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.

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The inclusion of conductor temperature variations for numerous power system planning and operational studies has long been recognized in the literature. The conductor temperature is majorly affected by environmental factors such as the ambient temperature. An efficient forecasting technique for forecasting ambient temperature is the need of the hour to prevent unexpected hazards in power systems and other areas caused due to temperature variations. Numerous researches have proposed different point forecasting and probabilistic forecasting models for the forecasting of ambient temperature. The probabilistic forecasting of ambient temperature provides complete information about future uncertainties, therefore quantifying the effects of daily temperature variations. A forecast combination approach, such as the quantile regression averaging, has never been utilized in this context. The selection of suitable point forecasters that complement each other's effects by characterizing different aspects of the ambient temperature data for averaging and the use of frequency components that explain the daily and periodic seasonal variations of ambient temperature to construct a forecasting model are the significant features of this paper. The proposed model is used with four varieties, and each is compared with the others. It is found that the variant using all the complementary individual point forecasters performs better in making probabilistic forecasts than the other options as well as better than the popular quantile k-nearest neighbors, quantile egression forests, and basic quantile regression as inferred from the quantile score, Winkler score, and reliability plots.