Building Energy Profile Clustering Based on Energy Consumption Patterns
| dc.contributor.author | Afzalan, Milad | en |
| dc.contributor.committeechair | Eldardiry, Hoda | en |
| dc.contributor.committeecochair | Jazizadeh, Farrokh | en |
| dc.contributor.committeemember | Fox, Edward A. | en |
| dc.contributor.department | Computer Science | en |
| dc.date.accessioned | 2020-07-09T13:01:40Z | en |
| dc.date.available | 2020-07-09T13:01:40Z | en |
| dc.date.issued | 2020-06 | en |
| dc.description.abstract | 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. | en |
| dc.description.abstractgeneral | 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. | en |
| dc.description.degree | M.S. | en |
| dc.format.medium | ETD | en |
| dc.identifier.uri | http://hdl.handle.net/10919/99317 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | Creative Commons Attribution-NoDerivatives 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by-nd/4.0/ | en |
| dc.subject | Clustering | en |
| dc.subject | Unsupervised learning | en |
| dc.subject | Segmentation | en |
| dc.subject | Smart gird | en |
| dc.subject | Energy consumption | en |
| dc.title | Building Energy Profile Clustering Based on Energy Consumption Patterns | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Computer Science and Application | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | M.S. | en |