# Deducing Energy Consumer Behavior from Smart Meter Data +
## 1 Introduction  
The deployment of smart electricity main meters for households has been tremendously growing in the last few years. For instance, China has nearly 210 million units installed [1], the United States (US) has already deployed more than 60 million meters [2], and the European Union (EU) is planning to replace at least 72% of the household electricity meters by 2020 (around 200 million smart meters will be deployed) [3]. Meters are not only used to provide electricity billing but also to deliver value-added services to the power grid. Such services are developed to provide energy utility with diagnostic information about the distribution grid [4, 5] or by permitting third-party application providers to deliver grid-related information services to residential homes [6]. Consequently, several types of research make use of the provided meter data and apply machine learning algorithms to it [7, 8]. Such algorithms are called Non-Intrusive Load Monitoring (NILM) algorithms and aim to disaggregate loads for individual appliances from the total power consumption data. Numerous NILM techniques are proposed in the literature [9]. In this context, the computer-aided design should be fruitfully applied to support load disaggregation algorithms as well as to combine them with different techniques to achieve better detection results and present the analyzed results in a user-friendly manner. This work rests on recent advancements in NILM based on smart meter data readings, data recovery mechanisms and model-to-text transformation techniques for presenting the final results in a user-friendly way. We propose a framework for deducing the user's behavior from the electricity usage fingerprints resulting from activities in the residential home. Figure 1 shows the conceptual architectureof the proposed framework; numerical labels are used to show the items to be explained. Initially, the power signature of each home appliance is measured individually by the main meter and stored in a shared database; the database is called DataBase Cloud Service (DBCS) [10]. The stored signatures will be used in the training of the NILM algorithms. NILM algorithm will look for such signatures in the aggregated meter data of the overall household consumption. However, such aggregated data may be faulty due to communication and/or computation inefficiencies. Therefore, quality check of the aggregated data is necessary (Label 1). In case of poor data quality, data recovery mechanisms are used to fix the aggregated data (Label 2). The recovered data is then stored in the DBCS and Non-Intrusive Load Monitoring (NILM) algorithms are applied to deduce the used appliances from the data (Label 3). The results of the algorithms will subsequently be stored in the DBCS. However, the disaggregation results are fallible due to the effect of the noise in the transmission lines and the energy consumption of unknown appliances. Therefore, outcome refinement step (Label 4) is used to observe common energy consumption pattern between multiple homes. The result of this process is appliance usage pattern, that can be used by the service providers to derive various statistical properties. Finally, for making the results comprehensible to the end-users, a natural language generator tool (Label 5) is developed to convert the processed data into textual Human-readable reports. The key novelty of our research comes from the combination of data recovery mechanisms, machine-learning techniques for event recognition with a subsequent analysis and translation of information into natural language. After an initial training period, the responses to the actual user behavior can be delivered in near real-time. The immediate benefit of the proposed framework stems from the fact that an observer no longer needs to be a skilled technician but instead can rely on comprehensible reports on user behavior.