Version 1
: Received: 30 August 2024 / Approved: 2 September 2024 / Online: 2 September 2024 (16:59:53 CEST)
How to cite:
Ibude, F. E.; Otebolaku, A.; Ameh, J.; Ikpehai, A. Multi-timescale Energy Consumption Management in Smart Buildings Using Hybrid Deep Artificial Neural Networks. Preprints2024, 2024090130. https://doi.org/10.20944/preprints202409.0130.v1
Ibude, F. E.; Otebolaku, A.; Ameh, J.; Ikpehai, A. Multi-timescale Energy Consumption Management in Smart Buildings Using Hybrid Deep Artificial Neural Networks. Preprints 2024, 2024090130. https://doi.org/10.20944/preprints202409.0130.v1
Ibude, F. E.; Otebolaku, A.; Ameh, J.; Ikpehai, A. Multi-timescale Energy Consumption Management in Smart Buildings Using Hybrid Deep Artificial Neural Networks. Preprints2024, 2024090130. https://doi.org/10.20944/preprints202409.0130.v1
APA Style
Ibude, F. E., Otebolaku, A., Ameh, J., & Ikpehai, A. (2024). Multi-timescale Energy Consumption Management in Smart Buildings Using Hybrid Deep Artificial Neural Networks. Preprints. https://doi.org/10.20944/preprints202409.0130.v1
Chicago/Turabian Style
Ibude, F. E., Jude Ameh and Augustine Ikpehai. 2024 "Multi-timescale Energy Consumption Management in Smart Buildings Using Hybrid Deep Artificial Neural Networks" Preprints. https://doi.org/10.20944/preprints202409.0130.v1
Abstract
Energy consumption is a critical issue in the energy sector, and recent events such as the global energy crisis, costs, the need to reduce greenhouse emissions, and extreme weather conditions have increased the demand for energy efficiency. Accurately predicting energy consumption is one of the key steps to addressing inefficiency in energy consumption, and its optimization. In this regard, accurate predictions of energy consumption would not only help to minimize wastage, but also to save cost. In this article, we propose intelligent models using an ensemble of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) neural networks for predicting energy consumption in smart buildings. The proposed model outperforms other state-of-the-art deep learning models for predicting minute energy consumption, with a mean square error of 0.109. The proposed model also accurately captures latent trends in the data that other models struggle to capture. The results highlight the potential of using hybrid deep learning models for improved energy efficiency management in smart buildings.
Keywords
Smart buildings; Energy consumption; Hybrid deep learning; Energy predictions; Building energy management systems
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
