Deep Learning Assisted Online Multi-Step Demand Forecasting of Fused Magnesia Smelting Processes

This paper proposes a multi-step ahead power demand model for fused magnesia smelting processes (FMSP) which combines a linear model and an unknown nonlinear term to predict the electricity demand and its variation tendency for the next 5 steps. The linear model is identified by the multi-output fas...

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Vydané v:	2022 4th International Conference on Industrial Artificial Intelligence (IAI) s. 1 - 8
Hlavní autori:	Li, Mingyu, Zhang, Jingwen, Chai, Tianyou
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 24.08.2022
Predmet:	Bayesian optimization Deep learning Demand forecasting Long-short term memory Multi-output fast recursive algorithm Online multi-step ahead demand forecasting Power demand Prediction algorithms Predictive models Real-time systems Smelting
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