Application of empirical mode decomposition in improving group method of data handling
The accuracy of electricity load demand forecasting is essential for avoiding energy waste and overuse. Hence, this paper aims to model the forecast electricity load demand by combining Empirical Mode Decomposition (EMD) with Group Method of Data Handling (GMDH) model. The proposed methodology wor...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | en |
| Published: |
2023
|
| Online Access: | http://eprints.utem.edu.my/id/eprint/27850/1/Application%20of%20empirical%20mode%20decomposition%20in%20improving%20group%20method%20of%20data%20handling.pdf http://eprints.utem.edu.my/id/eprint/27850/ https://pubs.aip.org/aip/acp/article-abstract/2500/1/020006/2875223/Application-of-empirical-mode-decomposition-in?redirectedFrom=fulltext |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The accuracy of electricity load demand forecasting is essential for avoiding energy waste and overuse. Hence,
this paper aims to model the forecast electricity load demand by combining Empirical Mode Decomposition
(EMD) with Group Method of Data Handling (GMDH) model. The proposed methodology works in three steps: it
decomposes the original load data series into several Intrinsic Model Functions (IMFs) and one residual
component, enables individual forecasting of each IMF and the residual using the GMDH model by using the
Partial Autocorrelation Function (PACF) as the input variable, and aggregates all the forecasted values to yield
the final prediction for electricity load demand. To compare the performance, another model is considered
namely the combination of EMD with the Artificial Neural Network (EMD-ANN). The empirical result from the
performance evaluation concluded that EMD-GMDH outperforms the EMD-ANN as well as the GMDH model
without decomposing the time series. |
|---|