| Autor: |
Dharani Tejaswini, Pavan Ramapragada, Sraavani Gundepudi, Prabhakar Rao Kandukuri, Vishal Garg, Jyotirmay Mathur, Rajat Gupta |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Energy Informatics, Vol 5, Iss S4, Pp 1-12 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2520-8942 |
| DOI: |
10.1186/s42162-022-00225-4 |
| Popis: |
Abstract Air Conditioners (ACs) have become a major contributor to residential electricity consumption in India. Non-intrusive Load Monitoring (NILM) can be used to understand residential AC use and its contribution to electricity consumption. NILM techniques use ground truth information along with meter readings to train disaggregation algorithms. There are datasets available for disaggregation, but no dataset is available for a hot tropical country like India especially for AC event detection. Our dataset’s primary objective is to help train NILM algorithms for AC event detection and compressor operations. The dataset comprises of home-level electrical current consumption and manually tagged AC ground truth (ON/OFF status) data at 1-min interval, indoor environment temperature and relative humidity readings at 5-min interval and dwelling, AC and household characteristics. The data was collected from 11 homes located in a composite climate zone-Hyderabad, India for 19 summer days (May) 2019. The dataset consists of 1.6 million data points and 450 AC cycles with each cycle having a runtime of more than 60 min (> 2000 compressor ON/OF cycles). Public availability of such a dataset will allow researchers to develop, train and test NILM algorithms that recognize AC and identify compressor operations. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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