A case study of NeuralProphet and nonlinear evaluation for high accuracy prediction in short-term forecasting in PV solar plant

Abstract

Prediction of the energy, active production from the PV solar plant is a challenge in cloudy weather or with clouds over the solar plant; therefore, it has impact in the planning of the power system, especially in the season analysis and prediction accuracy adjustments, for example in holidays. In 2022, some authors published some analysis associated to horizontal pyranometers and the limits in the evaluation of the data, the Mean Bias Error of Daily Solar Irradiation average (MIEave) ranges from 0.17% to 2.86% associated a sudden change in the weather, it increases the “risk of misestimating the potential electricity generation” with short-term error of more than 50% and the Global Horizontal Irradiance (GHI) has a mean bias error (MBE) of at least ±8% [1]. In this research article, a novel proposal for short-term forecasting combines the satellite with meteorological station data and statistical model associated to the new seasonality analysis by using two approaches: i) NeuralProphet, Ridge regression, ii) Long Short-Term Memory with convolutional neural networks. Besides, it requires three KPI as feedback, it is the mean absolute error (MAE), relative Root mean square error (RMSE), and mean absolute percentage error (MAPE). The results demonstrate a MAPE of 5.93% and a computational time 852.10 s and the comparison with new predictions methods from 2019 to 2021. This research article illustrates the new approach with the forecasting method in a case of the PV solar plant in Peru and proves the robustness and seasonality results, and new short-terms improvements associated to external influence as cloudy conditions and resource availability. Our findings are an improvement of the model MAPE 12.14%–5.93%; even compared with the literature and currently models as ARIMA-LSTM with 10.57%, LSTM with NN and G, SARIMA and SVM considering Gaussian White Noise with 8.14% and Prophet with SVM with 8.81%.