Short-Term Electricity Consumption Forecasting for a Steel Enterprise

Morgoeva, Angelika; Turluev, Ramzan; Madaeva, Mariet

doi:10.1109/rusautocon58002.2023.10272816

2023 International Russian Automation Conference (RusAutoCon) 2023

DOI: 10.1109/rusautocon58002.2023.10272816

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Short-Term Electricity Consumption Forecasting for a Steel Enterprise

Angelika Morgoeva,

Ramzan Turluev,

Mariet Madaeva

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A Multi-Output Regression Model for Energy Consumption Prediction Based on Optimized Multi-Kernel Learning: A Case Study of Tin Smelting Process

Wang,

Feng,

et al. 2023

Processes

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Energy consumption forecasting plays an important role in energy management, conservation, and optimization in manufacturing companies. Aiming at the tin smelting process with multiple types of energy consumption and a strong coupling with energy consumption, the traditional prediction model cannot be applied to the multi-output problem. Moreover, the data collection frequency of different processes is inconsistent, resulting in few effective data samples and strong nonlinearity. In this paper, we propose a multi-kernel multi-output support vector regression model optimized based on a differential evolutionary algorithm for the prediction of multiple types of energy consumption in tin smelting. Redundant feature variables are eliminated using the distance correlation coefficient method, multi-kernel learning is introduced to improve the multi-output support vector regression model, and a differential evolutionary algorithm is used to optimize the model hyperparameters. The validity and superiority of the model was verified using the energy consumption data of a non-ferrous metal producer in Southwest China. The experimental results show that the proposed model outperformed multi-output Gaussian process regression (MGPR) and a multi-layer perceptron neural network (MLPNN) in terms of measurement capability. Finally, this paper uses a grey correlation analysis model to discuss the influencing factors on the integrated energy consumption of the tin smelting process and gives corresponding energy-saving suggestions.

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A Multi-Output Regression Model for Energy Consumption Prediction Based on Optimized Multi-Kernel Learning: A Case Study of Tin Smelting Process

Wang,

Feng,

et al. 2023

Processes

View full text Add to dashboard Cite

show abstract

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Short-Term Electricity Consumption Forecasting for a Steel Enterprise

Cited by 1 publication

References 24 publications

A Multi-Output Regression Model for Energy Consumption Prediction Based on Optimized Multi-Kernel Learning: A Case Study of Tin Smelting Process

A Multi-Output Regression Model for Energy Consumption Prediction Based on Optimized Multi-Kernel Learning: A Case Study of Tin Smelting Process

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