# A Sensor Data-Driven Decision Support System for Liquefied Petroleum Gas Suppliers
## 5 Consumption Forecasts
### Short-Term LPG Consumption Prediction
#### 5.1.2 Application of Short-Term Prediction Results  
The result of the forecasting model operation is a predicted LPG consumption for a given tank. The predictions are then used to calculate the predicted gas level in a tank. Information on the action recommended for a given gas level are presented to a system operator and support their decisions. Two types of evaluation are introduced in the proposed system. The first type of evaluation supports the data scientist in deciding whether the quality of a given predictive model is acceptable, and the model can be implemented in the system operation. The performance of the prediction model can be evaluated by means of typical quality measures used in regression applications, e.g., root mean squared error (RMSE) or mean absolute error (MAE). Additionally, relative versions of these measures can be used (rRMSE and rMAE) in order to verify if the proposed models offer better results compared to the trivial approaches. The second type of evaluation supports the end user in decisions where risk evaluation is required (e.g., when the number of transport vehicles necessary on a given day exceeds the capacity available for a supplier). In such an approach, valuable information is introduced by an error range calculated for each prediction horizon on the basis of past predictions. The error range can assume the form of an error distribution or a confidence interval. Therefore, one of the approaches involves applying a boxplot representing historical error values on a predicted consumption plot. Another method is to plot the predicted LPG level within the confidence intervals calculated for a chosen confidence level. In both cases, the final user receives a visualization of the predicted gas level and a clearly plotted range that can be taken into consideration if needed. Examples of the boxplot visualization are presented in Figure 7. The plots were created for a single prediction in a seven day horizon. The gas level is reported as a percentage of the tank capacity. The predicted gas level is plotted as dots connected by a solid line for trend visualization. Each predicted value (a dot) is accompanied by the error range calculated on the basis of the predictions that were performed in a time range of 50 days preceding the given prediction. A dotted line at the gas level value equal to 0.2 indicates the level for which delivery should be made immediately. Figure 7: Error range visualized as a boxplot around the predicted gas level (dots) resulting in must go (**a**) and can go (**b**) recommendation for seven day prediction. Gas level is calculated as a percentage of the tank capacity, prediction horizon is presented in days. The predicted gas level in a tank and the possible error range are the pieces of information that support the final decision made by the user controlling the delivery of gas to customers. There are three action recommendations: must go, can go and do not go. The algorithm supporting a logistics specialist in planning deliveries to individual customers is based on current and forecasted gas levels in customer tanks. The forecasted level is based on the gas consumption forecast. Each tank has defined fuel level thresholds denoted as: \(HH\)--very high fuel level; \(H\)--high fuel level (the tank should be filled slightly above this value; \(L\)--low fuel level (delivery should be made immediately after the fuel level drops below \(L\); \(LL\)--too low fuel level; installation may be damaged). The recommendation algorithm supporting a logistics specialist in the planning of deliveries is parameterized, with the main algorithm parameter reflecting the level of risk accepted by the logistics specialist. There are two aspects to this risk. The first one corresponds to the situation when the delivery was sent too late and the tanker did not arrive at a customer who exceeded a drop below level \(L\).