Monthly Electricity Consumption Forecasting Method Based on X12 and STL Decomposition Model in an Integrated Energy System

Table 1

Literature review summary.

Ref.	Forecasted variable(s)	Forecasting method(s)	Decomposition method(s)	Decomposition component(s)
[6]	Monthly load	ARIMA	X12	Trend, seasonal, and irregular components.
[7]	Daily and weekly load	NN	Wavelet transform	Trend load series under different frequency bands and the detailed load series.
[8]	Monthly load	Hybrid method combining ARIMA, support vector machine (SVM), and Holt-Winters	Seasonal adjustments and H-P filter	Trend, seasonal, cyclic, and irregular components.
[9]	Fault line(s)	Intrinsic mode function (IMF)	Empirical mode decomposition (EMD)	Zero sequence current at different frequencies.
[10]	Monthly load	NN	Moving regression and smoothing spline decomposition models	Trend and fluctuation series.
[11]	Monthly load	SARIMA	Multiplicative decomposition	Trend and seasonal components.
[12]	Monthly load	ARIMA	X12	Trend, seasonal, and random components.
[13]	Monthly number of a software bug	Hybrid method combining ARIMA, X12, and polynomial regression	X12	Seasonal and cyclic components.
[14]	Monthly load	Hybrid method combining ARIMA and vector error correction (VEC)	X12	Trends of load and economy, seasonality, holiday, and irregular components.
[15]	Mean flying hours between failures for aircraft	Hybrid method combining grey box, back propagation NN (BPNN), and SVM.	STL	Long-term trend and seasonal components.
[16]	Future geospatial incidence levels	Kernel density estimation with dynamic kernel bandwidth	STL	Annual, seasonal, weekend effect, and random components.
[17]	Bids for amazon EC2 spot instances	Benchmarked time series forecasting methods such as naïve, ARIMA, and ETS	STL	Spike and seasonal components.
[18]	Daily and weekly load	Hybrid method combining Holt-Winters and SVR	STL	Base component and weather sensitive component.