model to the configuration of the machine learning procedure, such as the number of neurons and the width of the network, has been assessed. Furthermore, the uncertainty of the incorporated stochastic approach and the effect of different arrangements of inputs and outputs on the performance metrics have been evaluated. The proposed model achieves the root mean square error ranges from 2.37 kW to 3.43 kW for the real-data dataset.