Multi-dimensional evaluation of temporal neural networks on solar irradiance forecasting
Department of Computer Science
With recent developments and advances in machine learning methods, traditional time series analysis techniques, e.g., ARMA, ARIMA, ARIMAX, SARIMAX, etc. models are being replaced by deep learning models. The application of Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM) to the problem of solar irradiance prediction has been shown in the literature to achieve state-of-the-art performance in this domain. For sequence modeling, Temporal Convolutional Networks (TCN) are gaining increasing attention because of the excellent trade-off between performance accuracy and time in training the models. In this paper, an evaluation of these deep learning methods is completed on the application of short-term, one hour ahead solar irradiance forecasting. The results show that TCNs can reduce the resource usage, depending on the data-folding size and model complexity, to require only 5% of the training time with only a loss of 6% RMSE.
2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)
Brown, L. E.
Multi-dimensional evaluation of temporal neural networks on solar irradiance forecasting.
2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia), 4192-4197.
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/1344