Data-driven applications for wind energy analysis and prediction: The case of “La Haute Borne” wind farm

Mandzhieva, Radmila; Subhankulova, Rimma

doi:10.1016/j.dche.2022.100048

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…This is especially true of the smart city, which will steer the course of urbanization with the help of artificial intelligence. The data-driven digital economy [4] will speed up the process of incorporating digital technologies into smart city infrastructure. Roughly two-thirds of the global population will be residing in urban areas by the year 2050.…”

Section: Issn: 2789-181xmentioning

confidence: 99%

Foundational Aspects of Smart Cities Leading the Digital Economy - An Review

Pedro

2023

JCNS

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Due to the proliferation of advanced computing devices, it is now possible to attain faster data availability, data transportation, intuitive programming, and efficient infrastructures. In addition to enhancing the economy in terms of financial assets, and energy in the management of cities, the projected advantage is that it will have a positive impact on the quality of services provided to urban citizens. This article analyzes the foundational aspects of smart cities within the context of the digital economy by examining the roles of the digital economic developing in the construction of cities using China as a case study. This paper further explores the challenges facing the construction of the digital economy and smart cities, and provides a recommendation to Belarus to enhance its influence of the digital economy in their smart cities and e-government development. Based on a critical survey provided in this article, researchers and city developers are provided with insights and recommendations for bolstering the smart city's "digital radiation drive."

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Section: Issn: 2789-181xmentioning

confidence: 99%

Foundational Aspects of Smart Cities Leading the Digital Economy - An Review

Pedro

2023

JCNS

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“…In light of the dearth of wind data on actual locations, a number of methods for predicting wind power and speed have lately been created [14][15][16][17][18][19][20][21][22][23]. Additionally, three stage genetic ensemble and auxiliary predictor [24], Bayesian model averaging and ensemble learning (BMA-EL) [25], stacked recurrent neural network (SRNN) with parametric sine activation function (PSAF) algorithm [26], data-driven approach integrating data pre-processing and deep learning models [27], spatiotemporally multiple clustering algorithm and hybrid neural network method [28], deep residual gated recurrent unit (GRU) network combined with ensemble empirical mode decomposition (EEMD) and crisscross optimization algorithm (CSO) [29], machine learning [30], three improved encoder-decoder architectures (TIEDA), sequence-to-sequence bidirectional gated recurrent unit (SBIGRU), attention-based sequence-to-sequence Bi-GRU (ASBIGRU) and transformer, in natural language processing (T-NLP) [31], data-driven applications using both historical measurements and modern-era retrospective analysis [32], and wavelet transform based convolutional neural network and twin support vector regression [33] have been conducted to improve prediction accuracy in wind power forecasting. Appendix A (Tables A1 and A2) summarizes various techniques and approaches developed for the forecast of wind power and speed to understand the behavior of wind farms in different climatic condition.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking of Various Flexible Soft-Computing Strategies for the Accurate Estimation of Wind Turbine Output Power

Bilal,

Yetilmezsoy,

Ouassaid

2024

Energies

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This computational study explores the potential of several soft-computing techniques for wind turbine (WT) output power (kW) estimation based on seven input variables of wind speed (m/s), wind direction (°), air temperature (°C), pitch angle (°), generator temperature (°C), rotating speed of the generator (rpm), and voltage of the network (V). In the present analysis, a nonlinear regression-based model (NRM), three decision tree-based methods (random forest (RF), random tree (RT), and reduced error pruning tree (REPT) models), and multilayer perceptron-based soft-computing approach (artificial neural network (ANN) model) were simultaneously implemented for the first time in the prediction of WT output power (WTOP). To identify the top-performing soft computing technique, the applied models’ predictive success was compared using over 30 distinct statistical goodness-of-fit parameters. The performance assessment indices corroborated the superiority of the RF-based model over other data-intelligent models in predicting WTOP. It was seen from the results that the proposed RF-based model obtained the narrowest uncertainty bands and the lowest quantities of increased uncertainty values across all sets. Although the determination coefficient values of all competitive decision tree-based models were satisfactory, the lower percentile deviations and higher overall accuracy score of the RF-based model indicated its superior performance and higher accuracy over other competitive approaches. The generator’s rotational speed was shown to be the most useful parameter for RF-based model prediction of WTOP, according to a sensitivity study. This study highlighted the significance and capability of the implemented soft-computing strategy for better management and reliable operation of wind farms in wind energy forecasting.

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