Industrial Ultra-Short-Term Load Forecasting With Data Completion

Accurate and efficient ultra-short-term load forecasting is crucial for industrial power users to have stabilized and optimized operations. In this paper, we develop novel strategies for industrial power users to handle their challenges in ultra-short-term load forecasting. Firstly, this paper propo...

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Vydáno v:	IEEE access Ročník 8; s. 158928 - 158940
Hlavní autoři:	Jiang, Haoyu, Wu, Angjian, Wang, Bo, Xu, Peizhe, Yao, Gang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Back propagation networks clonal immune algorithm combined model Cubature Kalman filter Data loss Data mining Data models Dynamical systems Electricity consumption Forecasting Genetic algorithms grey neural network Kalman filters least squares support vector machine Load forecasting Load modeling Mathematical models Missing data Neural networks Nonlinear dynamics Nonlinear systems Particle swarm optimization Prediction algorithms Prediction models Predictive models PSO-SVR Support vector machines
ISSN:	2169-3536, 2169-3536
On-line přístup:	Získat plný text
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