Data Driven Approaches for Prediction of Building Energy Consumption at Urban Level
| Autor: | Ruth Kerrigan, James O‘Donnell, Donal Finn, Michael Oates, Giovanni Tardioli |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Engineering
Exploit 020209 energy media_common.quotation_subject Energy (esotericism) Context (language use) 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences Data-driven buildings clustering Energy(all) 11. Sustainability 0202 electrical engineering electronic engineering information engineering Quality (business) Scenario planning 0105 earth and related environmental sciences media_common Consumption (economics) business.industry large scale data-driven models Data science Variety (cybernetics) building energy consumption estimation Data mining business energy mapping computer |
| Zdroj: | Energy Procedia. 78:3378-3383 |
| ISSN: | 1876-6102 |
| DOI: | 10.1016/j.egypro.2015.11.754 |
| Popis: | The ability to predict building energy consumption in an urban environment context, using a variety of performance metrics for different building categories and granularities, across varying geographic scales, is critical for future energy scenario planning. The increased quantity and quality of data collected across urban districts facilitates the utilization of data-driven approaches, thereby realizing the potential for energy prediction as a complementary or alternative option to the more traditional physics based approaches. The majority of research to date that exploits data-driven approaches, has mainly focused on analysis at an individual building level. There are few examples in the literature of studies that utilize data-driven models for building energy prediction at an urban scale. The current paper provides a literature review of the recent applications of data-driven models at an urban scale, underlining the opportunities for further research in this context. |
| Databáze: | OpenAIRE |
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