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 nov

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PROVIDER: S-EPMC9513787 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature