## 4 Conclusions  
Buildings represent one of the major energy consumers and contributors of CO\({}_{2}\) emissions around the world. Therefore, retrofitting existing buildings to achieve ZEBs by minimizing their energy consumption and satisfying part of their energy needs is of utmost importance. In this paper, a framework for building retrofitting was presented. The framework covers data collection (energy retrofitting measures, LCC calculation, etc. ), mathematical programing, energy consumption forecasting, as well as ranking of retrofitting plans using the AHP. The framework's objective is to provide decision makers with the necessary steps to conduct a successful energy retrofitting project. To show the application of the framework, a case study pertaining to a residential building in Saudi Arabia was conducted. The case study generated four retrofitting scenarios, and the ranking showed that plans 4 and 3 are the best ones in terms of the decision maker's considered objectives: budget, energy savings, payback period, and ROI. The following recommendations can be considered for future research:  
* Applying the framework to a more sophisticated case study;
* Considering methods other than building simulation (i.e., artificial intelligence) for building energy consumption forecasting;
* Considering more building occupants' comfort measures, such as acoustic comfort and indoor air quality;
* Considering ranking methods other than the AHP (i.e., TOPSIS) for the retrofitting plans' ranking;
* Considering other multi-criteria decision-making methods to identify the best retrofitting plan, such as goal programing. **Funding:** APC was funded by Alfaisal University. **Acknowledgments:** This work was supported by Alfaisal University. **Conflicts of Interest:** The author declares no conflict of interest. \begin{table}
\begin{tabular}{c c} \hline \hline
**Plan** & **Overall Score** \\ \hline Plan 1 & 0.314 \\ Plan 2 & 0.396 \\ Plan 3 & 0.223 \\ Plan 4 & 0.066 \\ \hline \hline \end{tabular}
\end{table} 
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