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Study Results from University College Dublin Broaden Understanding of Machine Learning (Wind Power Forecasting Using Ensemble Learning for Day-ahead Energy Trading)

  • Jun 23, 2022
  • 55 views
Source: 
Robotics & Machine Learning Daily News

2022 JUN 22 (NewsRx) -- By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News -- Data detailed on Machine Learning have been presented. According to news reporting from Dublin, Ireland, by NewsRx journalists, research stated, “Wind power forecasting is a field characterised by sudden weather-related events, turbine failures and constraints imposed by the electricity grid. Nowadays, different energy markets add the extra challenge of requiring predictions at the minute level a day forward for bidding-processes.”

Financial supporters for this research include Enterprise Ireland, Sustainable Energy Authority of Ireland in the project FREMI (Forecasting Renewable Energy with Machine Intelligence).

The news correspondents obtained a quote from the research from University College Dublin, “This is to avoid trading energy as a bulk and match demand. In this context, we present a novel approach to predict power generation at high frequencies one day in advance, which handles constraints such as curtailment and turbine degradation. This has been tested over historical data from SCADA systems and historical forecasts from wind speed providers for eight windfarm locations in Ireland over two years. Our work was performed in two phases. First, we undertook a preliminary study to analyse the relationship between all combinations of observed wind, forecasted wind and electrical power. Secondly, a wide variety of Machine Learning algorithms were run over each of the locations in order to assess the degrees of predictability of different algorithms and regions. Most of the algorithms benchmarked improve linear wind to power mappings besides the high degree of noise in this domain.”

According to the news reporters, the research concluded: “Our analysis and experimental results show how boosting ensembles are a cost-effective solution in terms of runtime among other Machine Learning algorithms predicting wind power a day ahead.”

This research has been peer-reviewed.

For more information on this research see: Wind Power Forecasting Using Ensemble Learning for Day-ahead Energy Trading. Renewable Energy, 2022;191:685-698. Renewable Energy can be contacted at: Pergamon-elsevier Science Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England. (Elsevier - www.elsevier.com; Renewable Energy - http://www.journals.elsevier.com/renewable-energy/)

Our news journalists report that additional information may be obtained by contacting Andres L. Suarez-Cetrulo, University College Dublin Ucd, Carbajo Irelands Ctr Appl Artificial Intelligence, School of Computer Science, Dublin, Ireland. Additional authors for this research include Lauren Burnham-King, David Haughton and Ricardo Simon Carbajo.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.renene.2022.04.032. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

 

(Our reports deliver fact-based news of research and discoveries from around the world.)

 

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