# Estimating the Energy Savings of Energy Efficiency Actions with Ensemble Machine Learning Models
## 4 Case Study
### Data Set
It should be noted that the individual points that are located outside the whiskers of the box plot are possible outliers (an observation that is numerically distant from the rest of the data). In Figure 3a, we observe that the most costly investment categories are the building fabric measures and the HVAC plant, while on the other hand the heating, lighting, and compressed air actions can be considered among the least expensive ones. In terms of energy savings, it is observed that building fabric measures are among the lowest performing categories despite the high investment cost they involve. On the contrary, waste heat actions bring on significant energy savings, as seen in Figure 3b. Finally, in Figure 3c, we present the index of investment \begin{table}
\begin{tabular}{c l c} \hline \hline
**Category** & **Type** & **Projects** \\ \hline \multirow{4}{*}{Heating} & Oil/gas fired hot water/steam boilers—operational optimization (controls or automation) & 26 \\  & Oil/gas fired hot water/steam boilers—technical optimization (component renewal or supplement) & 56 \\  & Oil/gas fired hot water/steam boilers—complete renewal & 12 \\  & Heating distribution system—optimization (insulation of pipes/fittings/containment/other surfaces) & 47 \\ \hline \multirow{2}{*}{Cooling} & Free cooling & 35 \\ \hline \multirow{4}{*}{Lighting} & Re-lamp to LED & 66 \\  & Technical optimization (component renewal or supplement) & 688 \\  & Operational optimization (controls or automation) & 400 \\ \hline \multirow{4}{*}{Compressed Air} & Compressor renewal & 23 \\  & Technical optimization (component renewal or supplement) & 457 \\  & Operational optimization (controls or automation) & 653 \\  & Distribution system optimization & 20 \\ \hline \multirow{2}{*}{Waste Heat} & Heat from processes & 97 \\  & Heat from cross-cutting technologies & 91 \\ \hline \multirow{4}{*}{Pumps} & Operational optimization (control or automation) & 29 \\  & Technical optimization (component renewal or supplement) & 16 \\  & Complete renewal & 29 \\ \hline \multirow{4}{*}{Building Fabric} & Wall insulation & 17 \\  & Roof insulation & 54 \\  & Glazing upgrade & 56 \\ \hline \multirow{4}{*}{HVAC Plant} & Boiler/furnace replacement or upgrade & 287 \\  & Chiller plant replacement or upgrade & 2 \\ \cline{1-1}  & Pipe insulation & 6 \\ \cline{1-1}  & Ventilation upgrade & 950 \\ \cline{1-1}  & Installation or replacement of BEMS & 1 \\ \cline{1-1}  & Drinking/tap water heating & 59 \\ \cline{1-1}  & District heating supply & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The formulated investment categories and investment types per category for the EE investments used in the case study. For each type of investment, the number of available projects is also displayed. efficiency defined as the ratio of investment cost per energy savings (EUR/kWh). Effectively, this index indicates which categories are more effective in the sense that they result in great energy savings when compared to the corresponding cost of investment. According to this index, and as expected from our previous observations, building fabric measures is the least efficient category, while several investments in HVAC plants, lighting, and pumps have also low efficiency. On the other hand, compressed air, heating, and waste heat investments can be ranked among the most effective categories of measures. Figure 3: Comparative analysis of the 8 investment categories in terms of cost, energy savings, and investment efficiency (defined as investment cost per energy savings). (**a**) Investment cost per category. (**b**) Energy savings per category. (**c**) Investment efficiency per category.