There is not discussion about the need of energy conservation, it is well known that energy resources are limited moreover the global energy demands will double by the end of 2030, which certainly will bring implications on the environment and hence to all of us. Non-Intrusive load monitoring (NILM) is the process of recognize electrical devices and its energy consumption based on whole home electric signals, where this aggregated load data is acquired from a single point of measurement outside the household. The aim of this approach is to get optimal energy consumption and avoid energy wastage. Intrusive load monitoring (ILM) is the process of identify and locate single devices through the use of sensing systems to support control, monitor and intervention of such devices. The aim of this approach is to offer a base for the development of important applications for remote and automatic intervention of energy consumption inside buildings and homes as well. Appliance discerns can be tackled using approaches from data mining and machine learning, finding out the techniques that fit the best this requirements, is a key factor for achieving feasible and suitable appliance load monitoring solutions. This paper presents common and interesting methods used. Privacy concerns have been one of the bigger obstacles for implementing a widespread adoption of these solutions. The implementation of security over these approaches along with fine-grained energy monitoring would lead to a better public agreement of these solutions and hence a faster adoption of such approaches. This paper reveals a lack of security over these approaches with a real scenario.
 
