Abstract
Despite the great significance of precisely forecasting the wind speed for development of the new and clean energy technology and stable grid operators, the stochasticity of wind speed makes the prediction a complex and challenging task. For improving the security and economic performance of power grids, accurate short-term wind power forecasting is crucial. In this paper, a deep learning model (Long Short-term Memory (LSTM)) has been proposed for wind speed prediction. Knowing that wind speed time series is nonlinear stochastic, the mutual information (MI) approach was used to find the best subset from the data by maximizing the joint MI between subset and target output. To enhance the accuracy and reduce input characteristics and data uncertainties, rough set and interval type-2 fuzzy set theory are combined in the proposed deep learning model. Wind speed data from an international airport station in the southern coast of Iran Bandar-Abbas City was used as the original input dataset for the optimized deep learning model. Based on the statistical results, the rough set LSTM (RST-LSTM) model showed better prediction accuracy than fuzzy and original LSTM, as well as traditional neural networks, with the lowest error for training and testing datasets in different time horizons. The suggested model can support the optimization of the control approach and the smooth procedure of power system. The results confirm the superior capabilities of deep learning techniques for wind speed forecasting, which could also inspire new applications in meteorology assessment.
Highlights
Despite the great significance of precisely forecasting the wind speed for development of the new and clean energy technology and stable grid operators, the stochasticity of wind speed makes the prediction a complex and challenging task
In order to assess the efficacy of the approach suggested, forecasting performance of long short-term memory (LSTM) is compared with regular Recurrent Neural Network (RNN) and multi-layer neural network (MLNN) models using modeling performance criteria
Throughout the training and testing phases of the 1 h ahead prediction interval, the majority of the points were placed along the diagonal line based on the scatterplots of the observed versus predicted wind speed time series produced by both the Rough Set Theory (RST)-LSTM and
Summary
The depletion of fossil fuel resources, environmental pollution, and greenhouse effect have required the development of clean and safe energy sources for power generation [1,2]. Chang et al [12] suggested an improved neural network approach containing error feedback for forecasting short-term wind speed and power generation In another piece of research, Noorollahi et al [13] used ANN models to forecast temporal and spatial wind speeds in. This paper presents short-term wind speed forecasting using LSTM network with Rough Set Theory and Interval Type-2 Fuzzy Sets, with wind speed data obtained from international airport station located in southern coast of Iran, Bandar-Abbas City, the capital of the Hormozgan province. It is Iran’s largest port city and a vital economic and commercial hub. In order to assess the efficacy of the approach suggested, forecasting performance of LSTM is compared with regular RNN and multi-layer neural network (MLNN) models using modeling performance criteria
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