A cloud-oriented data-analysis framework to analyze peak demand dynamics in institutional building clusters
Bibliographic Data
| ID | 21234583 |
|---|---|
| Authors | Vipul Moudgil (University of British Columbia, Okanagan Campus), Rehan Sadiq (0000-0003-1474-2366, University of British Columbia, Okanagan Campus), Ezzeddin Bakhtavar (0000-0001-9524-2229, University of British Columbia), Amrit Paudel (0000-0002-3325-7828, BC Hydro (Canada)), Kasun Hewage (0000-0003-3931-0017, University of British Columbia, Okanagan Campus, corresponding author) |
| Year | 2024 |
| Volume | 111 |
| Pages | 105553 |
| Publication date | 2024-09-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sustainable Cities and Society (JOURNAL) |
| Journal identifiers | ISSN: 2210-6707 • E-ISSN: 2210-6715 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.scs.2024.105553 |
| OpenAlex | W4399564601 |
| Language | EN |
| Citations received | 1 |
| References cited | 30 |
Data analysis framework to examine institutional buildings’ peak electrical demand. • Quantile-based metrics are introduced to comprehend peaking patterns in buildings. • Cross building ranking is established to identify electrically inefficient buildings. • Framework was implemented and tested in real world university settings. • Cloud-based dashboard was developed for visually intuitive display of results. Peak loads in higher education institutional building clusters (IBCs) possess considerable economic repercussions on their overall operations. Thus, identifying electrically inefficient buildings presents a significant opportunity to curtail peak loads and promote energy efficiency in IBCs. Existing literature implements clustering algorithms to comprehend the electrical demand dynamics of buildings to analyze disparity in their load behavior. These techniques perform well in single building environment, however, fall short in comprehending the demand dynamics for building clusters, specifically during peak loads. This study introduces a cloud-oriented quantile-based data analysis framework, specifically designed for simultaneously evaluating demand profiles of multiple buildings within IBCs. Quantile-based metrics namely, Value-at-Risk, Conditional Value-at-Risk and Conditional Value-at-Risk standard deviation are implemented to comprehend the electrical fluctuations and quantify the electrical impact of each building during campus-wide peak demands. A cross-building comparison is established by linearly ranking buildings following two key criteria: (i) buildings with frequent demand fluctuations and (ii) buildings exerting a high electrical impact on overall IBC demand. Both criteria are equally weighted while ranking to identify the most inefficient buildings during peak loads. The framework is implemented in a Canadian university and resulted a substantial 50 MW demand reduction through recommissioning and retrofitting of inefficient buildings
Cloud computing · Data science · Econometrics · Economics · Geography · Regional science · Sociology · Computer Science · Consumer Retail Behavior Studies · Human Mobility and Location-Based Analysis · Transportation Planning and Optimization
| Citations per year | 1 |
|---|---|
| Citation span | 2025 - 2025 (1) |
| Highly cited | No |