Enhancing Short-Term Wind Power Forecasting through Multiresolution Analysis and Echo State Networks

Abstract

This article suggests the application of multiresolution analysis by Wavelet Transform-WT and Echo State Networks-ESN for the development of tools capable of providing wind speed and power generation forecasting. The models were developed to forecast the hourly mean wind speeds, which are applied to the wind turbine's power curve to obtain wind power forecasts with horizons ranging from 1 to 24 h ahead, for three different locations of the Brazilian Northeast. The average improvement of Normalized Mean Absolute Error-NMAE for the first six, twelve, eighteen and twenty-four hourly power generation forecasts obtained by using the models proposed in this article were 70.87%, 71.99%, 67.77% and 58.52%, respectively. These results of improvements in relation to the Persistence Model-PM are among the best published results to date for wind power forecasting. The adopted methodology was adequate, assuring statistically reliable forecasts. When comparing the performance of fully-connected feedforward Artificial Neural Networks-ANN and ESN, it was observed that both are powerful time series forecasting tools, but the ESN proved to be more suited for wind power forecasting.

Wind Power Forecasting Using Attention-Based Recurrent Neural Networks: A Comparative Study

“…Recurrent neural networks are designed specifically for learning temporal representations of a series, which differs greatly from the classical regression algorithms borrowed from the statistical learning and data mining community. In recent years, researchers have started employing an echo state network (ESN), an instance of RNN with a sparsely connected hidden layer and randomly assigned weights, to improve the accuracy of wind power forecasting [36], [37]. Long short-term memory (LSTM) is also favored by an increasing number of researchers [38]- [40], and quite a few works are devoted to combining it with existing wind power forecasting strategies such as VMD [41], [42], Gaussian mixture model [43] and ESN [37].…”

A state-of-the-art review on wind power deterministic prediction

“…Artificial neural network is widely used in wind power prediction. The types of these neural networks include back propagation neural network (Hu and Zhang, 2018; Li et al, 2020b; Wang et al, 2015a), radial basis function neural network (Chang, 2013; Ter Borg and Rothkrantz, 2006; Zhang et al, 2016), echo state network (Dorado-Moreno et al, 2017; Gouveia et al, 2018; Wang et al, 2019a; Xu et al, 2015), and extreme learning machine (Mahmoud et al, 2018ba, 2018b; Mohammadi et al, 2015). The artificial neural network has the characteristics of self-adaptive and self-learning, which can deal with complex systems, but it has the problems of slow training speed, difficult to determine the network structure and parameters, and easy to fall into local optimum.…”

Designing, Developing, and Implementing a Forecasting Method for the Produced and Consumed Electricity in the Case of Small Wind Farms Situated on Quite Complex Hilly Terrain

“…Gouveia et al proposed [17] short-term wind power forecasting models improved by means of multiresolution analysis through Wavelet Transform and Echo State Artificial Neural Networks for the purpose of providing the necessary means for developing quality tools for predicting wind speed and produced electricity. The developed models are used to predict, with a sampling frequency of 1 h, the average wind speeds that are afterwards used in conjunction with the power curve of the wind turbine for obtaining wind power predictions with a forecasting horizon varying between 1 and 24 h ahead.…”