# A Data-Driven Model for the Energy and Economic Assessment of Building Renovations
## 3 Results
### Economic Analysis of Retrofit Interventions  
The economic analysis of retrofit interventions represents a critical component of our data-driven methodology, translating energy efficiency improvements into financial metrics that stakeholders can use for decision making. This phase builds upon the feature importance and clustering analyses to evaluate the cost-effectiveness of various retrofit measures across different building typologies. For each building cluster identified in the previous phase, a comprehensive cost-benefit analysis of five key retrofit interventions was performed: Wall Insulation, Window Replacement, HVAC System Upgrades, Solar Panel Installation, and Smart Home Systems. The analysis incorporated actual cost data from [29], which included average costs per square meter, expected lifespans, and energy-saving percentages for each intervention. To ensure comparability across different building types and intervention scales, all cost-related metrics were normalized for visualization purposes (Algorithm 1). Figure 4: Building clusters based on energy consumption and characteristics. ```
**Algorithm 1**Economic Analysis of Retrofit Interventions
``` Input: B = {b\({}_{1}\), b\({}_{2}\),..., b\({}_{n}\)}: Set of buildings with characteristics and energy consumption data M = {m\({}_{1}\), m\({}_{2}\),..., m\({}_{k}\)}: Set of retrofit measures with associated parameters E = {r, p}: Economic parameters where r is discount rate and p is energy price Output: R: Economic indicators (NPV, ROI, payback period) for each measure by cluster Procedure:
1: function CalculateRetrofitEconomics(B, M, E)
2: R \(\leftarrow\)\(\bigcirc\)\(\blacktriangleright\) Initialize empty results collection
3: r \(\leftarrow\) E.discount_rate \(\blacktriangleright\) Extract discount rate
4: p \(\leftarrow\) E.energy_price \(\blacktriangleright\) Extract energy price
5: C \(\leftarrow\) ClusterBuildings(B) \(\blacktriangleright\) Group buildings into clusters
6: for each cluster c in C do
7: Ec = mean(energy_consumption(c)) \(\blacktriangleright\) Average energy consumption
8: Ac = mean(building_area(c)) \(\blacktriangleright\) Average building area
9: Rc \(\leftarrow\)\(\bigcirc\)\(\blacktriangleright\) Initialize results for this cluster
10: for each measure m in M do
11: id \(\leftarrow\) m.identifier
12: cost\({}_{0}\) = m.cost_per_m\({}^{2}\)\(\times\) Ac \(\blacktriangleright\) Initial investment cost
13: \(\eta\) = m.energy_saving_percentage / 100 \(\blacktriangleright\) Energy saving efficiency
14: s_energy \(\leftarrow\) Ec \(\times\)\(\eta\)\(\times\) Ac \(\blacktriangleright\) Annual energy savings (kWh)
15: s_annual = s_energy \(\times\) p \(\blacktriangleright\) Annual monetary savings (\(\blacktriangleright\))
16: L = m.expected_lifespan \(\blacktriangleright\) Lifespan in years
17: NPV = \(-\)cost\({}_{0}\)\(\blacktriangleright\) Begin with negative investment
18: for t \(\leftarrow\) 1 to L do
19: NPV = NPV + s_annual / (1 + r)\({}^{\text{t}}\blacktriangleright\) Add discounted annual savings
20: end for
21: ROI = NPV / cost\({}_{0}\)\(\blacktriangleright\) Return on investment ratio
22: PP = cost\({}_{0}\) / s_annual \(\blacktriangleright\) Payback period in years
23: Rc[id] = (NPV, ROI, PP, cost\({}_{0}\), s_annual, L)
24: end for
25: R[c] = Rc
26: end for
27: return R
28: end function
29: function RankInterventionsByMetric(R, metric)
30: T \(\leftarrow\)\(\bigcirc\)\(\blacktriangleright\) Initialize ranking result
31: for each cluster c in R do
32: Rc = R[c]
33: if metric \(\in\) [NPV, ROI] then
34: Tc = sort(Rc, metric, descending) \(\blacktriangleright\) Higher values are better
35: else
36: Tc = sort(Rc, metric, ascending) \(\blacktriangleright\) Lower values are better
37: end if
38: T[c] = Tc
39: end for
40: return T
41: end function ```  
**Algorithm 1**Economic Analysis of Retrofit Interventions  
Three primary economic indicators were calculated as shown in Algorithm 1 (Figure 5):  
1.