Abstract
This article uses a combination forecasting algorithm that combines long and short-term memory networks and a time series analysis method to realize short-term wind speed forecasting. Use the ARIMA model of the time series analysis method to predict the short-term wind speed to obtain the prediction results and prediction residuals, and then use the long- and short-term memory network to predict the prediction residuals, and finally linearly combine the prediction results obtained by the two methods to obtain the final prediction Result sequence. Experiments compare the pros and cons of several algorithms, and the results show that the combined algorithm combining long and short-term memory network and ARIMA model effectively improves the accuracy of short-term wind speed series prediction and is a feasible analysis method.
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The research was funded by the Natural Science Foundation of Guangdong Province (Natural Science Foundation of Guangdong Province), project number: 2020A1515010784.
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Li, R. (2022). Simulate and Predict Short-Term Wind Speed with LSTM and ARIMA Model. In: Li, K., Liu, Y., Wang, W. (eds) Exploration of Novel Intelligent Optimization Algorithms. ISICA 2021. Communications in Computer and Information Science, vol 1590. Springer, Singapore. https://doi.org/10.1007/978-981-19-4109-2_19
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DOI: https://doi.org/10.1007/978-981-19-4109-2_19
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