# Deducing Energy Consumer Behavior from Smart Meter Data +
## 2 State of the Art  
While frameworks for Non-Intrusive Load Monitoring (NILM) by smart meter power consumption data forms a relatively new research field, diverse algorithms and tools have been presented to implement these frameworks. For instance, NILM-Toolkit (NILMTK) [11] is a framework aims to evaluate NILM solutions. It supports a broad range of known datasets that can be used to train or validate an algorithm. It comes with two benchmark algorithms to compare a new algorithm against. The NILMTK will help researchers to easily share and compare algorithms [11]. Another framework to accomplish better comparison and sharing is the NILM-eval framework. It is based on Matlab and supports algorithms to be written in either Matlab or Python. The NILM-eval and the NILMTK are created to solve the same problem and compatible with each other since their interfaces are the same [12]. Besides the simplicity stemming from only using smart meter data, research has also focused on the use of data with low time resolution in terms of seconds. [PERSON] et al. in [13] present a smart system for recognition of electrical appliance activities in real-time. The proposed system provides 84.6% accuracy in determining the set of appliances being used. However, [PERSON] et al. in [14] rely on data collected at 1/60 Hz, and they achieved a precision of 76.1% in detecting switching events. [PERSON] et al. in [15] present a load disaggregation technique that uses harmonic frequency signatures of appliances. Their technique allows to disaggregate the loads in a very short time (a quarter of a second) since it does not wait for appliance state change, as it was done in previous approach, and it monitors directly the frequency signatures. Data quality is an area that recently has become a hot topic due to the large quantity of data. Geographic information, a field that deals with citizen data and used for maps, weather prediction and climate research, comes up with several ways of describing quality in spatial data [16] and methods for defining quality in time series data [17]. Data quality may vary due to some facts, on the one hand, network suitability and the test equipment faults may cause gaps in the collected datasets and that significantly effect its quality. One way to deal with such gaps is to use mathematical gap filling techniques to come with a qualified guess on how the data would look like in the gap [18]. However, if the signal is so stochastic, the gap filling is not recommended [19]. On the other hand, power line noise may add a layer of random data into the datasets [20]. Digital filters are commonly used to solve this problem [21]. However, many researchers strive to make tools that can better analyze data quality in different areas. In the area of spatial data, a tool named \"_Quality and Workflow_\" [22] is developed to help researchers to select the best-suited data for a given data-driven project. It does this by looking at different quality criteria, given by the user or found in standards for spatial data. In the area of bioinformatics, a tool named QCSreen [23] is developed to help creating better dataset to metabolomics studies. In metabolomics studies, the datasets often created by joining information from several different experiments of various quality. In order to store and share datasets, seamless interfaces are desired by the smart grid research community. [PERSON] et al. in [24] define a service-oriented architecture for the collection of electricity data from resource constrained devices in residential homes. It presents the requirements for open protocols, privacy, security, and scalability, leading to usage of ZigBee and IPv6 over Low power Wireless Personal Area Networks (6 LoWPAN). In the same work, the REpresentational State Transfer(REST) principle is applied when designing a database cloud service and application layer protocols, providing storage for other elements in the architecture.