Abstract
Due to inherent randomness and fluctuation of wind speeds, it is very challenging to develop an effective and practical model to achieve accurate wind speed forecasting, especially over large forecasting horizons. This paper presents a new decomposition-optimization model created by integrating Variational Mode Decomposition (VMD), Backtracking Search Algorithm (BSA), and Regularized Extreme Learning Machine (RELM) to enhance forecasting accuracy. The observed wind speed time series is firstly decomposed by VMD into several relative stable subsequences. Then, an emerging optimization algorithm, BSA, is utilized to search the optimal parameters of the RELM. Subsequently, the well-trained RELM is constructed to do multi-step (1-, 2-, 4-, and 6-step) wind speed forecasting. Experiments have been executed with the proposed method as well as several benchmark models using several datasets from a widely-studied wind farm, Sotavento Galicia in Spain. Additionally, the effects of decomposition and optimization methods on the final forecasting results are analyzed quantitatively, whereby the importance of decomposition technique is emphasized. Results reveal that the proposed VMD-BSA-RELM model achieves significantly better performance than its rivals both on single- and multi-step forecasting with at least 50% average improvement, which indicates it is a powerful tool for short-term wind speed forecasting.
Highlights
With the massive consumption of fossil fuel and the increasing pressure of environmental protection, wind energy, one of the most major sustainable and clean energy sources, has been attracting an increasing attention in the last decades due to its remarkable features, such as broad distribution and abundant reserves [1]
auto regressive integrated moving average (ARIMA), RBF, generalized regression neural networks (GRNNs), regularized extreme learning machine (ELM) (RELM), variational mode decomposition (VMD)-RELM, and backtracking search algorithm (BSA)-RELM are used as comparison models
root mean square error (RMSE), mean absolute error (MAE), and mean absolute percent error (MAPE) values provided by these seven forecasting models on the testing data for all datasets are exhibited in Table 2, where the model with the lowest evaluation indices values are highlighted in green
Summary
With the massive consumption of fossil fuel and the increasing pressure of environmental protection, wind energy, one of the most major sustainable and clean energy sources, has been attracting an increasing attention in the last decades due to its remarkable features, such as broad distribution and abundant reserves [1]. As the Global Wind Energy Council (GWEC) have reported, over 54 GW of clean and sustainable wind power has been installed across the global market in 2016, which contains over 90 countries, including nine with over 10,000 MW installed, and 29 which have exceeded the 1000 MW mark. Affected by various factors (e.g., terrain, air pressure, temperature), wind energy is seriously intermittent, random, highly non-linear, and non-stationary, which is not conducive to the large-scale grid-connected operation of wind farms, and can bring a series of fatal problems for the safe and stable operation of power systems. Accurate and reliable wind speed forecasting can effectively mitigate the negative impacts of wind energy on the power grid. Many efforts have been done in wind speed forecasting to achieve higher wind energy utilization rates, safe and stable operation of power grids, and thereby gain more economic profits
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