<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Energy consumption forecasting is a kind of fundamental work of the energy management in equipment-manufacturing enterprises, and an important way to reduce energy consumption. Therefore, this paper proposes an intellectualized, short-term distributed energy consumption forecasting model for equipment-manufacturing enterprises based on cloud computing and extreme learning machine considering the practical enterprise situation of massive and high-dimension data. The analysis of the real energy consumption data provided by LB Enterprise was undertaken and corresponding calculating experiments were completed using a 32-node cloud computing cluster. The experimental results show that the energy consumption forecasting accuracy of the proposed model is higher than the traditional support vector regression and the generalized neural network algorithm. Furthermore, the proposed forecasting algorithm possesses excellent parallel performance, overcomes the shortcoming of a single computer’s insufficient computing power when facing massive and high-dimensional data without increasing the cost.</p></abstract>

Energy consumption forecasting is a kind of fundamental work of the energy management in equipment-manufacturing enterprises, and an important way to reduce energy consumption. Therefore, this paper proposes an intellectualized, short-term distributed energy consumption forecasting model for equipment-manufacturing enterprises based on cloud computing and extreme learning machine considering the practical enterprise situation of massive and high-dimension data. The analysis of the real energy consumption data provided by LB Enterprise was undertaken and corresponding calculating experiments were completed using a 32-node cloud computing cluster. The experimental results show that the energy consumption forecasting accuracy of the proposed model is higher than the traditional support vector regression and the generalized neural network algorithm. Furthermore, the proposed forecasting algorithm possesses excellent parallel performance, overcomes the shortcoming of a single computer’s insufficient computing power when facing massive and high-dimensional data without increasing the cost.

Cloud Computing and Extreme Learning Machine for a Distributed Energy Consumption Forecasting in Equipment-Manufacturing Enterprises

Department of Mathematics, Tianjin University of Technology, Tianjin 300384,

ZhongHuan Information College, Tianjin University of Technology, Tianjin 300380,

Economics and Management School, Yanshan University, Qinhuangdao, Hebei 066004,

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Energy consumption forecasting is a kind of fundamental work of the energy management in equipment-manufacturing enterprises, and an important way to reduce...

Cloud Computing and Extreme Learning Machine for a Distributed Energy Consumption Forecasting in Equipment-Manufacturing Enterprises

Publié en ligne: 25 janv. 2017

Pages: 83 - 97

DOI: https://doi.org/10.1515/cait-2016-0079

Mots clés
Energy consumption forecasting, cloud computing, online sequential optimization, extreme learning machine, equipment-manufacturing enterprises

© 2016 Rui-Dong Wang et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Cloud Computing and Extreme Learning Machine for a Distributed Energy Consumption Forecasting in Equipment-Manufacturing Enterprises

Publié en ligne: 25 janv. 2017

Pages: 83 - 97

DOI: https://doi.org/10.1515/cait-2016-0079

Mots clésEnergy consumption forecasting, cloud computing, online sequential optimization, extreme learning machine, equipment-manufacturing enterprises

© 2016 Rui-Dong Wang et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
Energy consumption forecasting, cloud computing, online sequential optimization, extreme learning machine, equipment-manufacturing enterprises