Performance Improvement of Artificial Neural Network Model in Short-term Forecasting of Wind Farm Power Output

Medina, Sergio Leandro Velázquez; Ajenjo, Ulises Portero

doi:10.35833/mpce.2018.000792

Cited by 41 publications

(11 citation statements)

References 17 publications

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“…In formula (17), R fg represents the construction of a numerical enhancement function, D c represents the image reflection component of the region, and m u represents the control factor of the region. In order to avoid excessive enhancement in the region, the reflection image is used to set a numerical region in the control factor to control the excessive enhancement of the region by the enhancement method.…”

Section: Construction Of Region Hierarchical Enhancement Methods For ...mentioning

confidence: 99%

“…In formula (8), C u represents the weighted sum information of the activation function, G XYZ represents the connection function of pixel points in the x-axis, y-axis and z-axis, which plays a role in suppressing image blur defects in the hidden layer, B i represents the linear combination bias model parameter of the signal in reference [17]. After the data processing of the above three levels, the functions of the function in the neural network can be divided into two categories, namely, regional linear function and nonlinear function.…”

Section: Design a Fast Correction Algorithm For Fuzzy Defect Image Re...mentioning

confidence: 99%

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Restoration and Enhancement of Fuzzy Defect Image Based on Neural Network

Zhan-Peng Cui

2023

Journal of Computers

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<p>In contrast enhancement of fuzzy defect image, details loss and noise expansion are east to occur, which brings difficulties to subsequent image analysis and defect recognition. Therefore, a fuzzy defect image restoration and enhancement method based on neural network is proposed. A double fusion neural network composed of a depth generation network and a discrimination network is designed. The residual of the denoised fuzzy image and the real image is output by the network, which is input into the discrimination network together with the real image, and the difference between the two is judged by the total loss function. To solve the problem of pixel coordinate value of fuzzy defect image, neural network is used to build a fast correction algorithm. Therefore, a fuzzy image restoration and enhancement method based on neural network is proposed to improve the image quality. By reconstructing the resolution of fuzzy defect image, a hierarchical enhancement method of fuzzy defect image region is constructed to achieve fuzzy defect image restoration and enhancement. The results show that the proposed method has high image processing ability in restoration and enhancement of fuzzy defect images. The fitting value of neural network is 0.92, which is significantly higher than that of the other two methods, indicating that the image restoration and enhancement method based on neural network has higher accuracy. Therefore, the restoration and enhancement method of fuzzy defect image based on neural network has a good restoration and enhancement effect, and can effectively meet the actual needs of people for high-quality images.</p> <p> </p>

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Section: Construction Of Region Hierarchical Enhancement Methods For ...mentioning

confidence: 99%

Section: Design a Fast Correction Algorithm For Fuzzy Defect Image Re...mentioning

confidence: 99%

Restoration and Enhancement of Fuzzy Defect Image Based on Neural Network

Zhan-Peng Cui

2023

Journal of Computers

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“…The emergence of new technologies such as artificial intelligence and big data technology, such as artificial neural networks (ANNs) [ 15 ], Markov chain (MC) [ 16 ], extreme learning machine (ELM) [ 17 ], random forests (RFs) [ 18 ], and long–short-term memory neural network (LSTM) [ 19 ], has provided a new impetus for the application of the field of wind-power-prediction systems [ 20 ]. The literature [ 21 ] compares LSTM with other prediction models, and the results show that the LSTM model outperforms other prediction models in both long-term and short-term prediction.…”

Section: Introductionmentioning

confidence: 99%

Ultra-Short-Term Offshore Wind Power Prediction Based on PCA-SSA-VMD and BiLSTM

Wang,

Ying,

Kou

et al. 2024

Sensors

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In order to realize the economic dispatch and safety stability of offshore wind farms, and to address the problems of strong randomness and strong time correlation in offshore wind power forecasting, this paper proposes a combined model of principal component analysis (PCA), sparrow algorithm (SSA), variational modal decomposition (VMD), and bidirectional long- and short-term memory neural network (BiLSTM). Firstly, the multivariate time series data were screened using the principal component analysis algorithm (PCA) to reduce the data dimensionality. Secondly, the variable modal decomposition (VMD) optimized by the SSA algorithm was applied to adaptively decompose the wind power time series data into a collection of different frequency components to eliminate the noise signals in the original data; on this basis, the hyperparameters of the BiLSTM model were optimized by integrating SSA algorithm, and the final power prediction value was obtained. Ultimately, the verification was conducted through simulation experiments; the results show that the model proposed in this paper effectively improves the prediction accuracy and verifies the effectiveness of the prediction model.

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“…A convolution operation to capture the spatialtemporal correlation between neighboring wind farms was based on the novel spatial-temporal wind power predictor (CSTWPP) [15] and a spatiotemporal convolutional network (STCN), each developed separately [16]. New ANN model predictive control-based models [14,[17][18][19][20][21][22] have been developed and offered for wind power prediction in microgrid application and use air density and wind speed as input parameters.…”

Section: Introductionmentioning

confidence: 99%

A Review of Modern Wind Power Generation Forecasting Technologies

Tsai¹,

Hong²,

Lin³

et al. 2023

Preprint

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The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power prediction results exhibit large deviations due to the uncertainty of wind power generation. In order to meet the demand for accessing large-scale wind power into the electricity grid and to further improve the accuracy of short-term wind power prediction, it is necessary to develop models for accurate and precise short-term wind power prediction based on advanced algorithms for studying the output power of a wind power generation system. This paper summarizes the contribution of the current advanced wind power forecasting technology and delineates the key advantages and disadvantages of various wind power forecasting models. These models have different forecasting capabilities, update the weights of each model in real time, improve the comprehensive forecasting capability of the model, and have good application prospects in wind power generation forecasting. Furthermore, the case studies and examples in the literature for accurately predicting ultra-short-term and short-term wind power generation with uncertainty and randomness are reviewed and analyzed. Finally, we present prospects for future studies that can serve as useful directions for other researchers planning to conduct similar experiments and investigations.

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Performance Improvement of Artificial Neural Network Model in Short-term Forecasting of Wind Farm Power Output

Cited by 41 publications

References 17 publications

Restoration and Enhancement of Fuzzy Defect Image Based on Neural Network

Restoration and Enhancement of Fuzzy Defect Image Based on Neural Network

Ultra-Short-Term Offshore Wind Power Prediction Based on PCA-SSA-VMD and BiLSTM

A Review of Modern Wind Power Generation Forecasting Technologies

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