Electrical Energy Demand Forecasting Model Development and Evaluation with Maximum Overlap Discrete Wavelet Transform-Online Sequential Extreme Learning Machines Algorithms

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.

ELM-QR-Based Nonparametric Probabilistic Prediction Method for Wind Power

“…Figure 8 shows the prediction results of some typical quantiles. The sensitivity coefficient κ is introduced, and its calculation formula is shown in Equation (23), where τ, τ are the quantile points corresponding to the upper and lower bounds of the intervals. Calculate various performance indices under different sensitivities and make subsequent comparisons:…”

“…As a special type of single-hidden layer feedforward neural networks, ELM has the characteristics of fast learning speed and strong generalization capability. It has been widely used in recent years [23,24]. In [25], ELM with error correction was used for short-term wind speed prediction; in [26], the multinomial Bayesian extreme learning machine (MBELM) was proposed for multi-class classification; on-line sequential outlier robust extreme learning machine was applied in [27] for probabilistic wind speed forecasting; in [28], a self-adaptive kernel extreme learning machine was proposed for short-term wind speed forecasting; according to the training time shown in [29], ELM can obtain more accurate forecasting results with a faster calculation speed than comparison models, its structural advantages are fully demonstrated.…”

Forecasting Photosynthetic Photon Flux Density Under Cloud Effects: Novel Predictive Model Using Convolutional Neural Network Integrated With Long Short-term Memory Network

“…The theoretical details of deep learning (i.e., CNN, LSTM, DNN) and conventional machine learning (ELM and MARS) methods are described elsewhere [43][44][45][46][47][48]). The CLSTM model, constructed by integrating CNN and LSTM, had been used elsewhere in natural language processing where emotions were analysed with text inputs [49], in speech processing where voice search tasks were performed using CLDNN combining CNN, LSTM and DNN [50], in video processing with CNN and Bidirectional LSTM models built to recognize human actions in video sequences [51], in the medical area where the CNN-LSTM method was developed to detect arrhythmias in electrocardiograms [52] and in industrial areas where a convolutional bi-directional LSTM model was designed to predict tool wearing [53].…”

Denoising of Heavily Contaminated Partial Discharge Signals in High-Voltage Cables Using Maximal Overlap Discrete Wavelet Transform

“…MODWT is a modified version of the DWT that has many advantages over it that will be discussed in detail in Section 2. MODWT has been used before in many applications, such as, for example, the statistical analysis of atmospheric turbulence [30], analyzing price dynamics for crude oil [31], modeling of the depth of bodies of water [32], and forecasting electrical energy demand [33]. However, so far, apart from the authors [29], a MODWT-based algorithm has not been used in PD denoising.…”