NDLI: Accurate localized short term weather prediction for renewables planning

Content Provider	IEEE Xplore Digital Library
Author	Corne, D. Dissanayake, M. Peacock, A. Galloway, S. Owens, E.
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Sci., Heriot-Watt Univ., Edinburgh, UK (Corne, D.; Dissanayake, M.) \|\| Dept. of Electron. & Electr. Eng., Univ. of Strathclyde, Glasgow, UK (Galloway, S.) \|\| Sch. of the Built Environ., Heriot-Watt Univ., Edinburgh, UK (Peacock, A.; Owens, E.)
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.
Starting Page	1
Ending Page	8
File Size	863112
Page Count	8
File Format	PDF
ISBN	9781479945467
ISSN	23267690
DOI	10.1109/CIASG.2014.7011547
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Frequency selective surfaces Feature selection Clouds Buildings Weather forecasting Predictive models Regression Renewables forecasting Downscaling Forecasting Numerical weather prediction Statistical weather prediction Big data Nowcasting
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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