A Very Short Term Photovoltaic Power Forecasting Model by Deep Learning and the LDA Method Using Weather Multivariate Time Series Inputs

Photovoltaic (PV) system-generated solar energy has inconsistent and variable properties, which makes controlling electric power distribution and preserving grid stability extremely difficult. A photovoltaic (PV) system's performance is profoundly affected by the amount of sunlight that reaches the solar cell, the season of the year, the ambient temperature, and the humidity of the air. Every renewable energy technology, sadly, has its problems. As a result, the system is unable to function at its highest or best level. To combat the unstable and intermittent performance of solar power output, it is essential to achieve a precise PV system output power. This work introduces a new approach to enhancing accuracy and expanding the time range of very-short-term solar energy forecasting (15 min step ahead) by using multivariate time series inputs in deferent seasons. First, Linear Discriminat Analysis (LDA) is used to select the relevant factors from the mixed meteorological input data. Secondly, two very short-term deep learning prediction models, CNN and LSTM, are used to predict PV power for a shuffled and reduced database of weather inputs. Finally, the predicted output from the two models are combined using classification strategy. The proposed method is applied to one year of real data collected from a solar power plant located in southern Algeria, to demonstrate that this technique can improve the forecasting accuracy compared to other techniques, as determined by statistical analysis involving normalized root mean square error (NRMSE), mean absolute percentage error (MAPE), mean bias error (MBE), and coefficient of determination (R²).