Short-term load probabilistic forecasting based on quantile regression convolutional neural network and Epanechnikov kernel density estimation

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 a...

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Autori principali:	Hui He, Junting Pan, Nanyan Lu, Bo Chen, Runhai Jiao
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Elsevier 2020-12-01
Serie:	Energy Reports
Soggetti:	Probabilistic load forecasting Quantile regression Convolutional neural network Kernel density estimation
Accesso online:	http://www.sciencedirect.com/science/article/pii/S2352484720314062
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