An Improved CNN-BILSTM Model for Power Load Prediction in Uncertain Power Systems

Chao Tang, Yufeng Zhang, Fan Wu and Zhuo Tang ()
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Chao Tang: College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
Yufeng Zhang: College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
Fan Wu: College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
Zhuo Tang: College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China

Energies, 2024, vol. 17, issue 10, 1-16

Abstract: Power load prediction is fundamental for ensuring the reliability of power grid operation and the accuracy of power demand forecasting. However, the uncertainties stemming from power generation, such as wind speed and water flow, along with variations in electricity demand, present new challenges to existing power load prediction methods. In this paper, we propose an improved Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BILSTM) model for analyzing power load in systems affected by uncertain power conditions. Initially, we delineate the uncertainty characteristics inherent in real-world power systems and establish a data-driven power load model based on fluctuations in power source loads. Building upon this foundation, we design the CNN-BILSTM model, which comprises a convolutional neural network (CNN) module for extracting features from power data, along with a forward Long Short-Term Memory (LSTM) module and a reverse LSTM module. The two LSTM modules account for factors influencing forward and reverse power load timings in the entire power load data, thus enhancing model performance and data utilization efficiency. We further conduct comparative experiments to evaluate the effectiveness of the proposed CNN-BILSTM model. The experimental results demonstrate that CNN-BILSTM can effectively and more accurately predict power loads within power systems characterized by uncertain power generation and electricity demand. Consequently, it exhibits promising prospects for industrial applications.

Keywords: artificial intelligence; CNN-BILSTM model; power load prediction; uncertain power systems (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: 2024
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