Abstract
In the last few years, researchers have paid increasing attention to improving the accuracy of wind speed forecasting because of its vital impact on power dispatching and grid security. However, it is difficult to achieve a good forecasting performance due to the randomness and intermittency characteristics of wind speed time series. Current forecasting models based on neural network theory could adapt to various types of time series data; however, these models ignore the importance of data pre-processing and model parameter optimization, which leads to poor forecasting accuracy. In this paper, a new hybrid model is developed for short-term multi-step wind speed forecasting, which includes four modules: (1) the data pre-processing module; (2) the optimization module; (3) the hybrid nonlinear forecasting module and (4) the evaluation module. In order to estimate the forecasting ability of the proposed hybrid model, 10 min wind speed data were applied in this paper as a case study. The experimental results in six real forecasting cases indicate that the proposed hybrid model can provide not only accurate but also stable performance in terms of multi-step wind speed forecasting can be considered an effective tool in planning and dispatching for smart grids.
Highlights
Wind energy, as one of the most promising renewable energy resources (RESs), has gradually become a remarkable alternative resource to fossil energy due to being non-polluting, environmentally friendly cost effective [1]
To evaluate the forecasting performance, four forecasting error measures between the actual values and the forecasting values were employed for model evaluation and comparison: the average error (AE), mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE) [37,38]
The above experimental analysis aimed to demonstrate whether the MAE, MSE, VAR(Y), MAPE, Bias2 and IA of the proposed wavelet de-noising (WD)-APSOACO-back propagation (BP) hybrid model mean it manifests the best forecasting performance
Summary
As one of the most promising renewable energy resources (RESs), has gradually become a remarkable alternative resource to fossil energy due to being non-polluting, environmentally friendly cost effective [1]. Statistical forecasting models, which include the Auto Regressive Moving Average (ARMA) [9], Auto Regressive Integrated Moving Average (ARIMA) [10], Generalized Autoregressive Conditional (GARCH) [11], are more suitable for handling time series forecasting are simple to implement by utilizing a set of historical data. The most prevalent intelligent wind speed forecasting models are based on ANNs, which including the back propagation (BP) neural network [16], general regression neural network (GRNN) [17], radial basis function neural network (RBFNN) [18] and deep belief network (DBN) [19], among others These models possess the ability to structure the relationship between input data and output data with higher data error tolerance perform well in non-linear time series forecasting [16].
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