An improved sliding window prediction‐based outlier detection and correction for volatile time‐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 and k‐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. [ABSTRACT FROM AUTHOR]