Application of Long-Short-Term-Memory Recurrent Neural Networks to Forecast Wind Speed
Source abstract
Abstract
Forecasting wind speed is one of the most important and challenging problems in the wind power prediction for electricity generation. Long short-term memory was used as a solution to short-term memory to address the problem of the disappearance or explosion of gradient information during the training process experienced by the recurrent neural network (RNN) when used to study time series. In this study, this problem is addressed by proposing a prediction model based on long short-term memory and a deep neural network developed to forecast the wind speed values of multiple time steps in the future. The weather database in Halifax, Canada was used as a source for two series of wind speeds per hour. Two different seasons spring (March 2015) and summer (July 2015) were used for training and testing the forecasting model. The results showed that the use of the proposed model can effectively improve the accuracy of wind speed prediction.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Energy Load and Power Forecasting · Solar Radiation and Photovoltaics · Hydrological Forecasting Using AI
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Division of Engineering, Saint Mary’s University, Halifax, NS B3H 3C3, Canada
References (20)
Show source-provided references
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