An improved sliding windowprediction-basedoutlier detection and correction for volatiletime-series

Steady-state forecasting is indispensable for power system planning and operation. A forecasting model for inputs considering their historical record is a preliminary step for such type of studies. Since the historical data quality is decisive in edifice an accurate forecasting model, data preprocessing is essential. Primarily, the quality of raw data is affected by the presence of outliers, and preprocessing refers to outlier detection and correction. In this paper, an effort is made to improve the existing sliding window prediction-based preprocessing method. The recommended reforms are the calculation of appropriate window width and a new outlier correction approach. The proposed method denoted as improved sliding window prediction-based preprocessing is applied to the historical data of PV generation, load power, and the ambient temperature of different time-steps collected from various places in the United States and India. Firstly, the method's efficacy through detailed result analysis demonstrating the proposed preprocessing as a better way than its precursor andk-nearest neighbor approach is presented. Later, the improved out-of-sample forecasting accuracy canonizes the proposed method's concert compared to both the above techniques and the case without preprocessing.

Automated summary

The paper discusses the importance of steady-state forecasting for power systems and the necessity of data preprocessing, proposing improvements to handle outliers in historical data. Empirical results show that the proposed method outperforms existing techniques and k-nearest neighbor approach.