Work

Short-Term Electric Load Prediction and Early Warning in Industrial Parks Based on Neural Network

Year: 2021

Type: article

Abstract: This paper proposes a load forecasting method based on LSTM model, fully explores the regularity of historical load data of industrial park enterprises, inputs the data features into LSTM units for feature extraction, and applies the attention-based model for load forecasting. The experiments show that the accuracy of our prediction model and early warning model is better than that of the baseline and can reach the standard of application in practice; this model can also be used for early warning of local sudden large loads and identification of enterprise power demand. Therefore, the validity of the method proposed in this paper is verified using the historical dataset of industrial parks, and relevant technical products and business models are formed to provide value-added services to users by combining existing practical cases for the specific scenario of industrial parks.

Source: Discrete Dynamics in Nature and Society

DOI: 10.1155/2021/1435334

Authors: Guannan Wang, Pei Yang, Jiayi Chen

Institution: State Grid Corporation of China (China)

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,570