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Household Energy Consumption Prediction: A Deep Neuroevolution Approach

Authors:

Alexander Soudaei,

Mikael Tsechoev,

Ahmed OsmanAuthors Info & Claims

AI2A '23: Proceedings of the 2023 3rd International Conference on Artificial Intelligence, Automation and Algorithms

Pages 164 - 168

https://doi.org/10.1145/3611450.3611474

Published: 20 August 2023 Publication History

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Abstract

Accurate energy consumption prediction can provide insights to make better informed decisions on energy purchase and generation. It also can prevent overloading and make it possible to store energy more efficiently. In this work, we propose a new deep learning model to predict the household energy consumption. In the new model, we employ differential evolution (DE) algorithm to automatically determine the optimal architecture of the deep neural network. The energy prediction results are presented and analyzed to show the effectiveness of the deep neuroevolution model constructed.

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Cited By

Han YHao YFeng MChen KXing RLiu YLin XMa BFan JGeng Z(2024)Novel STAttention GraphWaveNet Model for Residential Household Appliance Prediction and Energy Structure OptimizationEnergy10.1016/j.energy.2024.132582(132582)Online publication date: Jul-2024
https://doi.org/10.1016/j.energy.2024.132582

Index Terms

Household Energy Consumption Prediction: A Deep Neuroevolution Approach
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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cover image ACM Other conferences

AI2A '23: Proceedings of the 2023 3rd International Conference on Artificial Intelligence, Automation and Algorithms

July 2023

199 pages

ISBN:9798400707605

DOI:10.1145/3611450

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

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Published: 20 August 2023

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AI2A '23

AI2A '23: 2023 3rd International Conference on Artificial Intelligence, Automation and Algorithms

July 21 - 23, 2023

Beijing, China

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Cited By

Han YHao YFeng MChen KXing RLiu YLin XMa BFan JGeng Z(2024)Novel STAttention GraphWaveNet Model for Residential Household Appliance Prediction and Energy Structure OptimizationEnergy10.1016/j.energy.2024.132582(132582)Online publication date: Jul-2024
https://doi.org/10.1016/j.energy.2024.132582

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