GMDH-type Neural Network Based Short-term Load Forecasting Method in Power System

被引：0
|
作者：
Bao, Yin-Yin [1 ]
Liu, Yu [2 ]
Wang, Jie-Sheng [2 ]
Wang, Ming-Wei [1 ]
机构：
[1] School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan,114051, China
[2] Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan,114051, China
来源：
IAENG International Journal of Computer Science | 2023年 / 50卷 / 04期
关键词：
Data handling - Electric power plant loads - Electric power system planning - Forecasting;
D O I：
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学科分类号：
摘要：
In modern electric power systems, operational planning procedures matter considerably in ensuring whether technical and economic performance standards are met while also meeting the power load requirements. A short-term load prediction method based on the Group Method of Data Handling (GMDH)-type neural network was proposed to address this. Combining the GMDH-type neural network could achieve accurate short-term load prediction in power systems. The neural network analyzed historical load data and other relevant factors to learn patterns and predict the near future. Besides, simulation experiments were also conducted to validate the effectiveness of the proposed algorithm, which fully demonstrated its capability to accurately predict short-term load in power systems, thus contributing to improving the operational planning and decision-making and enhancing the technical and economic performance of power systems. Overall, utilizing the GMDH-type neural networks in short-term load forecasting was found to be efficient in enhancing the operational efficiency and reliability of modern power systems. © (2023), (International Association of Engineers). All Rights Reserved.
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