Journal
ENERGY REPORTS
Volume 6, Issue -, Pages 1550-1556Publisher
ELSEVIER
DOI: 10.1016/j.egyr.2020.10.053
Keywords
Probabilistic load forecasting; Quantile regression; Convolutional neural network; Kernel density estimation
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Electricity load forecasting plays an indispensable role in the electric power systems. However, its characteristics of uncertainty and complexity are hard to handle. This paper proposes a probabilistic load forecasting approach named QRCNN-E. Specifically, the deep convolutional neural network is applied to model the non-linear relationship with the electricity load and its influencing factors. By replacing the traditional loss function with pinball loss, the network can eventually forecast loads in quantiles. Then, kernel density estimation takes quantile forecasts as inputs and produces deterministic and probabilistic results. Case studies on GEFCom2014 show that the proposed method presents better performance than other cutting-edge models. (C) 2020 The Authors. Published by Elsevier Ltd.
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