A Very Short-term Load Forecasting Method Based on Deep LSTM RNN at Zone Level

ZHANG Yufan1 AI Qian1 LIN Lin2 YUAN Shuai2 LI Zhaoyu

(1.Key Laboratory of Control of Power Transmission and Conversion (Shanghai Jiao Tong University), Ministry of Education, Minhang District, Shanghai, China 200240)
(2.Shandong Power Dispatching & Control Center, Ji’nan, Shandong Province, China 250000)

【Abstract】Very short-term load forecasting provides a basis for a real-time electricity market. Improvement of its forecasting accuracy is important for revealing the uncertainties of the load and the deviations of day-ahead load forecasting results. Based on big data resources in power systems, a deep long short-term memory (LSTM) load forecasting method at a small area level is proposed, including preprocessing of input data, construction of a deep LSTM network, and search for hyper-parameters with the random search method. Under such hyper-parameters, comparison of forecasting performance between the proposed method and machine-learning method is conducted. Results prove that the proposed method can achieve better forecasting accuracy, which is suitable for offline training and online predicting. Also, through visualization of hidden-layer activation units and calculation of correlation, the ability of feature learning and correlation mining of a deep LSTM network is proven.

【Keywords】 very short-term load forecasting; deep LSTM; recurrent neural network (RNN) ; visualization; correlation analysis;

【DOI】

【Funds】 Science and Technology Foundation of SGCC (52060018000N)

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(Translated by HAN R)

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This Article

ISSN:1000-3673

CN: 11-2410/TM

Vol 43, No. 06, Pages 1884-1892

June 2019

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Article Outline

Abstract

  • 0 Introduction
  • 1 Process and data preprocessing of VSTLF
  • 2 Structure of load forecasting model based on deep LSTM
  • 3 Example analysis
  • 4 Experimental results
  • 5 Conclusion
  • Appendix A
  • References