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

• Published in: Processes Vol. 12; no. 1; p. 32
• Main Authors: Wang, Zhenglang, Feng, Zao, Ma, Zhaojun, Peng, Jubo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2024
• Subjects: Accuracy; Algorithms; Carbon; Case studies; China; Correlation analysis; Correlation coefficients; Data collection; Data entry; Electricity; Emissions; Energy conservation; Energy consumption; Energy efficiency; Energy management; Energy management systems; Energy use; Evolutionary algorithms; Experiments; Gaussian process; Iron compounds; Learning; Manufacturing industry; Metallurgy; Multilayer perceptrons; Multilayers; Neural networks; Nonferrous metals; Nonlinear systems; Optimization algorithms; Prediction models; Regression models; Simulation; Smelting; Software; Steel production; Subject specialists; Support vector machines; Sustainable development; Tin; China
• ISSN: 2227-9717; 2227-9717
• DOI: 10.3390/pr12010032

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.