Short-Term Load Forecasting in Power System Using CNN-LSTM Neural Network
- Resource Type
- Conference
- Authors
- Bao Huy, Truong Hoang; Vo, Dieu Ngoc; Nguyen, Khai Phuc; Huynh, Viet Quoc; Huynh, Minh Quang; Truong, Khoa Hoang
- Source
- 2023 Asia Meeting on Environment and Electrical Engineering (EEE-AM) Environment and Electrical Engineering (EEE-AM), 2023 Asia Meeting on. :01-06 Nov, 2023
- Subject
- Components, Circuits, Devices and Systems
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
Power, Energy and Industry Applications
Load forecasting
Neural networks
Urban areas
Predictive models
Convolutional neural networks
Forecasting
Load modeling
Short-term load forecasting
CNN-LSTM
Long Short-Term Memory
Convolutional Neural Networks
- Language
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 short-term load forecasting.