Random vector functional link neural network based ensemble deep
learning for short-term load forecasting
- URL: http://arxiv.org/abs/2107.14385v1
- Date: Fri, 30 Jul 2021 01:20:48 GMT
- Title: Random vector functional link neural network based ensemble deep
learning for short-term load forecasting
- Authors: Ruobin Gao, Liang Du, P.N. Suganthan, Qin Zhou, Kum Fai Yuen
- Abstract summary: This paper proposes a novel ensemble deep Random Functional Link (edRVFL) network for electricity load forecasting.
The hidden layers are stacked to enforce deep representation learning.
The model generates the forecasts by ensembling the outputs of each layer.
- Score: 14.184042046855884
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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