# A Cyber-Physical Approach for Residential Energy Management: Current State and Future Directions
## 6 Data Analytics
### Monitoring Appliances  
The process of load monitoring facilitates identifying and monitoring main loads in the household [21; 25; 43]. There are two main categories to classify the methods to manage such processes: methods based on hardware and those based on software. In both, the goal is to recognize individual appliance loads through two stages, in addition to Data Acquisition: Feature extraction, and Classification [44; 45]. In feature extraction, an additional process is performed on the collected samples to obtain a signature that corresponds to the appliance electrical consumption. Lastly, in the classification stage, the resultant features are frequently classified through ML models. The use of ML techniques for predicting the behavior of appliances and translating raw data (e.g., current waveforms, voltage, and power) into an easy and understandable form, are common processes which both methods share [44]. On the other hand, the main difference lies in the Data Acquisition stage. Software-based methods (NILM) collect measurements from a single point of sensing (generally the smart meter device), while hardware-based methods (ILM) usually have more than two points of collection. NILM offers an attractive solution essentially due to its low-cost implementation.  
The feature extraction in NILM is mainly divided into two classes, namely, steady-state and transient features. Although these solutions have centered the attention of most studies in the field over the last five years, they have shown less precision and higher difficulty to its deployment in real-world scenarios compared to hardware-based methods. NILM's methodology is primarily based on event detection. It samples the aggregate signal to obtain individual signatures of electrical appliances. The aggregated power signal is usually very noisy, and therefore, only some electrical appliances can be detected, depending on the sampling frequency (e.g., oven, washing machine, air-conditioner, and EV) [23; 25].  
Figure 12: Data Analytics scheme in a HEMS.  
When facing these kinds of scenarios in terms of the type of appliance used, performance is still inconclusive on most common datasets. Techniques based on ILM consist of two sub-categories. One is the method in which energy appliances' power consumption is obtained using metering devices attached to appliances, also known as distributed sensing. The other is smart appliances (SA) which are devices with integrated capabilities to monitor and report their energy usage.  
In ILM, the feature extraction consists of the resultant unique vectors (e.g., sliding window) which are set as input of Machine Learning classifier models. Although the main constraint of ILM is cost, it provides greater efficiency and reliability in place of NILMs. Direct sensors have the sensing and controlling operation of various devices and appliances since they can be collocated (i.e., be placed next to the target appliance). An additional benefit is related to the classification stage. In this regard, the ILM appliance recognition system assigns a label that corresponds to the appliance being used. However, in the future, load monitoring techniques are expected to be hybrid, which means that it will combine the benefits of both NILM and ILM to make a more efficient HEMS [43]. Nowadays, smart appliances usage has been limited due to the high market prices and interoperability issues of these devices. Therefore, distributed sensing becomes an attractive solution for a massive deployment, but it needs to take advantage of communication technologies to allow the integration of all electrical devices.