Journal of Shandong University(Engineering Science) ›› 2021, Vol. 51 ›› Issue (2): 90-97.doi: 10.6040/j.issn.1672-3961.0.2020.226

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Model and application of short-term electricity consumption forecast based on C-LSTM

LIAO Jinping1, MO Yuchang1, YAN Ke2   

  1. 1. College of Mathematical Sciences, Huaqiao University, Quanzhou 362000, Fujian, China;
    2. College of Information Engineering, China Jiliang University, Hangzhou 310000, Zhejiang, China
  • Published:2021-04-16

Abstract: Research on short-term household electricity consumption prediction based on long-term short-term memory recurrent neural network under deep learning. This research introduced a hybrid deep neural network model C-LSTM that combines convolutional neural network(CNN)and long short term memory(LSTM)models, and proposed a multi-step prediction strategy based on this model. According to the research on the daily electricity consumption data set of 5 real households, C-LSTM realized household electricity demand forecasting in 5 min. Through continuous modification of model parameters and improvement of the model, from the analysis of the three error indicators provided in this study, the prediction accuracy of C-LSTM was higher than the autoregressive integrated moving average model, support vector regression model and LSTM model. The main basis for the evaluation of the model prediction effect in this study was the average absolute percentage error value. From the test results, it could be obtained that the C-LSTM model's household electricity demand forecast in 5 minutes was 4.63% higher than the support vector regression model, 22.8% higher than the LSTM, and 34.74% higher than the autoregressive integrated moving average model. Therefore, the C-LSTM model provided a guarantee for the smart grid's accurate and timely prediction of household-level electricity demand, and had an important impact on promoting the widespread popularity of personalized electricity packages and reducing energy waste.

Key words: short-term household electricity forecast, multi-step forecasting, convolutional neural network, long short term memory model, hybrid deep learning neural network

CLC Number: 

  • TP18
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