What question did this study set out to answer?

To improve energy efficiency and reduce costs in smart buildings through a multi-objective optimisation model.

A Holistic Multi‐Objective Optimisation Model for Energy Efficiency Improvement and Cost Savings in Smart Buildings

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Randomized trial shows improved energy efficiency in smart buildings, indicating potential cost savings.

To improve energy efficiency and reduce costs in smart buildings through a multi-objective optimisation model.
Developed a holistic framework integrating multiple energy-saving technologies and demand-side efficiency programmes.
Used deep-learning for 24-hour weather forecasting and calculated electricity generation via System Advisor Model (SAM).
Optimised the model using the epsilon-constraint method and CPLEX solver in a GAMS environment.
The BEEI index significantly increased from 2.36% to 37.40% after implementing energy-saving programmes.
Total annual cost increased from $34,719 to $37,362.

Rajaei et al. (2026) studied this question.

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Bookmark

View Full Paper

Ask AI

Bookmark

Authors

Randomized trial shows improved energy efficiency in smart buildings, indicating potential cost savings.

To improve energy efficiency and reduce costs in smart buildings through a multi-objective optimisation model.
Developed a holistic framework integrating multiple energy-saving technologies and demand-side efficiency programmes.
Used deep-learning for 24-hour weather forecasting and calculated electricity generation via System Advisor Model (SAM).
Optimised the model using the epsilon-constraint method and CPLEX solver in a GAMS environment.
The BEEI index significantly increased from 2.36% to 37.40% after implementing energy-saving programmes.
Total annual cost increased from $34,719 to $37,362.

Rajaei et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark