Design of Short-Term Power Load Forecasting Model Based on Deep Neural Network

Authors

  • Qinwei Duan Power Dispatch and Control Center, Guangdong Power Grid Co., Ltd, China Southern Power Grid, Guangzhou, 243000, China
  • Zhu Chao Power Dispatch and Control Center, Guangdong Power Grid Co., Ltd, China Southern Power Grid, Guangzhou, 243000, China
  • Cong Fu Power Dispatch and Control Center, Guangdong Power Grid Co., Ltd, China Southern Power Grid, Guangzhou, 243000, China
  • Yashan Zhong Power Dispatch and Control Center, Guangdong Power Grid Co., Ltd, China Southern Power Grid, Guangzhou, 243000, China
  • Jiaxin Zhuo Beijing TsIntergy Technology Co., Ltd, Beijing, 100084, China
  • Ye Liao Beijing TsIntergy Technology Co., Ltd, Beijing, 100084, China

DOI:

https://doi.org/10.13052/spee1048-5236.43211

Keywords:

RNN, global recurrent Unit, Short term power load, Attention LSTM, AdaBoost

Abstract

In power system operation and planning, the accuracy of short-term power load forecasting is very important. Because of its powerful data processing and modeling ability, deep neural network has become an effective tool to accurately predict short-term power load. In this study, a short-term power load prediction model based on deep neural network is designed, which adopts deep long short-term memory and threshold period unit model, and combines Boosting algorithm for model fusion. The results show that the average absolute percentage error of the model fused by Boosting algorithm is 0.07%, which is 1.02% lower than the average weight method and 0.59% lower than the reciprocal error method. Boosting fusion model can effectively reduce the overall prediction error and maintain high stability of prediction error at peak, plateau and time sampling points, so as to achieve good prediction effect. Specifically, the MAPE of the model fused using Boosting algorithm is 0.07% (95% confidence), which is 1.14% higher than the average weight method and 0.79% higher than the reciprocal error method. The design of short-term power load forecasting model based on deep neural network can provide more accurate prediction for power system operation and planning, and help to improve the operation efficiency and reliability of power system. At the same time, the design and application of this model also provide a new idea and method for the application of deep learning in power system. The introduction of Boosting algorithm further improves the prediction accuracy and stability of the model, which is a major innovation in model design.

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Author Biographies

Qinwei Duan, Power Dispatch and Control Center, Guangdong Power Grid Co., Ltd, China Southern Power Grid, Guangzhou, 243000, China

Qinwei Duan obtained his BE in Electrical Engineering & Automation from Sichuan University in 2013. He obtained his MEng in Electrical Engineering from the University of Nottingham in 2014 and Ph.D from the University of Hong Kong in 2018. Presently, he is working in Guangdong Power Grid Power Dispatch and Control Center, China Southern Power Grid. His areas of interest are power system dispatch, load forecast, power balance, and renewable energy integration.

Zhu Chao, Power Dispatch and Control Center, Guangdong Power Grid Co., Ltd, China Southern Power Grid, Guangzhou, 243000, China

Zhu Chao obtained his BE and Master degree in Electrical Engineering & Automation from South China University of Technology in 2008 and 2011, respectively. Presently, he is working in Guangdong Power Grid Power Dispatch and Control Center, China Southern Power Grid. His areas of interest are power system dispatch, load forecast, power balance, and electricity market.

Cong Fu, Power Dispatch and Control Center, Guangdong Power Grid Co., Ltd, China Southern Power Grid, Guangzhou, 243000, China

Cong Fu obtained his BE in Electrical Engineering & Automation from HUST in 2010. He obtained his Master degree in Electrical Engineering from Wuhan University. Presently, he is working in Guangdong Power Grid Power Dispatch and Control Center, China Southern Power Grid. His areas of interest are long term power balance, and renewable energy integration.

Yashan Zhong, Power Dispatch and Control Center, Guangdong Power Grid Co., Ltd, China Southern Power Grid, Guangzhou, 243000, China

Yashan Zhong obtained her BE in Business Administration from North China Electric Power University in 2014. She obtained her Master degree in Technology Economy and Management from North China Electric Power University in 2017. Presently, she is working in Guangdong Power Grid Power Dispatch and Control Center, China Southern Power Grid. Her areas of interest are power system dispatch, load forecast, power balance, and system economic operation.

Jiaxin Zhuo, Beijing TsIntergy Technology Co., Ltd, Beijing, 100084, China

Jiaxin Zhuo obtained his BE in Electrical Engineering & Automation from Southwest University in 2018. He obtained his Master degree in Agricultural electrification and automation from Southwest University in 2021. Presently, he is working in Beijing TsIntergy Technology Co., Ltd. His areas of interest are power system dispatch and load forecast.

Ye Liao, Beijing TsIntergy Technology Co., Ltd, Beijing, 100084, China

Ye Liao obtained his BE in Applied Chemistry from Nanchang University in 2003. He obtained his Master degree in Software Engineering from Beihang University in 2015. Presently, he is working in Beijing TsIntergy Technology Co., Ltd. His areas of interest are power system dispatch, load forecast, and power balance.

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Published

2024-01-14

How to Cite

Duan, Q. ., Chao, Z. ., Fu, C. ., Zhong, Y. ., Zhuo, J. ., & Liao, Y. . (2024). Design of Short-Term Power Load Forecasting Model Based on Deep Neural Network. Strategic Planning for Energy and the Environment, 43(02), 425–452. https://doi.org/10.13052/spee1048-5236.43211

Issue

Section

Greener Energy and Sustainable Development with AI-based loT