A Spatio-Temporal Prediction Method of Wind Energy in Green Cloud Data Centers

Accurate and reliable prediction of renewable energy is critical to the operation and optimization of resources in cloud data centers. It is also vital to reduce energy cost and harmful gas emission. However, it is highly challenging to achieve it due to unstable characteristics of renewable energy. Traditional prediction methods are mainly time series forecasting ones, and their prediction accuracy is unsatisfactory since they ignore spatial dependence in wind speed data. This work proposes a spatio-temporal prediction method to predict the wind speed data. It adopts a Savitzky-Golay filter to smooth the wind speed data to reduce the noise interference. It learns the spatial dependence through a graph convolutional network, and adopts a gated recurrent unit to extract temporal dependence of the wind speed data. In this way, this method effectively removes the noise and obtains temporal and spatial features of the wind speed data, thereby achieving better prediction accuracy. Experimental results demonstrate that the proposed approach outperforms other baseline peers by using real-world datasets.

Automated summary

Accurate and reliable prediction of wind speed data is crucial in optimizing resources in cloud data centers. This study introduces a spatio-temporal prediction method that outperforms traditional time series forecasting approaches by considering spatial and temporal features of the data. The proposed method effectively removes noise interference and achieves better prediction accuracy using real-world datasets.