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An intelligent approach to short-term wind power prediction using deep neural networks

Niksa-Rynkiewicz Tacjana, Stomma Piotr, Witkowska Anna, Rutkowska Danuta, Słowik Adam, Cpałka Krzysztof, Jaworek-Korjakowska Joanna, Kolendo Piotr

Journal of Artificial Intelligence and Soft Computing Research

2023

Vol. 13. No. 3

197--210

In this paper, an intelligent approach to the Short-Term Wind Power Prediction (STWPP) problem is considered, with the use of various types of Deep Neural Networks (DNNs). The impact of the prediction time horizon length on accuracy, and the influence of temperature on prediction effectiveness have been analyzed. Three types of DNNs have been implemented and tested, including: CNN (Convolutional Neural Networks), GRU (Gated Recurrent Unit), and H-MLP (Hierarchical Multilayer Perceptron). The DNN architectures are part of the Deep Learning Prediction (DLP) framework that is applied in the Deep Learning Power Prediction System (DLPPS). The system is trained based on data that comes from a real wind farm. This is significant because the prediction results strongly depend on weather conditions in specific locations. The results obtained from the proposed system, for the real data, are presented and compared. The best result has been achieved for the GRU network. The key advantage of the system is a high effectiveness prediction using a minimal subset of parameters. The prediction of wind power in wind farms is very important as wind power capacity has shown a rapid increase, and has become a promising source of renewable energies.

A hybrid algorithm combining auto-encoder network with Sparse Bayesian Regression optimized by Artificial Bee Colony for short-term Wind Power Forecasting

Li Y., Yang R.

Przegląd Elektrotechniczny

2013

R. 89, nr 2a

223--228

To forecast the short-term wind power precisely, this paper proposes a hybrid strategy which consists of a nonlinear dimensionality reduction component by auto-encoder network and a forecasting component based on Sparse Bayesian Regression optimized by Artificial Bee Colony Optimization. The proposed model can predict wind power curve per hour with a lead time of 3hours. Finally, an experiment is conducted to test the effectiveness of the forecasting model based on the detailed data from a wind farm in China.

W artykule zaproponowano hybrydową metodę przewidywania krzywej prędkości wiatru w okresie kolejnej godziny. Algorytm bazuje na nieliniowej redukcji wymiarowości przez sieć auto-enkoderową (sztuczną sieć neuronową) oraz na elemencie przewidującym, opartym na rzadkiej regresji Bayesa (ang. Sparse bayesian Regression) zoptymalizowanej metodą sztucznej koloni pszczół.