With the widespread adoption of smart meters in buildings, an unprecedented amount of high- resolution energy data is released, which provides opportunities to understand building consumption patterns. Accordingly, research efforts have employed data analytics and machine learning methods for the segmentation of consumers based on their load profiles, which help utilities and energy providers for customized/personalized targeting for energy programs. However, building energy segmentation methodologies may present oversimplified representations of load shapes, which do not properly capture the realistic energy consumption patterns, in terms of temporal shapes and magnitude. In this thesis, we introduce a clustering technique that is capable of preserving both temporal patterns and total consumption of load shapes from customers’ energy data. The proposed approach first overpopulates clusters as the initial stage to preserve the accuracy and merges the similar ones to reduce redundancy in the second stage by integrating time-series similarity techniques. For such a purpose, different time-series similarity measures based on Dynamic Time Warping (DTW) are employed. Furthermore, evaluations of different unsupervised clustering methods such as k-means, hierarchical clustering, fuzzy c-means, and self-organizing map were presented on building load shape portfolios, and their performance were quantitatively and qualitatively compared. The evaluation was carried out on real energy data of ~250 households. The comparative assessment (both qualitatively and quantitatively) demonstrated the applicability of the proposed approach compared to benchmark techniques for power time-series clustering of household load shapes. The contribution of this thesis is to: (1) present a comparative assessment of clustering techniques on household electricity load shapes and highlighting the inadequacy of conventional validation indices for choosing the cluster number and (2) propose a two-stage clustering approach to improve the representation of temporal patterns and magnitude of household load shapes. / M.S. / With the unprecedented amount of data collected by smart meters, we have opportunities to systematically analyze the energy consumption patterns of households. Specifically, through using data analytics methods, one could cluster a large number of energy patterns (collected on a daily basis) into a number of representative groups, which could reveal actionable patterns for electric utilities for energy planning. However, commonly used clustering approaches may not properly show the variation of energy patterns or energy volume of customers at a neighborhood scale. Therefore, in this thesis, we introduced a clustering approach to improve the cluster representation by preserving the temporal shapes and energy volume of daily profiles (i.e., the energy data of a household collected during 1 day). In the first part of the study, we evaluated several well-known clustering techniques and validation indices in the literature and showed that they do not necessarily work well for this domain-specific problem. As a result, in the second part, we introduced a two-stage clustering technique to extract the typical energy consumption patterns of households. Different visualization and quantified metrics are shown for the comparison and applicability of the methods. A case-study on several datasets comprising more than 250 households was considered for evaluation. The findings show that datasets with more than thousands of observations can be clustered into 10-50 groups through the introduced two-stage approach, while reasonably maintaining the energy patterns and energy volume of individual profiles.

As our electric power distribution infrastructure has aged, considerable investment has been applied to modernizing the electrical power grid through weatherization and in deployment of real-time monitoring systems. A key question is whether or not these investments are reducing the number and duration of power outages, leading to improved reliability. Statistical methods are applied to analyze electrical disturbance data (from the Department of Energy, DOE) and reliability index data (from state utility public service commission regulators) to detect signs of improvement. The number of installed smart meters provided by several utilities is used to determine whether the number of smart meters correlate with a reduction in outage frequency. Indication emerged that the number of power outages may be decreasing over time. The magnitude of power loss has decreased from 2003 to 2007, and behaves cyclically from 2008 to 2014, with a few outlier points in both groups. The duration also appears to be decreasing between 2003-2014. Large blackout events exceeding 5 GW continue to be rare, and certain power outage events are seasonally dependent. There was a linear relationship between the number of customers and the magnitude of a power outage event. However, no relationship was found between the magnitude of power outages and time to restore power. The frequency of outages maybe decreasing as the number of installed smart meters has increased. Recommendations for inclusion of additional metrics, changes to formatting and semantics of datasets currently provided by federal and state regulators are made to help aid researchers in performing more effective analysis. Confounding variables and lack of information that has made the analysis diffcult is also discussed.