# Evaluation of Features Generated by a High-End Low-Cost Electrical Smart Meter
## 3 Experimental Methodology and Setup
### Data Retrieval  
The retrieval of the data from the platform is performed through HTTP Requests. The features generated by the smart meter that are used for training the models are the following:  
* pwr: Active power;
* apwr: Apparent power;
* rpwr: Reactive power;
* cur: Current;
* angle: Angle between _V&I_;
* scre: Crest factor.  
These features are selected because of their importance in identifying appliances. Other features generated by the smart meter, such as energy, may be useful for different kinds of problems, such as regression. Additionally, the line voltage and the line frequency remain almost fixed and thus do not contribute much to the differentiation among appliances.  
To export the data, a Jupyter Notebook file was created. First, the POST HTTP method is invoked, which sends data to a server to create a resource. To request the data from the specified resource, we use the HTTP method GET parameterized by the address of the website, the identity of the smart meter, the features we are interested in as well as the time interval that interests us. The online platform is tuned so that the user can export and store up to 50.000 measurements at a time. This number corresponds to approximately 15 min of measurements and therefore, if the user chooses to retrieve data for a bigger  
Figure 8: Filters.  
amount of time, this interval is divided into subsections of 15 min and the retrieval process is repeated for each of these subranges. The retrieved data are saved in a dataframe, which is then converted to a CSV file. Five appliances were selected for experimentation. These devices are quite common in households and correspond to a considerable part of the total consumption of a typical house. These are a vacuum machine, an electric oven, an air-condition, an electric stove, and an ironing press. To measure the appliances during their function, we used only the din-rail meter and identified a time period when no other device changed its status of operation in the house. We adopted this approach since we did not want to use additional smart plugs that would provide us with a clean and simple signal of each appliance. Each appliance was operated for 15 min and then it was turned off. This means that the data used by the models, were obtained from different time periods that were not continuous. Figures 9-18 depict the consumption behavior of each appliance.  
In Figure 9, the active/reactive and apparent power of the vacuum cleaner is depicted during the start of the operation focusing at the transient behavior until the steady state is reached. In Figure 10, the total active/reactive and apparent power is depicted during the 15 min interval that the vacuum cleaner is functioning. In Figures 11-18 the same quantities are depicted for the the other devices as well.  
Figure 11: Oven’s opening.  
Figure 10: Vacuum’s total consumption from the beginning of its function until the end. Vacuum operated in a time period where no other device changed the state.  
Figure 12: Overn’s behavior. The oven operated in a time period where no other device changed the state.  
Figure 13: Air-conditioner’s opening.  
Figure 14: Air-conditioner’s behavior. The air-conditioner operated in a time period where no other device changed the state.  
Figure 15: Stove’s opening.  
Figure 16: Stove’s behavior. The stove operated in a time period where no other device changed the state.  
Figure 17: Press’ opening.