# Evaluation of Features Generated by a High-End Low-Cost Electrical Smart Meter
## 1 Introduction  
Easy to use and efficient energy disaggregation tools may be the holy grail of energy efficiency [1]. The commercial impact of energy disaggregation at the level of the home customers is the increased utility customer engagement and the reduced energy usage. The goal at this level is to itemize the consumer's energy bill [2], analyze the energy usage and cost per household appliance, make personalized and prioritized energy savings recommendations or even go as far as identify faulty appliances (e.g., frosting cycle of a fridge with a damaged seal is more frequent than a normal one [3]). All these should be viable through a single sensor per household that monitors the total energy consumption and other related quantities [4]. The requirement in energy disaggregation is to identify the usage of an appliance merely by its signature on the aggregate energy waveform of the household. The benefits to the consumer from using this technology have already been established [1, 3, 4]. The social impact of energy disaggregation is reflected on the permanent energy behavior shift of the users, in the reduced communication with the energy provider, in raising the awareness of the users and in providing the opportunity to low-income families to waste less energy. NILM-related products have already started to make their appearance [5], although the market is still at its infancy. Therefore, taking also into account that contemporary appliances can be remotely functioned, an increasing number of people are interested in learning their consumption profile. Moreover, an increasing need is arising for the user to turn from a passive consumer into an active consumer of electricity, whowill be fully aware of what he/she consumes. With respect to the consumer, his/her energy behavior is affected by raising inefficiencies that fall under the following 3 categories:  
* **Appliance-Related**: bad performance of an appliance, always-on appliances that are forgotten, low efficiency ratings of appliances, unreasonable large intervals to fall into stand-by-mode;
* **Dwelling-Related**: identification of poor insulation and its effects, geographic position of the dwelling;
* **Energy Use-Related**: how often and when major appliances are run. To fulfill this, it is necessary to fully record the behavior of all devices used by the user. However, this is not an easy task. One way would be to turn all devices into smart devices, which is very expensive and not environment-friendly and thus quite unlikely to be adopted at least for the near future. Another approach would be to place a smart meter on each device. As smart meters, we consider those which record the energy consumption of an electronic device at regular intervals and allow communication between the utility and the consumer. However, placing a smart meter behind every device is not an efficient way to solve the problem, as it is a difficult and complex process. A more efficient way, as already implied in the previous discussion, is to install a meter on the main panel of the user's home to record the total energy consumption. This is a Non-Intrusive method of Load Monitoring (NILM). In NILM, we attempt to measure total energy rather than using individual meters to measure the energy of each device. NILM can be roughly decomposed into the following steps (see [3; 6; 7]):  
1. **Load Monitoring--Data Acquisition:** Pre-processing of the raw data and computation of the necessary quantities;
2. **Event Detection:** The steady-state or transient changes that correspond to the operation of appliances are identified. Usually, event-based approaches are employed or state-based approaches [7]. The state-based approach uses each sample from the aggregated signal to detect events. They are mostly based on HMMs and state transitions are obtained automatically from the hidden state estimation.