Open AccessDay‐ahead and intra‐day wind power forecasting based on feedback error correction
Author(s) -
Gupta Akshita,
Kumar Arun,
Boopathi Kadhirvel
Publication year - 2021
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/rpg2.12211
Subject(s) - autoregressive integrated moving average , wind power forecasting , wavelet , wind power , autoregressive model , computer science , probabilistic forecasting , grid , wind speed , electric power system , moving average , power (physics) , time series , mathematical optimization , econometrics , artificial intelligence , mathematics , meteorology , machine learning , engineering , geography , physics , geometry , quantum mechanics , probabilistic logic , computer vision , electrical engineering
The major hindrance in the development of large‐scale grid integration of wind energy into the power system is the production of intermittent and variable power. A largescale integration requires a forecasting mechanism to support the power system operators while operating the grids. This study forecasts the day‐ahead and intra‐day wind power using the wavelet decomposition, followed by the autoregressive integrated moving average. The forecasting has been attempted on two datasets, actual and error wind power. The forecasting using the wavelet decomposition employs the discrete wavelets consisting of 16 wavelets, grouped into 5 families. The optimum length of the past data used for the model formulation has also been examined for the various scenarios. The forecasting results have been compared based on various performance metrics. The results of the forecasted values have been compared with the reference ARIMA model to see the effectiveness of the proposed wavelet‐ARIMA model. The results indicate that the feedback mechanism used in the error dataset have improved the forecasting efficiency over the use of actual data in both the day‐ahead and intra‐day forecasting. Also, it has been observed that some of the wavelet families outperformed the other families in terms of accuracy and speed.