Projects
Projects: Projects for Investigator |
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| Reference Number | NER/T/S/2002/00154 | |
| Title | Quantifying the economic value of coupled ocean-atmosphere model ensemble forecasts for decision making within the UK energy industry. | |
| Status | Completed | |
| Energy Categories | Other Power and Storage Technologies(Electricity transmission and distribution) 100%; | |
| Research Types | Basic and strategic applied research 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics) 25%; PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 25%; ENVIRONMENTAL SCIENCES (Earth Systems and Environmental Sciences) 50%; |
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| UKERC Cross Cutting Characterisation | Not Cross-cutting 100% | |
| Principal Investigator |
Professor DB Stephenson No email address given Engineering Computer Science and Maths University of Exeter |
|
| Award Type | R&D | |
| Funding Source | NERC | |
| Start Date | 01 November 2002 | |
| End Date | 31 October 2004 | |
| Duration | 24 months | |
| Total Grant Value | £107,162 | |
| Industrial Sectors | Transport Systems and Vehicles | |
| Region | South West | |
| Programme | Coupled Ocean Atmospheric Processes & European Climate (COAPEC) | |
| Investigators | Principal Investigator | Professor DB Stephenson , Engineering Computer Science and Maths, University of Exeter (99.998%) |
| Other Investigator | Dr R (Rowan ) Sutton , Meteorology, University of Reading (0.001%) Prof A (Alan ) O'Neill , Meteorology, University of Reading (0.001%) |
|
| Web Site | ||
| Objectives | Objectives not supplied | |
| Abstract | This pilot project will develop a proper Bayesian framework for exploiting the probabilistic weather information provided by coupled model forecasts. The generic methodology will be clearly illustrated by applying it to the problem of electricity demand forecasting in collaboration with the National Grid Company plc. Both historical weather data and multi-model ensembles of coupled model hindcasts provided by the EU DEMETER project will be combined using Bayesian methods to provide optimal hindcasts of the probability distribution of seasonal electricity demand. The added value will be carefully assessed using Bayesian decision theory value-at-risk models. This ?proof of concept? approach will enable us to quantitatively assess the real added value of coupled model seasonal forecastsand COAPEC science. | |
| Publications | (none) |
|
| Final Report | (none) |
|
| Added to Database | 08/09/08 | |
