| Description |
1 online resource (16 pages) : mostly color illustrations. |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
| Series |
Conference paper / NREL ; NREL/CP-5D00-77768 |
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Conference paper (National Renewable Energy Laboratory (U.S.)) ; NREL/CP-5D00-77768.
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| Note |
"September 2020." |
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"Presented at the 2020 ACEEE Summer Study on Energy Efficiency in Buildings, August 17-21, 2020"--Cover. |
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In scope of the U.S. Government Publishing Office Cataloging and Indexing Program (C&I) and Federal Depository Library Program (FDLP). |
| Bibliography |
Includes bibliographical references (pages 14-16). |
| Funding |
DE-AC36-08GO28308 |
| Note |
Description based on online resource; title from PDF title page (NREL, viewed February 27, 2023). |
| Summary |
Emerging grid-interactive efficient buildings (GEBs) have great potential to provide much-needed demand flexibility to electric grids while fulfilling their own control targets by co-optimizing smart appliances, solar photovoltaics, electric vehicles, and energy storage at buildings. To enable the optimal operation of GEBs, site-specific weather information-such as temperature, solar irradiance, relative humidity, and wind speed-is crucial; however, this information is generally unavailable or expensive to obtain. This paper develops advanced machine learning methods to provide precise weather forecasts for individual building sites using readily available weather station data. Support vector regression and artificial neural networks have been employed to learn the spatiotemporal correlations between the weather conditions at nearby weather stations and the individual building site. The proposed site-specific weather forecasting methods have been validated using 1-year actual weather measurement data collected in the Denver metro area. Results show that the developed machine-learning-driven methods can accurately forecast the temperature at the target building site 1 hour ahead with mean absolute error less than 0.72°C and a 48% improvement over the persistence method. Site-specific weather forecasts will improve the understanding of the microclimate effect and its impact on building energy consumption. This information will drive efficiency upgrades and adjustments of building control strategies to improve energy savings and increase flexibility in building loads. |
| Subject |
Weather -- Observations.
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Building sites -- Planning.
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Weather forecasting -- Mathematical models.
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Neural networks (Computer science) -- Scientific applications.
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Chantiers de construction -- Planification.
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Temps (Météorologie) -- Prévision -- Modèles mathématiques.
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Réseaux neuronaux (Informatique) -- Applications scientifiques.
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Building sites -- Planning
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Weather
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Weather forecasting -- Mathematical models
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| Indexed Term |
artificial neural networks |
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grid-interactive efficient buildings |
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support vector regression |
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weather forecasting |
| Genre/Form |
Observations
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| Added Author |
National Renewable Energy Laboratory (U.S.), issuing body.
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United States. Department of Energy, sponsoring body.
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| Standard No. |
1669587 OSTI ID |
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0000-0003-4374-4651 |
| Gpo Item No. |
0430-P-04 (online) |
| Sudoc No. |
E 9.17:NREL/CP-5 D 00-77768 |
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