Abstract
To support regional electricity markets, accurate and reliable energy demand (G) forecast models are vital stratagems for stakeholders in this sector. An online sequential extreme learning machine (OS-ELM) model integrated with a maximum overlap discrete wavelet transform (MODWT) algorithm was developed using daily G data obtained from three regional campuses (i.e., Toowoomba, Ipswich, and Springfield) at the University of Southern Queensland, Australia. In training the objective and benchmark models, the partial autocorrelation function (PACF) was first employed to select the most significant lagged input variables that captured historical fluctuations in the G time-series data. To address the challenges of non-stationarities associated with the model development datasets, a MODWT technique was adopted to decompose the potential model inputs into their wavelet and scaling coefficients before executing the OS-ELM model. The MODWT-PACF-OS-ELM (MPOE) performance was tested and compared with the non-wavelet equivalent based on the PACF-OS-ELM (POE) model using a range of statistical metrics, including, but not limited to, the mean absolute percentage error (MAPE%). For all of the three datasets, a significantly greater accuracy was achieved with the MPOE model relative to the POE model resulting in an MAPE = 4.31% vs. MAPE = 11.31%, respectively, for the case of the Toowoomba dataset, and a similarly high performance for the other two campuses. Therefore, considering the high efficacy of the proposed methodology, the study claims that the OS-ELM model performance can be improved quite significantly by integrating the model with the MODWT algorithm.
Highlights
To promote the application of appropriate strategic measures and provide accurate scheduling of electrical power in energy security platforms, a forecasting model that can reliably and precisely forecast the electricity energy demand (G), is required
While some other studies [5,6,17,20,21] have tried to address these issues by applying different forms of Wavelet transformation (WT) multiresolution analysis (MRA), for example, discrete wavelet transform (DWT)-MRA or maximal overlap discrete wavelet transform (MODWT)-MRA separately to the training, validation, and testing of data, these approaches require the full time-series to calculate the detail and approximation coefficients, leaving some of the previously mentioned issues unresolved [19]
As the quality of model forecasts of G data cannot be established by a single statistical metric for the testing phase [30], additional measures, besides the root-mean square error (RMSE) (Equation (9)), were used [30,31,32,33,34,35,36,37,38,39]
Summary
To promote the application of appropriate strategic measures and provide accurate scheduling of electrical power in energy security platforms, a forecasting model that can reliably and precisely forecast the electricity energy demand (G), is required. While some other studies [5,6,17,20,21] have tried to address these issues by applying different forms of WT multiresolution analysis (MRA), for example, discrete wavelet transform (DWT)-MRA or maximal overlap discrete wavelet transform (MODWT)-MRA separately to the training, validation, and testing of data, these approaches require the full time-series to calculate the detail and approximation coefficients, leaving some of the previously mentioned issues unresolved [19] Again these studies have failed to apply WT in real-world forecasting problem. The study limitations showing future work opportunities, and conclusions are summarized in Sections 5 and 6, respectively
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