Wind turbine power output prediction model design based on artificial neural networks and climatic spatiotemporal data

Bilal, Boudy; Ndongo, Mamoudou; Adjallah, Kondo H.; Sava, Alexandre; Kébé, Cheikh Mouhamed Fadel; Ndiaye, P. D.; Sabourin, Vincent

doi:10.1109/icit.2018.8352329

Cited by 32 publications

(18 citation statements)

References 39 publications

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“…The developed methodology presents better performance and forecasting precision as compared to traditional ANN models [26]. The researchers have reported the modeling of wind turbine generator operation by ANN and LSSVM techniques under various operating parameters [27][28][29]. However, the literature concerning the generator power modeling of a large-scale power complex under various operating scenarios is scarce.…”

Section: Introductionmentioning

confidence: 99%

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management. Part 2. Power Generation

Ashraf

Uddin

Kamal³

et al. 2020

Energies

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Modern data analytics techniques and computationally inexpensive software tools are fueling the commercial applications of data-driven decision making and process optimization strategies for complex industrial operations. In this paper, modern and reliable process modeling techniques, i.e., multiple linear regression (MLR), artificial neural network (ANN), and least square support vector machine (LSSVM), are employed and comprehensively compared as reliable and robust process models for the generator power of a 660 MWe supercritical coal combustion power plant. Based on the external validation test conducted by the unseen operation data, LSSVM has outperformed the MLR and ANN models to predict the power plant’s generator power. Later, the LSSVM model is used for the failure mode recovery and a very successful operation control excellence tool. Moreover, by adjusting the thermo-electric operating parameters, the generator power on an average is increased by 1.74%, 1.80%, and 1.0 at 50% generation capacity, 75% generation capacity, and 100% generation capacity of the power plant, respectively. The process modeling based on process data and data-driven process optimization strategy building for improved process control is an actual realization of industry 4.0 in the industrial applications.

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Section: Introductionmentioning

confidence: 99%

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management. Part 2. Power Generation

Ashraf

Uddin

Kamal³

et al. 2020

Energies

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“…The results of the research from Bilal et al [1] on input and output data of four different sites in Senegal showed that higher rates of the standard deviation of wind velocity could lead to a lower average fitting rate for prediction. The authors also proved that considering other climatic variables like temperature, humidity and solar radiation could reach an improvement of 0.3% in accuracy.…”

Section: Input Parametersmentioning

confidence: 99%

“…The minimum normalised root mean square error (NRMSE) that they achieved was 0.02. Bilal et al [1] designed an MLP network to forecast the wind power of four different wind farms in Senegal. The main input of their model was wind speed, but they also assessed different combinations of input variables like wind direction, temperature, humidity and solar radiation.…”

Section: Annsmentioning

confidence: 99%

“…The performance of ANNs is dependent on many different factors, including data preprocessing, data structure, learning method, connections between input and output data and so forth [25]. There are more than 50 forms of ANNs, including multilayer perceptron (MLP) [1], wavelet neural network (WNN) [26], back-propagation neural network (BPNN), radial basis function neural network (RBFNN) [27], Elman neural network (ENN) [15], long-short-term memory (LSTM) [4], convolutional neural network (CNN) [28], etc. Designing an ANN model requires dealing with two steps: first, the selection of the proper structure of the network and then specifying the direction of the passed information.…”

Section: Annsmentioning

confidence: 99%

“…In recent years, rapid economic growth has been owed to the power production increment in different ways. Energy extracted from fossil fuels has many opponents because it leads to air pollution, ozone depletion and global warming [1]. According to the Paris agreement, for the aim of limiting the global temperature rise under 2 • C, renewable energies have to supply two-thirds of the global energy demand up to 2050 [2].…”

Section: Introductionmentioning

confidence: 99%

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A Critical Review of Wind Power Forecasting Methods—Past, Present and Future

et al. 2020

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The largest obstacle that suppresses the increase of wind power penetration within the power grid is uncertainties and fluctuations in wind speeds. Therefore, accurate wind power forecasting is a challenging task, which can significantly impact the effective operation of power systems. Wind power forecasting is also vital for planning unit commitment, maintenance scheduling and profit maximisation of power traders. The current development of cost-effective operation and maintenance methods for modern wind turbines benefits from the advancement of effective and accurate wind power forecasting approaches. This paper systematically reviewed the state-of-the-art approaches of wind power forecasting with regard to physical, statistical (time series and artificial neural networks) and hybrid methods, including factors that affect accuracy and computational time in the predictive modelling efforts. Besides, this study provided a guideline for wind power forecasting process screening, allowing the wind turbine/farm operators to identify the most appropriate predictive methods based on time horizons, input features, computational time, error measurements, etc. More specifically, further recommendations for the research community of wind power forecasting were proposed based on reviewed literature.

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A collection and categorization of open‐source wind and wind power datasets

Effenberger

Ludwig

2022

Wind Energy

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Wind power and other forms of renewable energy sources play an ever more important role in the energy supply of today's power grids. Forecasting renewable energy sources has therefore become essential in balancing the power grid. While a lot of focus is placed on new forecasting methods, little attention is given on how to compare, reproduce and transfer the methods to other use cases and data. One reason for this lack of attention is the limited availability of open‐source datasets, as many currently used datasets are non‐disclosed and make reproducibility of research impossible. This unavailability of open‐source datasets is especially prevalent in commercially interesting fields such as wind power forecasting. However, with this paper, we want to enable researchers to compare their methods on publicly available datasets by providing the, to our knowledge, largest up‐to‐date overview of existing open‐source wind power datasets, and a categorization into different groups of datasets that can be used for wind power forecasting. We show that there are publicly available datasets sufficient for wind power forecasting tasks and discuss the different data groups properties to enable researchers to choose appropriate open‐source datasets and compare their methods on them.

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Wind turbine power output prediction model design based on artificial neural networks and climatic spatiotemporal data

Cited by 32 publications

References 39 publications

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management. Part 2. Power Generation

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management. Part 2. Power Generation

A Critical Review of Wind Power Forecasting Methods—Past, Present and Future

A collection and categorization of open‐source wind and wind power datasets

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