Wind power forecasting – A data-driven method along with gated recurrent neural network

Kisvari, Adam; Lin, Zhe; Liu, Xiaolei

doi:10.1016/j.renene.2020.10.119

Cited by 217 publications

(62 citation statements)

References 36 publications

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“…Consequently, it is more reasonable to consider using GRU when both the performance and training time are essential for forecasting wind speed. In fact, also for wind power forecasting, the same results confirming that the implementation of GRU is similar to the LSTM with faster convergence and less tuning were obtained by Kisvari et al (2021). To speed up the convergence, i.e., the training time of the LSTM, Yu et al (2019) proposed an enhancement technique known as LSTM-enhancement forget gate (LSTM-EFG).…”

Section: Rnn For Wind Power Forecastingmentioning

confidence: 57%

“…Nevertheless, RNN suffers from short-term memory, i.e., it cannot learn properly to preserve important information for long time sequences (Bianchini et al, 2013). Moreover, during the training process of RNN, the error gradient starts to exponentially fall until it vanishes, which interrupts the training process in the early stages (Kisvari et al, 2021). Two improved types of RNN nodes were proposed to overcome these issues, namely gated recurrent unit (GRU) and long short-term memory unit (LSTM).…”

Section: Rnns-based Methodologiesmentioning

confidence: 99%

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Machine Learning and Metaheuristic Methods for Renewable Power Forecasting: A Recent Review

et al. 2021

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The global trend toward a green sustainable future encouraged the penetration of renewable energies into the electricity sector to satisfy various demands of the market. Successful and steady integrations of renewables into the microgrids necessitate building reliable, accurate wind and solar power forecasters adopting these renewables' stochastic behaviors. In a few reported literature studies, machine learning- (ML-) based forecasters have been widely utilized for wind power and solar power forecasting with promising and accurate results. The objective of this article is to provide a critical systematic review of existing wind power and solar power ML forecasters, namely artificial neural networks (ANNs), recurrent neural networks (RNNs), support vector machines (SVMs), and extreme learning machines (ELMs). In addition, special attention is paid to metaheuristics accompanied by these ML models. Detailed comparisons of the different ML methodologies and the metaheuristic techniques are performed. The significant drawn-out findings from the reviewed papers are also summarized based on the forecasting targets and horizons in tables. Finally, challenges and future directions for research on the ML solar and wind prediction methods are presented. This review can guide scientists and engineers in analyzing and selecting the appropriate prediction approaches based on the different circumstances and applications.

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Section: Rnn For Wind Power Forecastingmentioning

confidence: 57%

Section: Rnns-based Methodologiesmentioning

confidence: 99%

Machine Learning and Metaheuristic Methods for Renewable Power Forecasting: A Recent Review

et al. 2021

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“…The increasingly prominent position of offshore wind power in renewable energy makes in-depth analysis of offshore wind power industry necessary. However, a drawback of wind as an energy source lies in its high variability (Kisvari et al 2020 ), especially for offshore wind power (Bains et al 2020 ). It is urgent to study the relevant factors affecting offshore wind power industry.…”

Section: Introductionmentioning

confidence: 99%

The influencing factors and hierarchical relationships of offshore wind power industry in China

Yang

Zhao

2021

Environ Sci Pollut Res

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As a new and cost-effective renewable energy power generation technology, offshore wind power is getting more and more attention. The development of offshore wind power industry is affected by policy-making, technology management, resources and environment, market supply and demand, and the relationship among the influencing factors is complex. This paper analyzes the factors that affect offshore wind power industry from a unique and comprehensive perspective. Fourteen factors are selected and interpretative structural model (ISM) is established to study the relationship between the influencing factors of offshore wind power industry. The results show that 14 influencing factors can be divided into five levels: the first level is the surface factors, including the economic incentive policy, operation mechanism, industrial chain, energy market mechanism, investment, and financing mechanism; the second and third levels are the intermediate factors, including generation cost, operation management, and offshore wind power technology; the fourth and fifth levels are deep-seated factors, including development planning and grid price, site selection, R&D investment, environmental protection policy, and offshore wind power supply. Deep-seated factors have a direct impact on the intermediate factors, the intermediate factors have an important impact on the surface factors, and the surface factors directly affect the development of offshore wind power industry. The influence of the 14 factors selected in this paper on offshore wind power industry is from bottom to top, from deep to shallow.

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“…Wind power production's main critical and challenging problem is its intermittent volatility, mainly to weather conditions [1], [2]; making the integration of wind turbines into the power grid not an easy task [3], [4]. Hence, accurately predicting wind power is of great interest to cope with the impacts of wind power fluctuation on power system operation.…”

Section: Introductionmentioning

confidence: 99%

An Effective Wind Power Prediction using Latent Regression Models

Bouyeddou

Harrou

Saidi

et al. 2021

2021 International Conference on ICT for Smart Society (ICISS)

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Wind power is considered one of the most promising renewable energies. Efficient prediction of wind power will support in efficiently integrating wind power in the power grid. However, the major challenge in wind power is its high fluctuation and intermittent nature, making it challenging to predict. This paper investigated and compared the performance of two commonly latent variable regression methods, namely principal component regression (PCR) and partial least squares regression (PLSR), for predicting wind power. Actual measurements recorded every 10 minutes from an actual wind turbine are used to demonstrate the prediction precision of the investigated techniques. The result showed that the prediction performances of PCR and PLSR are relatively comparable. The investigated models in this study can represent a helpful tool for model-based anomaly detection in wind turbines.

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Wind power forecasting – A data-driven method along with gated recurrent neural network

Cited by 217 publications

References 36 publications

Machine Learning and Metaheuristic Methods for Renewable Power Forecasting: A Recent Review

Machine Learning and Metaheuristic Methods for Renewable Power Forecasting: A Recent Review

The influencing factors and hierarchical relationships of offshore wind power industry in China

An Effective Wind Power Prediction using Latent Regression Models

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