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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

A hybrid deep learning and clonal selection algorithm-based model for commercial building energy consumption

¹Moscow Institute of Aeronautics and Technology, Anyang Institute of Technology, Anyang, China.

Science Progress

|September 28, 2024

Summary

This study introduces a hybrid deep learning model for commercial building energy consumption prediction. The novel CNN-GRU-CSA Network (CGC-Net) significantly improves prediction accuracy and efficiency, aiding sustainable energy management.

Keywords:

Commercial buildings building energy management deep learning models energy consumption prediction energy saving strategies optimization sustainability

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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Published on: July 3, 2020

Area of Science:

Energy Management
Artificial Intelligence
Sustainable Development

Background:

Commercial buildings are major energy consumers, posing environmental challenges.
Traditional energy management methods lack accuracy and applicability.
Accurate energy consumption prediction is vital for sustainable development.

Purpose of the Study:

To propose a hybrid deep learning model for commercial building energy consumption prediction and energy-saving strategies.
To enhance the accuracy and efficiency of energy consumption predictions.
To provide technical support for commercial building energy management.

Main Methods:

Integration of Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and the Clonal Selection Algorithm (CSA).
Development of the CNN-GRU-CSA Network (CGC-Net) model.
Validation on multiple benchmark datasets: BDGP, CBECS, NEPB, and BEBDEE.

Main Results:

CGC-Net achieved low Mean Absolute Errors (MAE) across datasets (15.94-17.12).
The model significantly outperformed traditional methods and other deep learning models.
Demonstrated faster training and inference times compared to existing approaches.

Conclusions:

The CGC-Net model offers a stable and superior solution for commercial building energy management.
The hybrid deep learning approach provides innovative solutions for energy efficiency.
This research offers essential technical support for optimizing energy consumption in commercial buildings.