The purpose of the present study is to compare the efficiency of test statistics in testing the differences between two dependent datasets applying a regression framework with centered independent variable values. Comparisons of the efficiency of the test statistics, namely the t RCM b * 0 , the Wilcoxon matched-pairs signed-ranks test, and the Paired sample t-test, are made with the correlation coefficients, the sample sizes, and the ratio of mean different between the two datasets being varied. Simulations of the test statistics apply a Monte Carlo technique and are repeated 1,000 times. The research results show that the efficiency in controlling Type I errors of the Wilcoxon matched-pairs signed-ranks test and the Paired t-test is high under all scenarios, while that of the t RCM b * 0 is high only in case the r xy is not excessively high, that is, under 0.80. In contrast, the power of the test of the latter is significantly higher than the former under all scenarios.

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Accurate long-term and midterm electricity load forecasting play an essential role in electric power system planning. Drawing on the seasonal-trend forecasting capacity of Fourier series and LOESS transformation, this paper applies modified Fourier series transformation (MFST) and modified seasonal-trend decomposition using LOESS transformation (MSTLT) to electricity load forecasting and compares the performance of two alternative models: the ARIMA(p,d,q) SARIMA(P,D,Q) model and the support vector regression (SVR) model. The data comprise monthly electricity consumption volumes between 2002 and 2019. The data between 2002 and 2018 are utilized to construct the forecasting model, while those in 2019 are employed to test the accuracy of the predicted values. The results confirm the validity of the proposed model in terms of forecasting accuracy and interpretability.