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

TL;DR

This paper introduces an ensemble deep Random Vector Functional Link neural network combined with empirical wavelet transformation for improved short-term electricity load forecasting, demonstrating superior accuracy over existing methods.

Contribution

The paper proposes a novel ensemble deep RVFL network with wavelet-based feature augmentation for load forecasting, addressing non-linearity and non-stationarity challenges.

Findings

Outperforms eleven existing forecasting methods

Achieves lower error metrics in tests

Demonstrates statistical significance in results

Abstract

Electricity load forecasting is crucial for the power systems' planning and maintenance. However, its un-stationary and non-linear characteristics impose significant difficulties in anticipating 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 kept fixed during the training process. The hidden layers are stacked to enforce deep representation learning. Then, the model generates the forecasts by ensembling the outputs of each layer. Moreover, we also propose to augment the random enhancement features by empirical wavelet transformation (EWT). The raw load data is decomposed by EWT in a walk-forward fashion, not 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 twenty 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 three error metrics and statistical tests on electricity load forecasting tasks.