Short-Term Load Forecasting in Power System Using CNN-LSTM Neural Network

被引：0

作者：

Truong Hoang Bao Huy ^{[1
]}

Dieu Ngoc Vo ^{[2
]}

Khai Phuc Nguyen ^{[2
]}

Viet Quoc Huynh ^{[2
]}

Minh Quang Huynh ^{[2
]}

Khoa Hoang Truong ^{[2
]}

机构：

[1] Soonchunhyang Univ, Dept Future Convergence Technol, Asan, Chuncheongnam D, South Korea

[2] Vietnam Natl Univ Ho Chi Minh City, Ho Chi Minh City Univ Technol HCMUT, Dept Power Syst, Ho Chi Minh City, Vietnam

来源：

2023 ASIA MEETING ON ENVIRONMENT AND ELECTRICAL ENGINEERING, EEE-AM | 2023年

关键词：

Short-term load forecasting; CNN-LSTM; Long; Short-Term Memory; Convolutional Neural Networks;

D O I：

暂无

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

The accurate forecasting of short-term load plays a significant role in power systems operation and planning. This paper suggests a short-term load forecasting model combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The developed CNN-LSTM aims to capture both spatial and temporal dependencies within the load data, leveraging the strengths of both architectures. Simulations are performed using real-world power system load data. Comparative analyses are carried out against standalone CNN and LSTM models. The CNN-LSTM has significantly better forecasting accuracy than other models, showcasing its effectiveness in shortterm load forecasting.

引用

页数：6

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