Accurate localized short term weather prediction for renewables planning

David Corne, Manjula Dissanayake, Andrew Peacock, Stuart Galloway, Eddie Owens

Research output: Contribution to conferenceProceeding

5 Citations (Scopus)

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.

Conference

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

Fingerprint

weather
Planning
weather forecasting
Weather forecasting
prediction
downscaling
cloud cover
planning
physics
wind velocity
time series
Time series
Physics
forecast
resource
energy
Statistical Models

Keywords

  • big data
  • downscaling
  • feature selection
  • nowcasting
  • numerical weather prediction
  • regression
  • renewables forecasting
  • statistical weather prediction
  • weather forecasting

Cite this

Corne, D., Dissanayake, M., Peacock, A., Galloway, S., & Owens, E. (2015). Accurate localized short term weather prediction for renewables planning. 1-8. IEEE Symposium on Computational Intelligence Applications in Smart Grid, Orlando, United States. https://doi.org/10.1109/CIASG.2014.7011547
Corne, David ; Dissanayake, Manjula ; Peacock, Andrew ; Galloway, Stuart ; Owens, Eddie. / Accurate localized short term weather prediction for renewables planning. IEEE Symposium on Computational Intelligence Applications in Smart Grid, Orlando, United States.8 p.
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Corne, D, Dissanayake, M, Peacock, A, Galloway, S & Owens, E 2015, 'Accurate localized short term weather prediction for renewables planning' IEEE Symposium on Computational Intelligence Applications in Smart Grid, Orlando, United States, 9/12/14 - 12/12/14, pp. 1-8. https://doi.org/10.1109/CIASG.2014.7011547

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

2015. 1-8 IEEE Symposium on Computational Intelligence Applications in Smart Grid, Orlando, United States.

Research output: Contribution to conferenceProceeding

TY - CONF

T1 - Accurate localized short term weather prediction for renewables planning

AU - Corne, David

AU - Dissanayake, Manjula

AU - Peacock, Andrew

AU - Galloway, Stuart

AU - Owens, Eddie

PY - 2015/1/15

Y1 - 2015/1/15

N2 - 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.

AB - 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.

KW - big data

KW - downscaling

KW - feature selection

KW - nowcasting

KW - numerical weather prediction

KW - regression

KW - renewables forecasting

KW - statistical weather prediction

KW - weather forecasting

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DO - 10.1109/CIASG.2014.7011547

M3 - Proceeding

SP - 1

EP - 8

ER -

Corne D, Dissanayake M, Peacock A, Galloway S, Owens E. Accurate localized short term weather prediction for renewables planning. 2015. IEEE Symposium on Computational Intelligence Applications in Smart Grid, Orlando, United States. https://doi.org/10.1109/CIASG.2014.7011547