## 4 Case Study
### The Energy Consumption Forecasting  
To test the expert system-based method from the forecasting module, the working time and operating characteristics of the equipment and installations were identified based on the analysis of current and voltage measurements done for 1 month, in 2019, with the power analyser [PERSON], using a sampling step by 5 s. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Cluster** & **JAN** & **FEB** & **MAR** & **APR** & **MAY** & **JUN** & **JUL** & **AUG** & **SEP** & **OCT** & **NOV** & **DEC** \\ \hline
**C1—medium** & 14 & 20 & 29 & 5 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
**C2—high** & 10 & 40 & 48 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
**C3—small** & 10 & 0 & 5 & 50 & 67 & 35 & 48 & 55 & 60 & 57 & 10 & 6 \\
**C4—small** & 19 & 0 & 0 & 20 & 29 & 50 & 30 & 45 & 15 & 26 & 0 & 6 \\
**C5—medium** & 43 & 5 & 14 & 25 & 5 & 15 & 22 & 0 & 25 & 17 & 90 & 89 \\
**C6—high** & 5 & 35 & 5 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline
**Intensity** & & & & & & & & & & & & & \\ \hline
**Small** & 29 & 0 & 5 & 70 & 95 & 85 & 78 & 100 & 75 & 83 & 10 & 11 \\
**Medium** & 57 & 25 & 43 & 30 & 5 & 15 & 22 & 0 & 25 & 17 & 90 & 89 \\
**High** & 14 & 75 & 52 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
**Total** & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The measurement of the intensity in each month, in (%). Figure 12: The typical energy consumption profiles associated with each cluster. The measurement equipment was installed in the general electric panel on the main feeder and secondary cables, which feeds each local electric panel. As an example, the consumption profile assigned to a day from the analysed period, recorded on the main feeder, with the identification of different equipment and installations on which it operated, is presented in Figure 13. The main operations (tinsmith, mechanical repairs, and car dyeing), highlighted with red circles, associated with the activities of the technological process can be identified inside the profile through the equipment and installations (air compressor, welding machine, and dyeing installation with the dyeing and drying phases), relieved with yellow colour. The working time depends on the following factors: the body types (hatchback, sedan, MUV/SUV, coupe, convertible, wagon, van, or jeep), the thickness of sheet metal, and the number of components which must be dyed. The analysis of working time corresponding to each equipment and installation led at the following shares from the total energy consumption: 50.1% dyeing installation, 20.5% air compressor, 16.2% lighting system, 7.2% other electric equipment (vehicle lift, welding machine, hammer drills, blowers, etc. ), and 5.9% reception office, see Figure 14.  
Figure 14: Daily electricity consumption for each equipment and installation in analysed period. Figure 13: The consumption sampled at 5 s in a day with high intensity of the activity. In the weekend periods, or when the equipment or installations did not operate, there is no energy consumption. In addition, all profiles led to the division of the energy consumptions in three categories (small, medium, and high) considering the working time (reduced, normal, and long) for each equipment and installation, depending on the factors listed above, see Table 8. The first eight positions of the table correspond to the phases of dyeing installation, which have operating regimes with different values of working times and energy consumptions according to the components (doors, tailgates, fenders, bumpers, etc.) or the whole body of the car. This information helps the Decision-Maker to complete the equipment and installations matrix (EIM) associated with the system expert-based aggregation method. The Decision-Maker can complete the matrix taken into account the type of operations for each programmed car in the next day.