An improved residual-based convolutional neural network for very short-term wind power forecasting

Yıldız, Ceyhun; Açıkgöz, Hakan; Korkmaz, Deniz; Budak, Ümit

doi:10.1016/j.enconman.2020.113731

Cited by 161 publications

(44 citation statements)

References 31 publications

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“…Yildiz et al [100] proposed a novel two-step method for wind energy forecasting based on the deep learning technique. Primarily, the feature extraction and conversion of features into images were executed using the variational mode decomposition (VMD) technique.…”

Section: ) Convolutional Neural Network -Based Hybrid Approachmentioning

confidence: 99%

Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects

et al. 2021

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Globally, wind energy is growing rapidly and has received huge consideration to fulfill global energy requirements. An accurate wind power forecasting is crucial to achieve a stable and reliable operation of the power grid. However, the unpredictability and stochastic characteristics of wind power affect the grid planning and operation adversely. To address these concerns, a substantial amount of research has been carried out to introduce an efficient wind power forecasting approach. Artificial Intelligence (AI) approaches have demonstrated high precision, better generalization performance and improved learning capability, thus can be ideal to handle unstable, inflexible and intermittent wind power. Recently, AI-based hybrid approaches have become popular due to their high precision, strong adaptability and improved performance. Thus, the goal of this review paper is to present the recent progress of AI-enabled hybrid approaches for wind power forecasting emphasizing classification, structure, strength, weakness and performance analysis. Moreover, this review explores the various influential factors toward the implementations of AI-based hybrid wind power forecasting including data preprocessing, feature selection, hyperparameters adjustment, training algorithm, activation functions and evaluation process. Besides, various key issues, challenges and difficulties are discussed to identify the existing limitations and research gaps. Finally, the review delivers a few selective future proposals that would be valuable to the industrialists and researchers to develop an advanced AI-based hybrid approach for accurate wind power forecasting toward sustainable grid operation.INDEX TERMS Wind power forecasting, artificial intelligence, machine learning, deep learning, optimization, hybrid approaches.

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Section: ) Convolutional Neural Network -Based Hybrid Approachmentioning

confidence: 99%

Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects

et al. 2021

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“…CNNs process maps of traits where each element corresponds to pixels in the original image. To obtain this result it is necessary to carry out an operation called convolution [153].…”

Section: Convolutional Neural Network For Time Series Datamentioning

confidence: 99%

Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review

Ciaburro

Iannace

2021

Data

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To predict the future behavior of a system, we can exploit the information collected in the past, trying to identify recurring structures in what happened to predict what could happen, if the same structures repeat themselves in the future as well. A time series represents a time sequence of numerical values observed in the past at a measurable variable. The values are sampled at equidistant time intervals, according to an appropriate granular frequency, such as the day, week, or month, and measured according to physical units of measurement. In machine learning-based algorithms, the information underlying the knowledge is extracted from the data themselves, which are explored and analyzed in search of recurring patterns or to discover hidden causal associations or relationships. The prediction model extracts knowledge through an inductive process: the input is the data and, possibly, a first example of the expected output, the machine will then learn the algorithm to follow to obtain the same result. This paper reviews the most recent work that has used machine learning-based techniques to extract knowledge from time series data.

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“…And Abedinia, Lotfi and Bagheri et al realized the prediction of wind power by inputting the signal decomposed by improved empirical mode decomposition (IEMD) into a hybrid model based on bagging neural network combined with K-means clustering [31]. Yildiz and Acikgoz et al developed a novel residual-based convolutional neural network model for wind power forecasting based on VMD [32].…”

Section: Literature Review 121 Research Progress On Forecasting Wind Power Generationmentioning

confidence: 99%

Forecasting the wind power generation in China by seasonal grey forecasting model based on collaborative optimization

Sui

Qian

2021

RAIRO-Oper. Res.

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Renewable energy represented by wind energy plays an increasingly important role in China's national energy system. The accurate prediction of wind power generation is of great significance to China's energy planning and power grid dispatch. However, due to the late development of the wind power industry in China and the lag of power enterprise information, there are little historical data available at present. Therefore, the traditional large sample prediction method is difficult to be applied to the forecasting of wind power generation in China. For this kind of small sample and poor information problem, the grey prediction method can give a good solution. Thus, given the seasonal and long memory characteristics of the seasonal wind power generation, this paper constructs a seasonal discrete grey prediction model based on collaborative optimization. On the one hand, the model is based on moving average filtering algorithm to realize the recognition of seasonal and trend features. On the other hand, based on the optimization of fractional order and initial value, the collaborative optimization of trend and season is realized. To verify the practicability and accuracy of the proposed model, this paper uses the model to predict the quarterly wind power generation of China from 2012Q1 to 2020Q1, and compares the prediction results with the prediction results of the traditional GM(1,1) model, SGM(1,1) model and Holt-Winters model. The results are shown that the proposed model has a strong ability to capture the trend and seasonal fluctuation characteristics of wind power generation. And the long-term forecasts are valid if the existing wind power expansion capacity policy is maintained in the next four years. Based on the forecast of China’s wind power generation from 2021Q2 to 2024Q2 in the future, it is predicted that China's wind power generation will reach 239.09 TWh in the future, which will be beneficial to the realization of China's energy-saving and emission reduction targets.

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An improved residual-based convolutional neural network for very short-term wind power forecasting

Cited by 161 publications

References 31 publications

Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects

Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects

Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review

Forecasting the wind power generation in China by seasonal grey forecasting model based on collaborative optimization

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