Short Term Wind Speed Forecasting for Wind Farms Using an Improved Autoregression Method

Zhang, Wenyu; Zhao, Zeng-Bao; Han, Tingting; Kong, Lingfeng

doi:10.1109/icm.2011.269

Cited by 8 publications

(3 citation statements)

References 3 publications

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“…They differ in the available inputs, their classification and the horizon of the prediction. Among these methods, some are based on linear models such as Linear Regression (LR), Auto-Regressive Moving Average (ARMA) and Auto-Regressive (AR) [1,2]. However, because of the nonlinear behavior of the solar radiation, researchers propose several nonlinear models based on wavelet-based methods, fuzzy models, Adaptive Neural Fuzzy Inference Systems (ANFIS) Random Forests (RF), k-Nearest Neighbors (kNN) and Artificial Neural Networks (ANN) [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation

et al. 2018

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Abstract:The solar photovoltaic (PV) energy has an important place among the renewable energy sources. Therefore, several researchers have been interested by its modelling and its prediction, in order to improve the management of the electrical systems which include PV arrays. Among the existing techniques, artificial neural networks have proved their performance in the prediction of the solar radiation. However, the existing neural network models don't satisfy the requirements of certain specific situations such as the one analyzed in this paper. The aim of this research work is to supply, with electricity, a race sailboat using exclusively renewable sources. The developed solution predicts the direct solar radiation on a horizontal surface. For that, a Nonlinear Autoregressive Exogenous (NARX) neural network is used. All the specific conditions of the sailboat operation are taken into account. The results show that the best prediction performance is obtained when the training phase of the neural network is performed periodically.

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Section: Introductionmentioning

confidence: 99%

A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation

et al. 2018

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“…,the best approximation for matrix of the coefficients is got by least squares error method [9]: This paper values =3 and =3 for the veracity and efficiency of the proposed model. For different ,the wavelet coefficients are as follows:…”

Section: B Certainty Of the Parameters Iar Modelmentioning

confidence: 99%

Wind Speed Forecasting Using a Combined Method Based on Auto Regression and Wavelet Transform

Tong

Zhao

Zhang

2012

AMR

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This paper presents a new strategy in wind speed prediction based on AR model and wavelet transform.The model uses the adjacent data for short-term wind speed forecasting and the data of the same moment in earlier days for long-term wind speed prediction at that moment,taking the similarity of wind speed at the same moment every day into account.Using the new model to analyze the wind speed of An-xi,China in April,2010,this paper concludes that the model is effective for that the correlation coefficient between the predicted value and the original data is larger than 0.8 when the prediction is less than 48 hours;while the prediction time is long ahead (48-120h),the error is acceptable (within 40%),which demonstrates that the new method is a novel and good idea for prediction on wind speed.

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“…e new energy technology such as wind power and photovoltaic power has become the focus of research at home and abroad. However, renewable energy itself has considerable randomness and volatility, which will adversely affect the stability and reliability of power grid when used in power system widely [2,3]. As a small, decentralized and independent system, microgrid is featured by self-control, protection, and management.…”

Section: Introductionmentioning

confidence: 99%

Microgrid Load Forecasting Based on Improved Long Short-Term Memory Network

Huang

Zheng²,

2022

Journal of Electrical and Computer Engineering

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With the fast growing of new energy technologies, the proportion of distributed renewable energy sources dominated by wind and light energy in the microgrid continues to increase. However, the uncertainty and randomness of energy itself bring challenges to the stable operation of the power system. Microgrid load forecasting with high accuracy is the key means to handle the above problems. It can provide help for power grid dispatching and decision-making, optimize resource allocation, reduce operation cost, and ensure system safety. In this paper, a load-forecasting algorithm for microgrid based on improved long short-term memory neural network (LSTM) is proposed. Firstly, the criticality analysis of load influencing factors is carried out, and the clustering classification and weight calculation are completed. Then, the input data is preprocessed to ensure the quality of database. Secondly, the LSTM gets improved from three aspects: multilayer convolution channel, lookahead optimizer, and AM weight. And a complete forecasting model is designed to accomplish the load forecasting. Finally, based on the data of a local microgrid in Zhejiang Province, China, simulation experiments are conducted. The results are quantitatively compared with other forecasting algorithms to verify the accuracy and superiority of the proposed algorithm.

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Short Term Wind Speed Forecasting for Wind Farms Using an Improved Autoregression Method

Cited by 8 publications

References 3 publications

A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation

A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation

Wind Speed Forecasting Using a Combined Method Based on Auto Regression and Wavelet Transform

Microgrid Load Forecasting Based on Improved Long Short-Term Memory Network

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