Decomposition-based stacked bagging boosting ensemble for dynamic line rating forecasting

Ahmadi, A., Taheri, S., Ghorbani, R., Vahidinasab, V. ORCID: 0000-0002-0779-8727 and Mohammadi-ivatloo, B., 2023. Decomposition-based stacked bagging boosting ensemble for dynamic line rating forecasting. IEEE Transactions on Power Delivery. ISSN 0885-8977

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Effective exploitation of overhead transmission lines needs reliable and precise dynamic line rating forecasting. High-accuracy dynamic line rating forecasting, in particular, is an important short-term method for coping with grid congestion, enhancing grid stability, and accommodating high renewable energy penetration. Due to the non-stationarity and stochasticity of the meteorological variables, a single model is often not sufficient to accurately predict the dynamic line rating. Herein, a new stacked bagging boosting ensemble is developed based on multivariate empirical mode decomposition to overcome single models' restrictions and increase the dynamic line rating forecasting performance. The developed ensemble is utilized on the data gathered from a 400 kV aluminum conductor steel-reinforced overhead power line with a length of 32.85 Km between Ghadamgah and Binalood wind farms, located in the northeast of Iran. The simulation results substantiate that the proposed ensemble can capture meteorological variables' non-linear characteristics, yielding more accurate yet to noisy data forecasts than single forecasting models.

Item Type: Journal article
Publication Title: IEEE Transactions on Power Delivery
Creators: Ahmadi, A., Taheri, S., Ghorbani, R., Vahidinasab, V. and Mohammadi-ivatloo, B.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 17 April 2023
ISSN: 0885-8977
Rights: © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Divisions: Schools > School of Science and Technology
Record created by: Laura Ward
Date Added: 20 Apr 2023 15:55
Last Modified: 20 Apr 2023 15:55

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