Open AccessForecasting Error Modelling for Improving PV Generation Prediction
Author(s) -
Happy Aprillia,
Hong-Tzer Yang
Publication year - 2019
Publication title -
specta
Language(s) - English
Resource type - Journals
eISSN - 2622-9099
pISSN - 2549-2713
DOI - 10.35718/specta.v2i1.92
Subject(s) - mean absolute percentage error , photovoltaic system , estimator , artificial neural network , computer science , approximation error , kernel density estimation , electric power system , statistics , power (physics) , mathematics , algorithm , engineering , machine learning , physics , quantum mechanics , electrical engineering
Accurate forecasting of Photovoltaic (PV) generation output is important in operation of high PV-penetrated power systems. In this paper, an adaptive uncertainty modelling method for forecasting error is proposed to improve the prediction accuracy of PV generation. The proposed method models the uncertainty in forecast data using Kernel Density Estimator and guarantee the provision of accurate expected value. Neural Network model is then constructed by the developed uncertainty model to forecast the PV output. The actual confidence level is traced within the day and injected as an input to the Neural Network model by observing the Mean Absolute Prediction Error (MAPE) and Unscaled Mean Bounded Relative Absolute Error (UMBRAE). The proposed method is tested with various significant changes of weather condition and proved to have promising performance on PV generation forecasting. Thus, the developed adaptive uncertainty model can be further used in power system planning that have high-penetration energy sources with stochastic behavior.