Forecasting the PV Power Utilizing a Combined Convolutional Neural Network and Long Short-Term Memory Model

Abstract

The quantity of photovoltaic systems generated relies on the climate, and to generate significant variations depending on the climatic conditions. Its electricity production processes exhibit unpredictability, instability, and intermittent behavior. The augmented convolutional long-short memory combination network is recommended for PV energy combined forecasting in accordance to overcome the challenge of considerable instability in the high proportion of sustainable energy output. The LSTM and CNN models’ training procedure uses the enhanced artificial bee colony (ABC) to adjust their characteristics. To determine the projected model parameters of PV power, the estimated input parameters of every model are stacked and then reassembled. The studies showed that the forecast precision was significantly higher than the reliability rate of the Convolutional LSTM combinational network as well as the forecasting findings from the BP, CNN, and LSTM independent computational methods. This forecast specificity was predicated on the enhanced ABC to optimize the variables of the CLSTM combinational network for forecasting Photo Voltaic power. For comparative study and assessment, the most recent operational data from a Photovoltaic power plant in northern China have been utilized. The suggested technique resolved more quickly and was higher weather-adaptable than the individual neural network and hybrid forecasting models mentioned prior.