Abstract
Electricity load demand forecasting is a complex process since the pattern of electricity load demand data sets varies. To overcome this problem, a fast ensemble-decomposed model was proposed in this work. Firstly, two data sets of electricity load demand, which are electricity consumption and electricity production, were decomposed into two Intrinsic Mode Functions (IMFs). Secondly, the different values of ensemble trials are employed into fast ensemble-decomposed model. Then, the second IMF was used as the intrinsic prediction trend for the actual electricity load demand data sets. Lastly, the second IMF was compared with the actual electricity load demand time series data and the intrinsic prediction trend of the second IMF was forecasted. Simulation results revealed that the FED model better than the ARIMA and ANN methods and different values of ensembles trials do effect forecast accuracy.
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.