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

Ting Jin (), Rui Xu, Kunqi Su and Jinrui Gao
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Ting Jin: School of Management Science and Engineering, Nanjing Univerity of Information Science and Technology, Nanjing 210044, 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

Mathematics, 2025, vol. 13, issue 4, 1-23

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.

Keywords: residential electricity consumption; dendritic neural network-based model; AdaMax optimization algorithm; machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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