Master of Science / Department of Electrical and Computer Engineering / Anil Pahwa / Electricity generation capacity from different renewable sources has been significantly growing worldwide in recent years, specially wind power. Fast dispatch of wind power provides flexibility for spinning reserve. However, wind is intermittent in nature. Thus, stable grid operations and energy management are becoming more challenging with the increasing penetration of wind in power systems. Efficient forecast methods can help the scenario. Many wind forecast models have been developed over the years. Highly effective models with the combination of numerical weather prediction and statistical models also exist at present. This study intends to develop a model to forecast hourly wind speed using an artificial neural network (ANN) approach for effective and fast operation with minimum data. The procedure is outlined in this work and the performance of the ANN model is compared with the persistence forecast model.
| Identifer | oai:union.ndltd.org:KSU/oai:krex.k-state.edu:2097/38945 |
| Date | January 1900 |
| Creators | Datta, Pallab Kumar |
| Source Sets | K-State Research Exchange |
| Language | en_US |
| Detected Language | English |
| Type | Report |
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