A Novel Hybrid Strategy Using Three-Phase Feature Extraction and a Weighted Regularized Extreme Learning Machine for Multi-Step Ahead Wind Speed Prediction

Wang, Jujie; Wang, Yanfeng; Li, Yaning

doi:10.3390/en11020321

Cited by 22 publications

(9 citation statements)

References 47 publications

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“…Therefore, the output weights between hidden and output layers are determined as finding the least square solution to the given linear system. Other variants of ELM utilized in wind speed and power forecasting include Hysteresis ELM (HELM) [61], Online Sequential ELM (OSELM) [62], Stacked ELM (SELM) [63], Regularized ELM (RELM) [64], and Weighted RELM (WRELM) [65]. Reference [66] discussed in detail the trends in ELM.…”

Section: ) Artificial Intelligence/ Machine Learning Methodsmentioning

confidence: 99%

Current Perspective on the Accuracy of Deterministic Wind Speed and Power Forecasting

2019

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The intermittent nature of wind energy raised multiple challenges to the power systems and is the biggest challenge to declare wind energy a reliable source. One solution to overcome this problem is wind energy forecasting. A precise forecast can help to develop appropriate incentives and wellfunctioning electric markets. The paper presents a comprehensive review of existing research and current developments in deterministic wind speed and power forecasting. Firstly, we categorize wind forecasting methods into four broader classifications: input data, timescales , power output, and forecasting method. Secondly, the performance of wind speed and power forecasting models is evaluated based on 634 accuracy tests reported in twenty-eight published articles covering fifty locations of ten countries. From the analysis, the most significant errors were witnessed for the physical models, whereas the hybrid models showed the best performance. Although, the physical models have a large normalized root mean square error values but have small volatility. The hybrid models perform best for every time horizon. However, the errors almost doubled at the medium-term forecast from its initial value. The statistical models showed better performance than artificial intelligence models only in the very short term forecast. Overall, we observed the increase in the performance of forecasting models during the last ten years such that the normalized mean absolute error and normalized root mean square error values reduced to about half the initial values.

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Section: ) Artificial Intelligence/ Machine Learning Methodsmentioning

confidence: 99%

Current Perspective on the Accuracy of Deterministic Wind Speed and Power Forecasting

2019

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“…Wang et al proposed a three-phase signal decomposition technique to decompose wind speed and predicted multi-step ahead wind speed through feature extraction and weighted regularized extreme learning machine. Four real wind speed prediction cases verified the effectiveness of their proposed hybrid model [17]. Liu et al investigated hybrid methods using wavelet packet decomposition (WPD), empirical mode decomposition (EMD) and the ELM for wind speed prediction.…”

Section: Introductionmentioning

confidence: 92%

“…Then, multiple filters extract features in different scale to capture the mapping relationship between input data and output data. The convolution of stride is employed instead of pooling (e.g., max pooling) because stride convolution is fully differentiable and allows the network to learn its own special down-sampling [17].…”

Section: Forecasting Model Structurementioning

confidence: 99%

A Hybrid Framework for Short Term Multi-Step Wind Speed Forecasting Based on Variational Model Decomposition and Convolutional Neural Network

Zhou

Liu

et al. 2018

Energies

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Wind speed is an important factor in wind power generation. Wind speed forecasting is complicated due to its highly nonstationary character. Therefore, this paper presents a hybrid framework for the development of multi-step wind speed forecasting based on variational model decomposition and convolutional neural networks. In the first step of signal pre-processing, the variational model decomposition approach decomposes the wind speed data into several independent modes under different center pulsation. The vibrations of decomposed modes are useful for accurate wind speed forecasting. Then, the influence of different numbers of modes and the input length of the convolutional neural network are discussed to select the optimal value through calculating the errors. During the regression step, each mode is treated as a channel that constitutes the input of the forecasting model. The convolution operations in convolutional neural networks extract helpful local features in each mode and the relationships between modes for forecasting. We take advantage of the convolutional neural network and directly output multi-step forecasting results. In order to show the forecasting and generalization performance of the proposed method, wind seed data from two wind farms in Inner Mongolia, China and Sotavento Galicia, Spain with different statistical information were employed. Some classic statistical approaches were adopted for comparison. The experimental results show the satisfactory performance for all of the methods in single-step forecasting and the advantages of using decomposed modes. The root mean squared errors range from 0.79 m/s to 1.64 m/s for all of the methods. In the case of multi-step forecasting, our proposed method achieves an outstanding improvement compared with the other methods. The root mean squared error of our proposed method was 1.30 m/s while the worst performance of the other methods was 9.68 m/s. The proposed method is able to directly predict the variation trend of wind speed based on historical data with minor errors. Hence, the proposed forecasting schemes can be utilized for wind speed multi-step forecasting to cost-effectively manage wind power generation.

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“…This system achieved a mean absolute percentage error rating of 3.5%. Wang et al [167] developed a novel hybrid strategy based on a three-phase signal decomposition (TPSD) technique, feature extraction (FE) and weighted regularized extreme learning machine (WRELM) model. This model was able to do a multi-step ahead wind speed prediction.…”

Section: A Machine Learning Application In Wind Energy Forecastingmentioning

confidence: 99%

Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review

et al. 2019

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This paper conducts a comprehensive study on the application of big data and machine learning in the electrical power grid introduced through the emergence of the next-generation power system-the smart grid (SG). Connectivity lies at the core of this new grid infrastructure, which is provided by the Internet of Things (IoT). This connectivity, and constant communication required in this system, also introduced a massive data volume that demands techniques far superior to conventional methods for proper analysis and decision-making. The IoT-integrated SG system can provide efficient load forecasting and data acquisition technique along with cost-effectiveness. Big data analysis and machine learning techniques are essential to reaping these benefits. In the complex connected system of SG, cyber security becomes a critical issue; IoT devices and their data turning into major targets of attacks. Such security concerns and their solutions are also included in this paper. Key information obtained through literature review is tabulated in the corresponding sections to provide a clear synopsis; and the findings of this rigorous review are listed to give a concise picture of this area of study and promising future fields of academic and industrial research, with current limitations with viable solutions along with their effectiveness.

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A Novel Hybrid Strategy Using Three-Phase Feature Extraction and a Weighted Regularized Extreme Learning Machine for Multi-Step Ahead Wind Speed Prediction

Cited by 22 publications

References 47 publications

Current Perspective on the Accuracy of Deterministic Wind Speed and Power Forecasting

Current Perspective on the Accuracy of Deterministic Wind Speed and Power Forecasting

A Hybrid Framework for Short Term Multi-Step Wind Speed Forecasting Based on Variational Model Decomposition and Convolutional Neural Network

Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review

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