Ciaran Gilbert and I have won an international wind power forecasting competition run by TU Dresden in conjunction with the 14th European Energy Market Conference. The competition required us to forecast the total generation for a portfolio of wind farms from 2 to 38 hours-ahead every day for two weeks. Our method, which was based on regime-switching autoregression, produced forecasts with an error score 4.5% lower than the second place finishers. Ciaran will be attending the EEM conference in Dresden in June to receive a cash prize and present the approach, which is detailed in this paper and available in open-source software (R code).
 
Energy forecasting is a vital component of modern power system and electricity market operation, and improving the production and use of forecasts is becoming increasingly important as the penetration of weather-dependent renewable generation increases. Improved forecasting will help keep consumer energy bills down by reducing the amount of reserve power required to manage fluctuations in generation and demand. Strathclyde has a strong and growing capability in energy forecasting with active projects in wind, solar and electricity demand forecasting, and engagement from leading utilities including National Grid, SSE and Iberdrola.

I'm participating in the Global Energy Forecasting Competition 2017. This edition of GEFcom is a hierarchical load forecasting challenge, entries take the form of quantile forecasts for the zones of ISO New England, total-Massachusetts, and total-ISONE. I though this was a good excuse to play with ployly... see below for my total-ISONE entry to the first task.

I'm working with a great consortium of industry leaders to develop decision-support tools for wind O&M. Recently, we've been focused on heavy-lift operations which as subject to strict wind speed safety limits. Using scenario forecast (some times called trajectory or statistical ensembles) we can estimate both the expected cost and risk associated with different scheduling options. This kind of information can help reduce operational costs for on- and offshore wind, lowering the cost of wind energy for all.

Thanks to our partners Romax, Datalytics, ScottishPower, and SSE!