Journal
APPLIED SOFT COMPUTING
Volume 100, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2020.106996
Keywords
Wind speed; Hybrid model; Long short-term memory (LSTM); Decomposition; Grey wolf optimizer (GWO)
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A new hybrid WSF model is developed in this paper, combining LSTM and decomposition methods, optimized with GWO. The experimental results show that the proposed hybrid model can capture the non-linear characteristics of WSTS, achieving better forecasting accuracy than single forecasting models.
Reliable and accurate wind speed forecasting (WSF) is fundamental for efficient exploitation of wind power. In particular, high accuracy short-term WSF (ST-WSF) has a significant impact on the efficiency of wind power generation systems. Due to the non-stationarity and stochasticity of the wind speed (WS), a single model is often not sufficient in practice for the accurate estimation of the WS. Hybrid models are being proposed to overcome the limitations of single models and increase the WS forecasting performance. In this paper, a new hybrid WSF model is developed based on long short-term memory (LSTM) network and decomposition methods with grey wolf optimizer (GWO). In the preprocessing stage, the missing data is filled by the weighted moving average (WMA) method, the WS time series (WSTS) data are smoothed by WMA filtering and the smoothed data are used as model input after Z-score normalization. The forecasting model is formed by the combination of a single model, a decomposition method and an advanced optimization algorithm. Successively, the hybrid WSF model is developed by combining the LSTM and decomposition methods, and optimizing the intrinsic mode function (IMF) estimated outputs with a grey wolf optimizer (GWO). The developed non-linear hybrid model is utilized on the data collected from five wind farms in the Marmara region, Turkey. The obtained experimental results indicate that the proposed combined model can capture non-linear characteristics of WSTS, achieving better forecasting performance than single forecasting models, in terms of accuracy. (c) 2020 Elsevier B.V. All rights reserved.
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