A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network

Chujie Tian, Jian Ma, Chunhong Zhang and Panpan Zhan
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Chujie Tian: Institute of Network Technology, Beijing University of Posts and Telecommunications, Xitucheng Road No.10 Hadian District, Beijing 100876, China
Jian Ma: Institute of Network Technology, Beijing University of Posts and Telecommunications, Xitucheng Road No.10 Hadian District, Beijing 100876, China
Chunhong Zhang: School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Xitucheng Road No.10 Hadian District, Beijing 100876, China
Panpan Zhan: Beijing Institute of Spacecraft System Engineering, 104 YouYi Road Hadian District, Beijing 100094, China

Energies, 2018, vol. 11, issue 12, 1-13

Abstract: Accurate electrical load forecasting is of great significance to help power companies in better scheduling and efficient management. Since high levels of uncertainties exist in the load time series, it is a challenging task to make accurate short-term load forecast (STLF). In recent years, deep learning approaches provide better performance to predict electrical load in real world cases. The convolutional neural network (CNN) can extract the local trend and capture the same pattern, and the long short-term memory (LSTM) is proposed to learn the relationship in time steps. In this paper, a new deep neural network framework that integrates the hidden feature of the CNN model and the LSTM model is proposed to improve the forecasting accuracy. The proposed model was tested in a real-world case, and detailed experiments were conducted to validate its practicality and stability. The forecasting performance of the proposed model was compared with the LSTM model and the CNN model. The Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) were used as the evaluation indexes. The experimental results demonstrate that the proposed model can achieve better and stable performance in STLF.

Keywords: short-term load forecast; long short-term memory networks; convolutional neural networks; deep neural networks; artificial intelligence (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (77)

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