September 16, 2025Applied SciencesOpen Access

Hybrid ML/DL Approach to Optimize Mid-Term Electrical Load Forecasting for Smart Buildings

View Full Paper

Ask AI

Bookmark

Authors

AHAyaz HussainUniversity of Bergamo GFGiuseppe FranchiniUniversity of Bergamo MAMuhammad AkramUniversity of Sannio

Discussion

Member takes

Implication

This analysis demonstrates improved energy load forecasting accuracy in smart buildings using machine learning and deep learning models, indicating efficient resource management.

Key Points

The hybrid model combining FireNet and XGBoost achieved the highest forecasting accuracy for smart building energy usage.
Forecasting accuracy was evaluated using metrics such as mean squared error (MSE) and root mean squared error (RMSE).
The study utilized a hybrid two-layer architecture to leverage complementary strengths of various machine learning and deep learning techniques.
Findings emphasize the importance of accurate energy load forecasting in promoting sustainable practices in the building sector.

Cite This Study

Hussain et al. (2025) studied this question.

synapsesocial.com/papers/68d453a431b076d99fa59ac7 https://doi.org/10.3390/app151810066

View Full Paper

Ask AI

Bookmark

Implication

This analysis demonstrates improved energy load forecasting accuracy in smart buildings using machine learning and deep learning models, indicating efficient resource management.

Hybrid ML/DL Approach to Optimize Mid-Term Electrical Load Forecasting for Smart Buildings

Discussion

Member takes

Implication

Key Points

Cite This Study

Hybrid ML/DL Approach to Optimize Mid-Term Electrical Load Forecasting for Smart Buildings

Discussion

Member takes

Implication

Key Points

Cite This Study

Also Consider

Also Consider