Online Sequential Extreme Learning Machine Algorithm for Better Predispatch Electricity Price Forecasting Grids
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
Original language | English |
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Article number | 9320514 |
Pages (from-to) | 1860-1871 |
Journal / Publication | IEEE Transactions on Industry Applications |
Volume | 57 |
Issue number | 2 |
Online published | 12 Jan 2021 |
Publication status | Published - Mar 2021 |
Externally published | Yes |
Link(s)
Abstract
The predispatch price forecast plays a key element in the electricity market. However, such a forecast usually depends on the traditional offline batch-learning technologies, which cannot respond in time to the unexpected changes in the local power system environment. Further, the predispatch local price forecast is often affected by the dynamic price changes from the neighboring regions. This article proposes a novel online learning forecast approach to overcome the above issues to provide a better predispatch price forecast by using the online sequential extreme learning machine (OS-ELM) algorithm. The article proposes a novel data structure in the form of a 2-D orthogonal list and two corresponding OS-ELM modules. One module provides the rolling day-ahead price prediction and prediction intervals using the day-by-day online training update, while the other provides the rolling 30-min prediction using the 2-h-by-2-h online training update. The proposed approach can continuously perceive any unexpected events and any price fluctuations from the neighboring regions in the nonlinear patterns. The proposed approach is validated using simulation studies based on the data from the Australian electricity market, and the simulation results show that the proposed approach can help in improving the forecast accuracy, especially when unexpected changes occur both locally and in the neighboring area. © 2021 IEEE.
Research Area(s)
- Electricity market, electricity price, extreme learning machine, online training, regression analysis
Citation Format(s)
Online Sequential Extreme Learning Machine Algorithm for Better Predispatch Electricity Price Forecasting Grids. / Xiao, Chixin; Sutanto, Danny; Muttaqi, Kashem M. et al.
In: IEEE Transactions on Industry Applications, Vol. 57, No. 2, 9320514, 03.2021, p. 1860-1871.
In: IEEE Transactions on Industry Applications, Vol. 57, No. 2, 9320514, 03.2021, p. 1860-1871.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review