Abstract: Weather and climate risk impacts upon many areas of modern life. This presentation will focus on the nature of predictability and the use of numerical meteorological models (so called ‘NWP’ and ‘GCMs’) to understand weather and climate risk in complex impacted systems. Energy-system design and operations will be a particular but not exclusive focus.
The talk begins by briefly reviewing the nature of numerical meteorological models and the phenomena that they seek to simulate and predict. The challenges of linking simulated/predicted meteorological variations to ‘impacts’ in complex human- and environmental- systems will then be discussed, drawing on a range of examples spanning different types of simulation/prediction problem. These may include:
- Understanding, quantifying and reducing the role of climate uncertainty in power-system capacity expansion planning (particularly focusing on the use of intelligent sampling techniques applied to extensive meteorological datasets).
- Assessing the benefits of ensemble numerical weather prediction for scheduling and resourcing maintenance.
- The benefits of sequential learning algorithms (machine learning) and pattern-based conditional predictability applied to energy forecasting.
Citations for key publications to be discussed are provided below.
Climate uncertainty in power system capacity expansion planning
- Efficient quantification of the impact of demand and weather uncertainty in power system models.
- Importance subsampling: improving power system planning under climate-based uncertainty.
- Mitigating a century of European renewable variability with transmission and informed siting.
- Exploring the meteorological potential for planning a high performance European Electricity Super-grid: optimal power capacity distribution among countries.
Ensemble numerical weather prediction for maintenance scheduling/resourcing
Sequential learning algorithms and pattern-based forecasting for energy