Category: ESIPP

Here is a review of the most common statistical post-processing methods used in renewable energy forecasting. The main methods discussed here are: machine learning, model output statistics, Kalman filters, regression models, historical analogs and a process of post-processing depending on weather typing. post-processing

I attended EMS2018 last week in Budapest, where I presented a talk on my on-going work with Conor and Emily Gleeson from Met Éireann. The talk was titled “An Evaluation of Integrated Cloud Condensate in the HARMONIE-AROME NWP Model” as part of the session on cloud-aerosol-radiation interactions. I used MÉRA for the data and examined […]

Article Title: Skilful seasonal predictions for the European energy industry Published in the Environmental Research Letters journal, in 2017. This article, produced by the UK’s Met Office Hadley Centre, is a follow up on previous recent research conducted by scientists from the same centre. That previous research has been able to reproduce the North Atlantic Oscillation […]

To quantify the skill of forecast systems or reanalysis models, deterministic skill scores are used. This report describes some of the most common deterministic skill scores used in meteorology and energy/power forecasting and under which circumstances they are most suitable. These include, among others: Mean error, root mean square error (RMSE) and mean absolute error. […]