Hourly Day-Ahead Wind Power Prediction Using the Hybrid Model of Variational Model Decomposition and Long Short-Term Memory

Abstract

A more accurate hourly prediction of day-ahead wind power can effectively reduce the uncertainty of wind power integration and improve the competitiveness of wind power in power auction markets. However, due to the inherent stochastic and intermittent nature of wind energy, it is very difficult to sharply improve the multi-step wind power forecasting (WPF) accuracy. According to theory of direct and recursive multi-step prediction, this study firstly proposes the models of R (recursive)-VMD (variational model decomposition)-LSTM (long short-term memory) and D (direct)-VMD-LSTM for the hourly forecast of day-ahead wind power by using a combination of a novel and in-depth neural network forecasting model called LSTM and the variational model decomposition (VMD) technique. The data from these model tests were obtained from two real-world wind power series from a wind farm located in Henan, China. The experimental results show that LSTM can achieve more precise predictions than traditional neural networks, and that VMD has a good self-adaptive ability to remove the stochastic volatility and retain more adequate data information than empirical mode decomposition (EMD). Secondly, the R-VMD-LSTM and D-VMD-LSTM are comparatively studied to analyze the accuracy of each step. The results verify the effectiveness of the combination of the two models: The R-VMD-LSTM model provides a more accurate prediction at the beginning of a day, while the D-VMD-LSTM model provides a more accurate prediction at the end of a day.Energies 2018, 11, 3227 2 of 20 dispatching plan in time, reduce the operating cost of the power system, and determine the appropriate wind power price [4].Wind power forecasts (WPFs) can be divided into four different types: very short-term (a few seconds to 30 min), short-term (30 min to 6 h), medium-term (6 h to 24 h), and long-term (one day and more) [5]. The China National Energy Bureau (NEB) enacted a regulation in 2011 that requires the hourly prediction of day-ahead WPFs for dispatching preparation. In addition, the maximum error of the daily forecast curve should not exceed 25%, and the root mean square error (RMSE) of the all-day forecast results should be less than 20% [6]. Due to the randomness of wind power output forecasts, wind power has brought new demands to the safe operation of the power system. Hence, day-ahead WPFs, especially for WPFs up to 1-24 h, have become a hot button issue in wind power systems and new energy domains with the implementation of large-scale wind power projects [7].The mainstream WPF methods are generally divided into physical methods and machine learning methods [8]. Physical methods aim to describe the physical process of the transformation from wind power to electric energy, and physical models rely on numerical weather prediction (NWP) [9]. Since multiple parameters are involved in NWP, and wind farms are located in sparsely populated regions, complete data can be hardly guaranteed [10]. The statistical methods applied in the WPF field are mostly time-series-based appr...

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