Abstract
A cost-effective and efficient wind energy production trend leads to larger wind turbine generators and drive for more advanced forecast models to increase their accuracy. This paper proposes a combined forecasting model that consists of empirical mode decomposition, fuzzy group method of data handling neural network, and grey wolf optimization algorithm. A combined K-means and identifying density-based local outliers is applied to detect and clean the outliers of the raw supervisory control and data acquisition data in the proposed forecasting model. Moreover, the empirical mode decomposition is employed to decompose signals and pre-processing data. The fuzzy GMDH neural network is a forecaster engine to estimate the future amount of wind turbines energy production, where the grey wolf optimization is used to optimize the fuzzy GMDH neural network parameters in order to achieve a lower forecasting error. Moreover, the model has been applied using actual data from a pilot onshore wind farm in Sweden. The obtained results indicate that the proposed model has a higher accuracy than others in the literature and provides single and combined forecasting models in different time-steps ahead and seasons.
Highlights
Wind power industries have been tremendously expanded and are expected to progress at a compound annual growth rate (CAGR) of 5.2% between 2020 and 2027
We propose an integrated strategy that couples an empirical mode decomposition, fuzzy GMDH neural network, and grey wolf optimization algorithm (GWO) to forecast the produced power of wind turbines
In order to assess the accuracy and reliability of the proposed forecasting model, different error indicators have been used in this paper: The mean absolute percentage error (MAPE), the sum squared error (SSE), the root mean squared error (RMSE), and the mean absolute error (MAE)
Summary
Wind power industries have been tremendously expanded and are expected to progress at a compound annual growth rate (CAGR) of 5.2% between 2020 and 2027. Amjady et al (2011) provided the short-term wind power prediction dependent on the ridgelet neural network (RNN) with a high capacity estimate ability. Osório et al (2014) proposed a combined forecasting model based on mutual information, wavelet transform, particle swarm optimization, and adaptive neuro-fuzzy inference system framework to predict the short-term wind power and electricity market prices [24]. Duan et al (2021) developed a combined intelligent model based on the improved variational mode decomposition and Correntropy long short-term memory neural network to predict wind power. We propose an integrated strategy that couples an empirical mode decomposition, fuzzy GMDH (group method of data handling) neural network, and grey wolf optimization algorithm (GWO) to forecast the produced power of wind turbines. After a brief description of the SCADA system and data gathering, this section illustrates the artifificial intelligence methods prooppoosseedd iinn tthhiiss ppaappeerr
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