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Improving LSTM Neural Networks for Better Short-Term Wind Power Predictions

by   Maximilian Du, et al.

This paper introduces an improved method of wind power prediction via weather forecast-contextualized Long Short- Term Memory Neural Network (LSTM) models. Wind power and weather forecast data were acquired from open-source databases and combined. However, a generic LSTM model performs poorly on this data, with erratic behavior observed on even low-variance data sections. To address this issue, LSTM modifications were proposed and tested for accuracy through both a Normalized Mean Absolute Error and the Naive Ratio, which is a score introduced by this paper to quantify unwanted "naive" model behavior. Results showed an increase in model accuracy with the addition of weather forecast data to the models, as well as major improvements in performance with some model modifications, which are attributed to the increased contextualization and stability of the new models. These new and improved models have the potential to improve power grid stability and expedite renewable power integration.


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