# Sustainable Building Tool by Energy Baseline: Case Study
## 2 Methodology
### Energy Value Model and Measured Value Ratio  
In the pursuit of enacting an energy baseline (EnBL) through the utilization of the absolute consumption model and specific energy consumption, a recommended methodology involves the employment of statistical analyses founded on time series. A time series, in this context, denotes an assemblage of numerous observations pertaining to a variable measured at consecutive temporal intervals or successive time periods [27].  
The primary objective of a time series resides in the discernment of discernible patterns within historical data, subsequently extrapolating these patterns into prospective temporal domains. This prognostication is predicated solely upon past values of the variable or preceding forecast errors. The intricate composition of a time series data point's pattern or performance emanates from diverse components, typically encompassing trend, cyclical, seasonal, and irregular elements.  
Within the framework of a time series, several forecast models are available, depending on the historical data available, including the average historical monthly consumption, weighted average of historical monthly consumption, seasonality indexes, and order moving average, considering the average daily consumption trend.  
\begin{table}
\begin{tabular}{c c c} \hline
**EnBL Types** & **Strengths** & **Weaknesses** \\ \hline \multirow{6}{*}{Statistical model} & Needs a system with one or more relevant & \multirow{2}{*}{In many cases, the effect between the relevant variables and energy consumption.} & \\  & Determines the improvement in energy & & consumption is not considered. The \\  & performance under comparable conditions. & & limitation of the statistical regression \\  & Commonly uses linear regression models & & model is that it is only valid when certain \\  & with one or more relevant variables to & & assumptions are fulfilled. \\  & monitor energy performance. & & \\ \hline \multirow{6}{*}{Engineering or simulation model} & Evaluates energy performance where energy & \multirow{2}{*}{They need to be well calibrated; the} & \\  & use and relevant variables are multiple. & & models can provide a standardized basis \\ \cline{1-1}  & Useful in a design stage to conceptually & & for determining energy performance \\ \cline{1-1}  & optimize the energy performance of simple & & improvement. \\ \cline{1-1}  & or complex systems and facilities. & & \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._A pivotal aspect of this process is the establishment of error measures, serving as evaluative tools to scrutinize the soundness of the executed forecasting model. These error measures facilitate the precision assessment of these forecasting methods, guiding the selection of the most optimal one [28; 29]. The error associated with any forecast is the disparity between the observed value in the time series and the forecast itself. This forecast error may manifest as positive or negative, which is contingent upon whether the forecast overestimates or underestimates the actual value. The computation of forecast errors [30] typically follows Equation (1).  
\[Forecast\ error=Actual\ demand-forecast\ value \tag{1}\]  
Figure 2: Methodology for LBE model selection.  
This formula facilitates informed decisions regarding the optimal forecasting method. It empowers the identification of anomalies or discrepancies in our demand forecast, enabling timely adjustments and redirection toward more judicious choices.  
The Mean Absolute Percentage Error (_MAPE_) serves as a metric quantifying the deviation in percentage terms. It represents the mean absolute error, elucidating the variance between the actual demand and the forecasted values. This metric, also recognized as Percentage Error Mean Absolute (PEMA) (Equation (2)), stands as a robust tool for evaluating the precision of forecasting methodologies.  
\[MAPE=\frac{\frac{\sum_{i=1}^{n}|Actual_{i}-Forecast_{i}|}{ Actual_{i}}}{n} \tag{2}\]