A Dendritic Neural Network-Based Model for Residential Electricity Consumption Prediction
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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
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| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/4/575 |
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| Summary: | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> of DNM is 0.7405, the highest among the six models, followed by the XGBoost model with an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> of 0.7286. Subsequently, the paper leverages the interpretability of DNM to further filter the data, obtaining a dataset with 6 features, and the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> on this dataset is further improved to 0.7423, resulting in an increase of 0.0018. |
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| ISSN: | 2227-7390 |