# Power Profiling of Smart Grid Users Using Dynamic Time Warping +
## 1 Introduction  
With the advent of smart meters and their advanced features, load data can now be analyzed more quickly and accurately in the smart grid. Advanced metering infrastructure (AMI), as a core technology, provides bidirectional information flow between utility providers and consumers. It accesses each individual location in real time and uploads a vast amount of data to the smart grid [1, 2]. These data allow for improved management of utility assets as well as demand response (DR) systems [3]. Furthermore, it reveals valuable data for profiling consumers' power consumption and analyzing their usage behavior [4]. Power consumption is a critical variable for expansion planning, load forecasting, performance analysis, and demand management in smart grids [5, 6, 7]. Analyzing consumers' power consumption behavior, known as power profiling, is essential for demand forecasting and optimizing resource allocation. The power profile of a consumer reveals their power consumption patterns over a specific period and serves as a valuable tool forvarious smart grid applications, such as DR and load forecasting [8; 9]. Various methods have been employed to extract and classify patterns for power profiling, often relying on representative data and statistical averages of electrical appliance usage [10]. In reality, a power profile is influenced not only by the load patterns of electrical appliances but also by consumers' consumption behavior and contextual factors such as time, location, and environment [10; 11; 12; 13]. Given these factors, comprehensive power profiling is categorized into three concepts: user, device, and context [14]. The overall power profile results from a combination of these concepts. Accurate resources and analytical methods are crucial for determining load curve characteristics and identifying consumer power consumption behavior. Although more realistic power profiles lead to more accurate demand management, collecting fine-grained metering measurements provides additional information about consumers' daily life patterns [15; 16]. This potentially raises privacy concerns for consumers, particularly with regard to untrusted third parties. In this paper, we introduce a model to analyze consumers' power consumption, describe their power usage behavior, profile their power usage, and predict consumption based on these profiles. It analyzes consumers' consumption behaviors in a realistic environment affected by the time factor. To build a time-invariant consumer-level load clustering, we perform load data time-series correlation using the dynamic time warping (DTW) algorithm [17]. DTW is selected to measure the similarity between the power usage time-series due to its properties, namely, signal warping invariability and implementation simplicity. By clustering the consumers' load data with the DTW algorithm, load forecasting is performed by correlating the historical load data and time-series of the new power usage data. We use the electricity consumption data extracted from the Almanac of Minutely Power Dataset Version 2 (AMPds2), available at Harvard Dataverse [18]. The AMPds2 is a real-world dataset that captures all three main types of consumption, including electricity, water, and natural gas, collected from a house in the Greater Vancouver metropolitan area, Canada, over a long period of time (two years) [18]. Short-term power usage data acquired by real-time smart meters are used to extract load patterns and generate realistic power profiling. The similarities between the short-term electricity consumption data points measured by DTW are used to extract the consumers' load patterns. Furthermore, the variations and patterns in the daily load factor [19] are examined to understand and analyze users' consumption behavior. The main contributions of this paper are as follows:  
* Extracting power consumption patterns by measuring the DTW similarity between a consumer's load data time series. * Power profiling based on the signal warping invariability property of the DTW algorithm.