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A Dendritic Neural Network-Based Model for Residential Electricity Consumption Prediction

Author

Listed:
  • Ting Jin

    (School of Management Science and Engineering, Nanjing Univerity of Information Science and Technology, Nanjing 210044, China
    College of Science, Nanjing Forestry University, Nanjing 210037, China)

  • Rui Xu

    (College of Science, Nanjing Forestry University, Nanjing 210037, China)

  • Kunqi Su

    (College of Science, Nanjing Forestry University, Nanjing 210037, China)

  • Jinrui Gao

    (Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan)

Abstract

Residential electricity consumption represents a large percentage of overall energy use. Therefore, accurately predicting residential electricity consumption and understanding the factors that influence it can provide effective strategies for reducing energy demand. In this study, a dendritic neural network-based model (DNM), combined with the AdaMax optimization algorithm, is used to predict residential electricity consumption. The case study uses the U.S. residential electricity consumption dataset.This paper constructs a feature selection framework for the dataset, reducing the high-dimensional data to 12 features. The DNM model is then used for fitting and compared with five commonly used prediction models. The R 2 of DNM is 0.7405, the highest among the six models, followed by the XGBoost model with an R 2 of 0.7286. Subsequently, the paper leverages the interpretability of DNM to further filter the data, obtaining a dataset with 6 features, and the R 2 on this dataset is further improved to 0.7423, resulting in an increase of 0.0018.

Suggested Citation

  • Ting Jin & Rui Xu & Kunqi Su & Jinrui Gao, 2025. "A Dendritic Neural Network-Based Model for Residential Electricity Consumption Prediction," Mathematics, MDPI, vol. 13(4), pages 1-23, February.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:4:p:575-:d:1587262
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