Survey of Wind Power Output Power Forecasting Technology

Abstract

Uncertainty and volatility of wind power generation, bring some serious challenges for the grid-connected wind power system. Prediction of wind power in advance is an important way to solve the above problems. Due to the existence of uncontrollable factors such as sensor transmission and network communication, the data collected for wind power prediction have abnormal values and missing values. Therefore, corresponding outlier detection and missing value interpolation operations should be performed before wind power prediction. To further promote the development of wind power data cleaning and prediction technology, current existing models and methods are analyzed and summarized, and the existing technologies are divided and compared. Starting from time series data, this paper first classifies, analyzes and summarizes the research status of outlier detection methods in the field of wind power prediction, summarizes the deficiencies and defects of existing anomaly detection methods, and prospects the research directions that may become the focus in the future development. Secondly, the evaluation indices of the existing missing value treatment methods are described. According to the different treatment methods, the processing techniques are analyzed and summarized according to the conventional treatment methods, discriminative interpolation methods, generative interpolation methods and physical characteristics methods, and the existing problems in the existing research are analyzed. Finally, the current research status of forecasting methods, multi-level forecasting and adaptive forecasting systems in existing research are analyzed and summarized, and the existing challenges and future development directions of existing forecasting are summarized and prospected.

Keywords

• |deep learning|wind power forecasting|outlier detection|missing value imputation|time series data