# Sustainable Building Tool by Energy Baseline: Case Study
## 5 Discussion  
The quality of data and the varying levels of measurement present one of the most significant limitations in applying the proposed methodology for establishing an energy baseline in buildings. This limitation is closely tied to the degree of monitoring in buildings across different contexts and sectors. However, the methodology provides opportunities to estimate the baseline using available data resources, allowing for more accurate saving projections based on the level of monitoring and the inclusion of variables that may affect energy consumption.  
The choice of model depends on the availability of data, particularly the frequency and number of variables associated with consumption. For buildings that only have access to monthly billing data, a time series model is appropriate. In contrast, if real-time consumption data, weather variables, and occupancy-related factors are available, linear and multivariable regression models provide more accurate baseline estimates for forecasting energy consumption. This analysis aims to present a range of possibilities  
Figure 11: Electrical consumption breakdown of the building. Devices: 84,630.14 kWh, Lighting 10,014.03 kWh, Refrigeration: 37,466.96 kWh, Outdoor lighting: 1735.83 kWh.  
Figure 12: CO\({}_{2}\) emissions for the lecture hall building.  
by validating mathematical and statistical tools when selecting a model to establish an energy baseline. The developed models demonstrate that the choice of model influences the overestimation or underestimation of energy consumption forecasts, which in turn impacts saving calculations. Time series models are functional when only energy consumption data is available, serving as a starting point for building management. On the other hand, regression models yield more precise results for building operations by accounting for climate conditions, occupancy levels, equipment usage, and other factors.  
Statistical tests are essential to determine which model best describes and reflects the energy consumption trends in the facility. These tests also help assess whether the energy performance improvement percentages make sense in relation to the facility's operational patterns and the selected baseline reporting periods.  
The use of different models can lead to either underestimation or overestimation of energy savings, and statistical validation plays a critical role in selecting the model with the best predictive accuracy and lowest error. In this regard, the ability to use different models tailored to the needs and data resources of the building allows for the identification of initial savings, which can be adjusted as monitoring systems evolve and relevant variables and static factors affecting consumption are identified. The methodology enables the selection of a model according to the level of monitoring, which is crucial for the accuracy of saving estimates and their subsequent realization. The limitations observed in the four models proposed for baseline estimation are primarily associated with data quality, specifically data frequency and measurement gaps. These issues are recurrent in energy monitoring systems, often stemming from communication problems between meters and the management software. Challenges arise in defining the boundaries and ranges used to establish the baseline, which must account for building occupancy, seasonality, and the need to segment analysis periods based on the building's operational activities.  
Another key element is identifying the relevant variables that influence energy consumption. Statistical tests (e.g., \(p\) value criteria) can help determine whether these variables significantly impact energy consumption and should therefore be used for normalization. Statistical verification of error percentages and compliance with statistical assumptions is essential to ensure that the baseline is valid for setting energy saving targets and implementing reliable energy efficiency indicators.