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Online Sequential Extreme Learning Machine Algorithm for Better Predispatch Electricity Price Forecasting Grids
IEEE Transactions on Industry Applications ( IF 4.4 ) Pub Date : 2021-01-12 , DOI: 10.1109/tia.2021.3051105
Chixin Xiao , Danny Sutanto , Kashem M. Muttaqi , Minjie Zhang , Ke Meng , Zhao Yang Dong

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.

中文翻译:

在线序贯极限学习机算法,用于更好的调度前电价预测网格

调度前的价格预测在电力市场中起着关键作用。但是,这种预测通常取决于传统的离线批处理学习技术,这些技术无法及时响应本地电力系统环境中的意外变化。此外,调度前的本地价格预测通常受邻近地区动态价格变化的影响。本文提出了一种新颖的在线学习预测方法,以克服上述问题,从而通过使用在线顺序极限学习机(OS-ELM)算法提供更好的调度前价格预测。本文以二维正交列表和两个相应的OS-ELM模块的形式提出了一种新颖的数据结构。一个模块使用每日在线培训更新提供滚动的日前价格预测和预测间隔,另一个则使用2小时乘2小时的在线培训更新来提供30分钟的滚动预测。所提出的方法可以连续地感知非线性模式中来自邻近区域的任何意外事件和价格波动。基于澳大利亚电力市场数据的仿真研究验证了该方法的有效性,仿真结果表明,该方法可以帮助提高预报的准确性,尤其是当本地和邻近地区发生意外变化时。
更新日期:2021-03-19
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