Abstract
A wind power short-term forecasting method based on discrete wavelet transform and long short-term memory networks (DWT_LSTM) is proposed. The LSTM network is designed to effectively exhibit the dynamic behavior of the wind power time series. The discrete wavelet transform is introduced to decompose the non-stationary wind power time series into several components which have more stationarity and are easier to predict. Each component is dug by an independent LSTM. The forecasting results of the wind power are obtained by synthesizing the prediction values of all components. The prediction accuracy has been improved by the proposed method, which is validated by the MAE (mean absolute error), MAPE (mean absolute percentage error), and RMSE (root mean square error) of experimental results of three wind farms as the benchmarks. Wind power forecasting based on the proposed method provides an alternative way to improve the security and stability of the electric power network with the high penetration of wind power.
Highlights
Under the pressure of environmental pollution and an energy crisis [1], the use of renewable energy sources, such as wind power, photovoltaic power, and biomass power, is rapidly increasing as alternatives to conventional sources [2,3,4,5,6,7,8]
In order to guarantee the normal operation of the power grids and reduce additional maintenance costs, it is of great significance to study high-accuracy Wind power prediction (WPP) methods, especially the short-term wind power prediction method [24]
This paper presents a discrete wavelet transform and long short-term memory network (DWT_LSTM) WPP method based on Long short-term memory (LSTM) and discrete wavelet transform (DWT)
Summary
Under the pressure of environmental pollution and an energy crisis [1], the use of renewable energy sources, such as wind power, photovoltaic power, and biomass power, is rapidly increasing as alternatives to conventional sources [2,3,4,5,6,7,8]. Wind power generation is one of the fastest-increasing types of renewable energy generation [12,13,14,15]. Due to the intermittent and variable nature of wind power, wind power prediction [16] is of great importance for the safety [17,18], stability [19,20], and economic efficiency [21,22] of power grids. Wind power prediction (WPP) models can provide useful information about the upcoming wind power generation profile [12]. In order to guarantee the normal operation of the power grids and reduce additional maintenance costs, it is of great significance to study high-accuracy WPP methods, especially the short-term wind power prediction method [24]
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