- Title
- Prediction of fire danger index using a new machine learning based method to enhance power system resiliency against wildfires
- Creator
- Pham, Tan; Shah, Rakibuzzaman; Amjady, Nima; Islam, Syed
- Date
- 2024
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/204705
- Identifier
- vital:20072
- Identifier
-
https://doi.org/10.1049/gtd2.13320
- Identifier
- ISSN:1751-8687 (ISSN)
- Abstract
- Wildfires, which can cause significant damage to power systems, are mostly inevitable and unpredictable. Fire danger indexes, such as the Forest Fire Danger Index (FFDI) and the Canadian Fire Weather Index (FWI), measure the potential wildfire danger at a given time and location. Thus, by predicting these fire danger indexes in advance, power system operators can obtain valuable insight into the potential wildfire risks and can better be prepared to tackle the wildfires. However, due to dependency on weather conditions, these indexes usually have volatile time series, which make their prediction complex. Taking these facts into account, this paper, unlike previous approaches that predict fire danger indexes based on climatological models, develops a machine learning-based forecast process to predict these indexes using the relevant weather data and past performance. To do this, first, a volatility analysis approach is presented to analyse the volatility level of the time series data of a fire danger index. Afterwards, an effective machine learning-based forecast methodology using a new deep feature selection model is proposed to predict fire danger indexes. The developed forecast methodology is tested on the real-world data of FFDI and FWI and is compared with several popular alternative methods reported in the literature. © 2024 The Author(s). IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
- Publisher
- John Wiley and Sons Inc
- Relation
- IET Generation, Transmission and Distribution Vol. 18, no. 23 (2024), p. 4008-4022
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- http://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © 2024 The Author(s)
- Rights
- Open Access
- Subject
- 4008 Electrical engineering; 4009 Electronics, sensors and digital hardware; Forecasting theory; Wildfires
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