National Natural Science Foundation of China; Guangdong Province Science and Technology Plan Project; Brunel University London; UK BRIEF Funding; Department of Finance and Education of Guangdong Province 2016; Key Discipline Construction Program, China; Education Department of Guangdong Province: New and Integrated Energy System Theory and Technology Research Grou

Lai, CS

Lai, LL

Ng, WWY

Pan, K

Vaccaro, A

Wang, T

Zhang, M

International Journal of Electrical Power & Energy Systems

This paper presents a short-term electric load forecasting model based on deep autoencoder with localized stochastic sensitivity (D-LiSSA). D-LiSSA can learn informative hidden representations from unseen samples by minimizing the perturbed error (including the training error and stochastic sensitivity) from historical load data. Specifically, this general deep autoencoder network as a deep learning model improves prediction accuracy and reliability. Moreover, a nonlinear fully connected feedforward neural network as a regression layer is applied to forecast the short-term load, with the generalization capability of the proposed model using hidden representations learned by D-LiSSA. The performance of D-LiSSA is evaluated using real-world public electric load markets of France (FR), Germany (GR), Romania (RO), and Spain (ES) from ENTSO-E. Extensive experimental results and comparisons with the classical and state-of-the-art models show that D-LiSSA yields accurate load forecasting results and achieves desired reliable capability. For instance, with the French case, D-LiSSA yields the lowest mean absolute error, mean absolute percentage error, root mean squared error; providing up to 61.89%, 63.20%, and 56.40% forecasting accuracy improvements as compared to the benchmark model for forecasting hourly horizon, respectively.National Natural Science Foundation of China; Guangdong Province Science and Technology Plan Project; Brunel University London; UK BRIEF Funding; Department of Finance and Education of Guangdong Province 2016; Key Discipline Construction Program, China; Education Department of Guangdong Province: New and Integrated Energy System Theory and Technology Research Grou

Brunel University Research Archive

Deep autoencoder with localized stochastic sensitivity for short-term load forecasting

http://bura.brunel.ac.uk/bitstream/2438/22495/1/FullText.pdf

Deep autoencoder with localized stochastic sensitivity for short-term load forecasting

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Brunel University Research Archive