Hybrid CNN-GA Framework for Optimized Energy Consumption Prediction in Public Buildings

DOI

10.2991/978-94-6463-718-2_59How to use a DOI?

Keywords

Hybrid CNN-GA; energy consumption prediction; public buildings; optimization; deep learning; genetic algorithm; machine learning; energy forecasting; citation; MLA style; interdisciplinary research; energy management

Abstract

A hybrid CNN-GA framework for predicting energy consumption of public buildings This study combines the deep learning capabilities of CNN with the optimization strengths of GA to achieve a higher prediction accuracy and execution efficiency in energy consumption forecasting. Utilising the malleable nature of the MLA citation style allows this research to branch out across a myriad of resources, ranging from academic articles, other online sources to various reports, in an effort to attain a well-rounded view of energy prediction process. This 9th edition follows a simple MLA format for reliable reference to electronic and non-electronic sources, thereby ensuring accuracy and reliability of the information used. Furthermore, the versatile nature of MLA formatting makes it applicable to various other studies/ victimology and cross-jurisdiction crimes, along with other ones such as hybrid models of machine learning and energy consumption optimization in training. We adopt as a model hybrid CNN-GA framework and empirically prove that this can both facilitate rapid improvement of energy management practice in the field of public buildings while offering a robust methodological base supported by a significant and clean citation base.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - E. Baby Anitha
AU  - U. Kasthuri
AU  - E. Nandhini
AU  - A. Imran
AU  - R. Karthick
AU  - K. Kesavan
PY  - 2025
DA  - 2025/05/23
TI  - Hybrid CNN-GA Framework for Optimized Energy Consumption Prediction in Public Buildings
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 683
EP  - 697
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_59
DO  - 10.2991/978-94-6463-718-2_59
ID  - Anitha2025
ER  -