Wind power forecasting: A systematic literature review

Maldonado, Jorge Luis; Solano, JC; Rojas-Moncayo, Marco

doi:10.1177/0309524x19891672

Cited by 39 publications

(18 citation statements)

References 40 publications

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“…A systematic literature review for wind power forecasting by Maldonado-Correa et al (2019) confirmed that ANNs are considered the most frequently applied intelligence models in the literature studies for wind power forecasting in the past 5 years. These networks provided adequate results because of their ability to capture non-linearity in wind patterns, especially for short-term and medium-term forecasting (Du et al, 2019;Maldonado-Correa et al, 2019). The simplest type of ANNs is the feed-forward NN (FFNN; Nielson et al, 2020), this network was used to predict a monthly energy production of 2.5 MW of a wind turbine.…”

Section: Ann For Wind Power Forecastingmentioning

confidence: 85%

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: Ann For Wind Power Forecastingmentioning

confidence: 85%

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

et al. 2021

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“…Due to the variable nature of solar and wind power generation, ML has been used extensively to construct short-term forecasts of these quantities, as described in other reviews (42)(43)(44)(45). Mirroring the above discussion of trends in demand estimation, some trends in solar and wind power estimation include disaggregating power generation to obtain real-time estimates, as well as employing innovative features or structural information for short-term forecasts.…”

Section: Solar and Wind Power Estimationmentioning

confidence: 99%

Machine Learning for Sustainable Energy Systems

Donti

Kolter

2021

Annu. Rev. Environ. Resour.

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In recent years, machine learning has proven to be a powerful tool for deriving insights from data. In this review, we describe ways in which machine learning has been leveraged to facilitate the development and operation of sustainable energy systems. We first provide a taxonomy of machine learning paradigms and techniques, along with a discussion of their strengths and limitations. We then provide an overview of existing research using machine learning for sustainable energy production, delivery, and storage. Finally, we identify gaps in this literature, propose future research directions, and discuss important considerations for deployment. Expected final online publication date for the Annual Review of Environment and Resources, Volume 46 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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“…A number of studies have been found to be related to short-term forecasting models of wind power generation capacity [10]. The researchers, in [11] introduced short-term wind speed forecasting of wind farms based on the least squares support vector machine (SVM) model.…”

Section: Introductionmentioning

confidence: 99%

Models for Short-Term Wind Power Forecasting Based on Improved Artificial Neural Network Using Particle Swarm Optimization and Genetic Algorithms

et al. 2020

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As sources of conventional energy are alarmingly being depleted, leveraging renewable energy sources, especially wind power, has been increasingly important in the electricity market to meet growing global demands for energy. However, the uncertainty in weather factors can cause large errors in wind power forecasts, raising the cost of power reservation in the power system and significantly impacting ancillary services in the electricity market. In pursuance of a higher accuracy level in wind power forecasting, this paper proposes a double-optimization approach to developing a tool for forecasting wind power generation output in the short term, using two novel models that combine an artificial neural network with the particle swarm optimization algorithm and genetic algorithm. In these models, a first particle swarm optimization algorithm is used to adjust the neural network parameters to improve accuracy. Next, the genetic algorithm or another particle swarm optimization is applied to adjust the parameters of the first particle swarm optimization algorithm to enhance the accuracy of the forecasting results. The models were tested with actual data collected from the Tuy Phong wind power plant in Binh Thuan Province, Vietnam. The testing showed improved accuracy and that this model can be widely implemented at other wind farms.

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Wind power forecasting: A systematic literature review

Cited by 39 publications

References 40 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

Machine Learning for Sustainable Energy Systems

Models for Short-Term Wind Power Forecasting Based on Improved Artificial Neural Network Using Particle Swarm Optimization and Genetic Algorithms

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