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

Truong Hoang Bao Huy, Dieu Ngoc Vo, Khai Phuc Nguyen, Viet Quoc Huynh, Minh Q. Huynh, Khoa Hoang Truong

202324 citationsDOI

Abstract

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.

Topics & Concepts

Computer scienceTerm (time)Convolutional neural networkArtificial intelligenceElectric power systemArtificial neural networkLong short term memoryData modelingMachine learningPower (physics)Recurrent neural networkDatabasePhysicsQuantum mechanicsEnergy Load and Power ForecastingStock Market Forecasting MethodsTraffic Prediction and Management Techniques