Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.