Abstract

Short-term prediction of meteorological variables is important for many applications. For example, many 'smart grid' planning and control scenarios rely on accurate short term prediction of renewable energy generation, which in turn requires accurate forecasts of wind-speed, cloud-cover, and other such variables. Accurate short-term weather forecasting therefore enables smooth integration of renewables into future intelligent power systems. Weather forecasting at a specific location is currently achieved by numerical weather prediction (NWP), or by statistical models built from local time series data, or by a hybrid of these two methods broadly known as 'downscaling'. We introduce a new data-intensive approach to localized short-term weather prediction that relies on harvesting multiple freely available observations and forecasts pertaining to the wider geographic region. Our hypothesis is that NWP-based forecast resources, despite the benefit of a dynamical physics-based model, tend to be only sparsely informed by observation-based inputs at a local level, while statistical downscaling models, though locally well-informed, invariably miss the opportunity to include rich additional data sources concerning the wider local region. By harvesting the data stream of multiple forecasts and observations from the wider local region we expect to achieve better accuracy than available otherwise. We describe the approach and demonstrate results for three locations, focusing on the 1hr-24hrs ahead forecasting of variables crucial for renewables forecasting. This work is part of the ORIGIN EU FP7 project (www.origin-concept.eu) and the weather forecasting approach, used in ORIGIN as input for both demand and renewables prediction, began live operation (initially for three European locations) in October 2014.

Original languageEnglish
DOIs
StatePublished - 15 Jan 2015
Event2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid - Orlando, United States

Conference

Conference2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid
Abbreviated titleCIASG 2014
CountryUnited States
CityOrlando
Period9/12/1412/12/14

Fingerprint

Weather forecasting
Planning
Time series
Physics

Keywords

  • Smart grid
  • renewable-energy and Built Environment
  • Demand response
  • Affordance
  • Distributed generation
  • Electricity supply

Cite this

Corne, D., Dissanayake, M., Peacock, A., Galloway, S., & Owens, E. H. (2015). Accurate localized short term weather prediction for renewables planning. Paper presented at 2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid, Orlando, United States.DOI: 10.1109/CIASG.2014.7011547

Corne, David; Dissanayake, Manjula; Peacock, Andrew; Galloway, Stuart; Owens, Edward Hugh / Accurate localized short term weather prediction for renewables planning.

2015. Paper presented at 2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid, Orlando, United States.

Research output: Contribution to conferencePaper

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abstract = "Short-term prediction of meteorological variables is important for many applications. For example, many 'smart grid' planning and control scenarios rely on accurate short term prediction of renewable energy generation, which in turn requires accurate forecasts of wind-speed, cloud-cover, and other such variables. Accurate short-term weather forecasting therefore enables smooth integration of renewables into future intelligent power systems. Weather forecasting at a specific location is currently achieved by numerical weather prediction (NWP), or by statistical models built from local time series data, or by a hybrid of these two methods broadly known as 'downscaling'. We introduce a new data-intensive approach to localized short-term weather prediction that relies on harvesting multiple freely available observations and forecasts pertaining to the wider geographic region. Our hypothesis is that NWP-based forecast resources, despite the benefit of a dynamical physics-based model, tend to be only sparsely informed by observation-based inputs at a local level, while statistical downscaling models, though locally well-informed, invariably miss the opportunity to include rich additional data sources concerning the wider local region. By harvesting the data stream of multiple forecasts and observations from the wider local region we expect to achieve better accuracy than available otherwise. We describe the approach and demonstrate results for three locations, focusing on the 1hr-24hrs ahead forecasting of variables crucial for renewables forecasting. This work is part of the ORIGIN EU FP7 project (www.origin-concept.eu) and the weather forecasting approach, used in ORIGIN as input for both demand and renewables prediction, began live operation (initially for three European locations) in October 2014.",
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Corne, D, Dissanayake, M, Peacock, A, Galloway, S & Owens, EH 2015, 'Accurate localized short term weather prediction for renewables planning' Paper presented at 2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid, Orlando, United States, 9/12/14 - 12/12/14, . DOI: 10.1109/CIASG.2014.7011547

Accurate localized short term weather prediction for renewables planning. / Corne, David; Dissanayake, Manjula; Peacock, Andrew; Galloway, Stuart; Owens, Edward Hugh.

2015. Paper presented at 2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid, Orlando, United States.

Research output: Contribution to conferencePaper

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AU - Corne,David

AU - Dissanayake,Manjula

AU - Peacock,Andrew

AU - Galloway,Stuart

AU - Owens,Edward Hugh

N1 - IEEE Symposium on Computational Intelligence Applications in Smart Grid CIASG

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KW - renewable-energy and Built Environment

KW - Demand response

KW - Affordance

KW - Distributed generation

KW - Electricity supply

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M3 - Paper

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Corne D, Dissanayake M, Peacock A, Galloway S, Owens EH. Accurate localized short term weather prediction for renewables planning. 2015. Paper presented at 2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid, Orlando, United States. Available from, DOI: 10.1109/CIASG.2014.7011547