Random vector functional link neural network based ensemble deep learning for short-term load forecasting

Abstract

Electric load forecasting is essential for the planning and maintenance of power systems. However, its un-stationary and non-linear properties impose significant difficulties in predicting future demand. This paper proposes a novel ensemble deep Random Vector Functional Link (edRVFL) network for electricity load forecasting. The weights of hidden layers are randomly initialized and fixed during the training process. The hidden layers are stacked to enforce deep representation learning. Then, the model generates the forecasts using the ensemble of the outputs of each layer. Moreover, we also propose to augment the random enhancement features by empirical wavelet transformation (EWT). The raw load data are decomposed by EWT in a walk-forward approach without introducing future data leakage problems in the decomposition process. Finally, all the sub-series generated by the EWT, including raw data, are fed into the edRVFL for forecasting purposes. The proposed model is evaluated on sixteen publicly available time series from the Australian Energy Market Operator of the year 2020. The simulation results demonstrate the proposed model’s superior performance over eleven forecasting methods in two error metrics and statistical tests on electricity load forecasting tasks.

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ORCID for Qin Zhou: orcid.org/0000-0002-0273-6295

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