CaltechAUTHORS
  A Caltech Library Service

Short-Term Load Forecasting for Industrial Enterprises Based on Long Short-Term Memory Network

Wang, Tingyuan and Chen, Xiaoqiao and Wang, Yuanyuan and Chen, Yuhao and Chen, Jun and Sun, Shanfeng (2019) Short-Term Load Forecasting for Industrial Enterprises Based on Long Short-Term Memory Network. In: 2019 IEEE 3rd Conference on Energy Internet and Energy System Integration (EI2). IEEE , Piscataway, NJ, pp. 1759-1764. ISBN 9781728131375. https://resolver.caltech.edu/CaltechAUTHORS:20200417-134621813

Full text is not posted in this repository. Consult Related URLs below.

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20200417-134621813

Abstract

In China, industrial enterprises develop rapidly, and industrial electricity consumption account for 70% of social electricity consumption. However, the charging mode of industrial electricity is very complicated which can be divided into two basic modes, one mode is to pay according to transformer capacity and other mode is to pay according to maximum demand. In both modes, the complex charging type involving seasonal factors and national economic growth. Once a company chooses the wrong mode to buy electricity, it has to change it three months later. In this case, it not only increases the cost of electricity but also wastes resources. Therefore, it is important to predict the future load data according to the energy consumption characteristics of enterprises. In the context of big data, the electricity data of Chinese enterprises will be developed and utilized gradually. In this paper, the long short-term memory (LSTM) model is built to carry out load forecasting of the international dairy companies which called Ausnutria. The simulation results show that LSTM network is feasible in the field of load prediction, and the method can ensure the accuracy.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/ei247390.2019.9061773DOIArticle
Additional Information:© 2019 IEEE.
Subject Keywords:load forecasting, Long Short-Term Memory, industrial load, neural network
Record Number:CaltechAUTHORS:20200417-134621813
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200417-134621813
Official Citation:T. Wang, X. Chen, Y. Wang, Y. Chen, J. Chen and S. Sun, "Short-Term Load Forecasting for Industrial Enterprises Based on Long Short-Term Memory Network," 2019 IEEE 3rd Conference on Energy Internet and Energy System Integration (EI2), Changsha, China, 2019, pp. 1759-1764; doi: 10.1109/ei247390.2019.9061773
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:102610
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:17 Apr 2020 20:55
Last Modified:17 Apr 2020 20:55

Repository Staff Only: item control page