Articles | Volume 9, issue 6
https://doi.org/10.5194/wes-9-1431-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-9-1431-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Jun 2024
Research article |
| 27 Jun 2024
| 27 Jun 2024
Machine-learning-based estimate of the wind speed over complex terrain using the long short-term memory (LSTM) recurrent neural network
Cássia Maria Leme Beu
CORRESPONDING AUTHOR
Instituto de Pesquisas Energéticas e Nucleares (IPEN), 2242 Prof. Lineu Prestes Av., São Paulo, Brazil
Eduardo Landulfo
Instituto de Pesquisas Energéticas e Nucleares (IPEN), 2242 Prof. Lineu Prestes Av., São Paulo, Brazil
Related authors
No articles found.
Gregori de Arruda Moreira, Fábio Juliano da Silva Lopes, Juan Luis Guerrero-Rascado, Jonatan João da Silva, Antonio Arleques Gomes, Eduardo Landulfo, and Lucas Alados-Arboledas
Atmos. Meas. Tech., 12, 4261–4276, https://doi.org/10.5194/amt-12-4261-2019, https://doi.org/10.5194/amt-12-4261-2019, 2019
Short summary
Short summary
In this paper, we present a comparative analysis of the use of lidar-backscattered signals at three wavelengths (355, 532 and 1064 nm) to study the ABL by investigating high-order moments, which gives us information about the ABL height (derived using the variance method), aerosol layer movements (skewness) and mixing conditions (kurtosis) at several heights.
Gregori de Arruda Moreira, Juan Luis Guerrero-Rascado, Jose A. Benavent-Oltra, Pablo Ortiz-Amezcua, Roberto Román, Andrés E. Bedoya-Velásquez, Juan Antonio Bravo-Aranda, Francisco Jose Olmo Reyes, Eduardo Landulfo, and Lucas Alados-Arboledas
Atmos. Chem. Phys., 19, 1263–1280, https://doi.org/10.5194/acp-19-1263-2019, https://doi.org/10.5194/acp-19-1263-2019, 2019
Short summary
Short summary
In this study we show the capabilities of combining different remote sensing systems (microwave radiometer – MWR, Doppler lidar – DL – and elastic lidar – EL) for retrieving a detailed picture of the PBL turbulent features. Concerning EL, in addition to analyzing the influence of noise, we explore the use of different wavelengths, which usually includes EL systems operated in extended networks, like EARLINET, LALINET, MPLNET or SKYNET.
Igor Veselovskii, Philippe Goloub, Qiaoyun Hu, Thierry Podvin, David N. Whiteman, Mikhael Korenskiy, and Eduardo Landulfo
Atmos. Meas. Tech., 12, 119–128, https://doi.org/10.5194/amt-12-119-2019, https://doi.org/10.5194/amt-12-119-2019, 2019
Short summary
Short summary
Methane is currently the second most important greenhouse gas of anthropogenic origin (after carbon dioxide) and its concentration can be increased inside the boundary layer. So, the development of instruments for vertical profiling of the methane mixing ratio is an important task. We present the results of methane profiling in the lower troposphere using LILAS Raman lidar from the Lille University observatory platform (France).
Carlos Eduardo Souto-Oliveira, Maria de Fátima Andrade, Prashant Kumar, Fábio Juliano da Silva Lopes, Marly Babinski, and Eduardo Landulfo
Atmos. Chem. Phys., 16, 14635–14656, https://doi.org/10.5194/acp-16-14635-2016, https://doi.org/10.5194/acp-16-14635-2016, 2016
Short summary
Short summary
The Metropolitan Area of São Paulo is the biggest megacity of South America, with over 20 million inhabitants. In recent years, the region has been facing a modification in rain patterns. In this study, we evaluated the effects of local and remote sources of air pollution on cloud-condensation nuclei activation properties. Our results showed that the local vehicular traffic emission products presented more negative effects on cloud-condensation nuclei activation than the remote sources.
F. J. S. Lopes, E. Landulfo, and M. A. Vaughan
Atmos. Meas. Tech., 6, 3281–3299, https://doi.org/10.5194/amt-6-3281-2013, https://doi.org/10.5194/amt-6-3281-2013, 2013
E. G. Larroza, W. M. Nakaema, R. Bourayou, C. Hoareau, E. Landulfo, and P. Keckhut
Atmos. Meas. Tech., 6, 3197–3210, https://doi.org/10.5194/amt-6-3197-2013, https://doi.org/10.5194/amt-6-3197-2013, 2013
Related subject area
Thematic area: Wind and the atmosphere | Topic: Wind and turbulence
Method to predict the minimum measurement and experiment durations needed to achieve converged and significant results in a wind energy field experiment
Evaluation of wind farm parameterizations in the WRF model under different atmospheric stability conditions with high-resolution wake simulations
Renewable Energy Complementarity (RECom) maps – a comprehensive visualisation tool to support spatial diversification
Control-oriented modelling of wind direction variability
Machine learning methods to improve spatial predictions of coastal wind speed profiles and low-level jets using single-level ERA5 data
Offshore low-level jet observations and model representation using lidar buoy data off the California coast
Underestimation of strong wind speeds offshore in ERA5: evidence, discussion, and correction
Measurement-driven large-eddy simulations of a diurnal cycle during a wake-steering field campaign
The fractal turbulent–non-turbulent interface in the atmosphere
TOSCA – an open-source, finite-volume, large-eddy simulation (LES) environment for wind farm flows
Quantitative comparison of power production and power quality onshore and offshore: a case study from the eastern United States
The wind farm pressure field
Realistic turbulent inflow conditions for estimating the performance of a floating wind turbine
Brief communication: On the definition of the low-level jet
A decision-tree-based measure–correlate–predict approach for peak wind gust estimation from a global reanalysis dataset
Revealing inflow and wake conditions of a 6 MW floating turbine
Brief communication: A simple axial induction modification to WRF’s Fitch wind farm parameterisation
Stochastic gradient descent for wind farm optimization
Modelling the impact of trapped lee waves on offshore wind farm power output
Applying a random time mapping to Mann-modeled turbulence for the generation of intermittent wind fields
Brief communication: Impact of swell waves on atmospheric surface turbulence: A wave-turbulence decomposition method
From shear to veer: theory, statistics, and practical application
Quantification and correction of motion influence for nacelle-based lidar systems on floating wind turbines
Gaussian mixture models for the optimal sparse sampling of offshore wind resource
Dependence of turbulence estimations on nacelle lidar scanning strategies
Vertical extrapolation of Advanced Scatterometer (ASCAT) ocean surface winds using machine-learning techniques
An investigation of spatial wind direction variability and its consideration in engineering models
From gigawatt to multi-gigawatt wind farms: wake effects, energy budgets and inertial gravity waves investigated by large-eddy simulations
Investigations of correlation and coherence in turbulence from a large-eddy simulation
Validation of turbulence intensity as simulated by the Weather Research and Forecasting model off the US northeast coast
On the laminar–turbulent transition mechanism on megawatt wind turbine blades operating in atmospheric flow
Brief communication: A momentum-conserving superposition method applied to the super-Gaussian wind turbine wake model
Turbulence structures and entrainment length scales in large offshore wind farms
Effect of different source terms and inflow direction in atmospheric boundary modeling over the complex terrain site of Perdigão
Comparison of large eddy simulations against measurements from the Lillgrund offshore wind farm
Adjusted spectral correction method for calculating extreme winds in tropical-cyclone-affected water areas
The Jensen wind farm parameterization
Current and future wind energy resources in the North Sea according to CMIP6
Optimization of wind farm portfolios for minimizing overall power fluctuations at selective frequencies – a case study of the Faroe Islands
Evaluating the mesoscale spatio-temporal variability in simulated wind speed time series over northern Europe
Gaussian mixture model for extreme wind turbulence estimation
The sensitivity of the Fitch wind farm parameterization to a three-dimensional planetary boundary layer scheme
Offshore reanalysis wind speed assessment across the wind turbine rotor layer off the United States Pacific coast
Statistical post-processing of reanalysis wind speeds at hub heights using a diagnostic wind model and neural networks
Turbulence in a coastal environment: the case of Vindeby
Computational-fluid-dynamics analysis of a Darrieus vertical-axis wind turbine installation on the rooftop of buildings under turbulent-inflow conditions
Spatiotemporal observations of nocturnal low-level jets and impacts on wind power production
Computational fluid dynamics studies on wind turbine interactions with the turbulent local flow field influenced by complex topography and thermal stratification
Brief communication: How does complex terrain change the power curve of a wind turbine?
The wide range of factors contributing to wind resource assessment accuracy in complex terrain
Daniel R. Houck, Nathaniel B. de Velder, David C. Maniaci, and Brent C. Houchens
Wind Energ. Sci., 9, 1189–1209, https://doi.org/10.5194/wes-9-1189-2024, https://doi.org/10.5194/wes-9-1189-2024, 2024
Short summary
Short summary
Experiments offer incredible value to science, but results must come with an uncertainty quantification to be meaningful. We present a method to simulate a proposed experiment, calculate uncertainties, and determine the measurement duration (total time of measurements) and the experiment duration (total time to collect the required measurement data when including condition variability and time when measurement is not occurring) required to produce statistically significant and converged results.
Oscar García-Santiago, Andrea N. Hahmann, Jake Badger, and Alfredo Peña
Wind Energ. Sci., 9, 963–979, https://doi.org/10.5194/wes-9-963-2024, https://doi.org/10.5194/wes-9-963-2024, 2024
Short summary
Short summary
This study compares the results of two wind farm parameterizations (WFPs) in the Weather Research and Forecasting model, simulating a two-turbine array under three atmospheric stabilities with large-eddy simulations. We show that the WFPs accurately depict wind speeds either near turbines or in the far-wake areas, but not both. The parameterizations’ performance varies by variable (wind speed or turbulent kinetic energy) and atmospheric stability, with reduced accuracy in stable conditions.
Til Kristian Vrana and Harald G. Svendsen
Wind Energ. Sci., 9, 919–932, https://doi.org/10.5194/wes-9-919-2024, https://doi.org/10.5194/wes-9-919-2024, 2024
Short summary
Short summary
We developed new ways to plot comprehensive wind resource maps that show the revenue potential of different locations for future wind power developments. The relative capacity factor is introduced as an indicator showing the expected mean power output. The market value factor is introduced, which captures the expected mean market value relative to other wind parks. The Renewable Energy Complementarity (RECom) index combines the two into a single index, resulting in the RECom map.
Scott Dallas, Adam Stock, and Edward Hart
Wind Energ. Sci., 9, 841–867, https://doi.org/10.5194/wes-9-841-2024, https://doi.org/10.5194/wes-9-841-2024, 2024
Short summary
Short summary
This review presents the current understanding of wind direction variability in the context of control-oriented modelling of wind turbines and wind farms in a manner suitable to a wide audience. Motivation comes from the significant and commonly seen yaw error of horizontal axis wind turbines, which carries substantial negative impacts on annual energy production and the levellised cost of wind energy. Gaps in the literature are identified, and the critical challenges in this area are discussed.
Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Ville Vakkari, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée
Wind Energ. Sci., 9, 821–840, https://doi.org/10.5194/wes-9-821-2024, https://doi.org/10.5194/wes-9-821-2024, 2024
Short summary
Short summary
Knowing the wind speed across the rotor of a wind turbine is key in making good predictions of the power production. However, models struggle to capture both the speed and the shape of the wind profile. Using machine learning methods based on the model data, we show that the predictions can be improved drastically. The work focuses on three coastal sites, spread over the Northern Hemisphere (the Baltic Sea, the North Sea, and the US Atlantic coast) with similar results for all sites.
Lindsay M. Sheridan, Raghavendra Krishnamurthy, William I. Gustafson Jr., Ye Liu, Brian J. Gaudet, Nicola Bodini, Rob K. Newsom, and Mikhail Pekour
Wind Energ. Sci., 9, 741–758, https://doi.org/10.5194/wes-9-741-2024, https://doi.org/10.5194/wes-9-741-2024, 2024
Short summary
Short summary
In 2020, lidar-mounted buoys owned by the US Department of Energy (DOE) were deployed off the California coast in two wind energy lease areas and provided valuable year-long analyses of offshore low-level jet (LLJ) characteristics at heights relevant to wind turbines. In addition to the LLJ climatology, this work provides validation of LLJ representation in atmospheric models that are essential for assessing the potential energy yield of offshore wind farms.
Rémi Gandoin and Jorge Garza
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-27, https://doi.org/10.5194/wes-2024-27, 2024
Revised manuscript accepted for WES
Short summary
Short summary
ERA5 has become the workhorse of most wind resource assessment applications, as it compares better with in-situ measurements than the other reanalyses. However, for design purposes, ERA5 suffers from a drawback: it underestimates strong wind speeds offshore (approx. from 10 m/s). This is not widely discussed in the scientific literature. We address this bias and proposes a simple, robust correction. This article support the growing need for use-case specific validations of reanalysis datasets.
Eliot Quon
Wind Energ. Sci., 9, 495–518, https://doi.org/10.5194/wes-9-495-2024, https://doi.org/10.5194/wes-9-495-2024, 2024
Short summary
Short summary
Engineering models used to design wind farms generally do not account for realistic atmospheric conditions that can rapidly evolve from minute to minute. This paper uses a first-principles simulation technique to predict the performance of five wind turbines during a wind farm control experiment. Challenges included limited observations and atypical conditions. The simulation accurately predicts the aerodynamics of a turbine when it is situated partially within the wake of an upstream turbine.
Lars Neuhaus, Matthias Wächter, and Joachim Peinke
Wind Energ. Sci., 9, 439–452, https://doi.org/10.5194/wes-9-439-2024, https://doi.org/10.5194/wes-9-439-2024, 2024
Short summary
Short summary
Future wind turbines reach unprecedented heights and are affected by wind conditions that have not yet been studied in detail. With increasing height, a transition to laminar conditions with a turbulent–non-turbulent interface (TNTI) becomes more likely. In this paper, the presence and fractality of this TNTI in the atmosphere are studied. Typical fractalities known from ideal laboratory and numerical studies and a frequent occurrence of the TNTI at heights of multi-megawatt turbines are found.
Sebastiano Stipa, Arjun Ajay, Dries Allaerts, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 297–320, https://doi.org/10.5194/wes-9-297-2024, https://doi.org/10.5194/wes-9-297-2024, 2024
Short summary
Short summary
In the current study, we introduce TOSCA (Toolbox fOr Stratified Convective Atmospheres), an open-source computational fluid dynamics (CFD) tool, and demonstrate its capabilities by simulating the flow around a large wind farm, operating in realistic flow conditions. This is one of the grand challenges of the present decade and can yield better insight into physical phenomena that strongly affect wind farm operation but which are not yet fully understood.
Rebecca Foody, Jacob Coburn, Jeanie A. Aird, Rebecca J. Barthelmie, and Sara C. Pryor
Wind Energ. Sci., 9, 263–280, https://doi.org/10.5194/wes-9-263-2024, https://doi.org/10.5194/wes-9-263-2024, 2024
Short summary
Short summary
Using lidar-derived wind speed measurements at approx. 150 m height at onshore and offshore locations, we quantify the advantages of deploying wind turbines offshore in terms of the amount of electrical power produced and the higher reliability and predictability of the electrical power.
Ronald B. Smith
Wind Energ. Sci., 9, 253–261, https://doi.org/10.5194/wes-9-253-2024, https://doi.org/10.5194/wes-9-253-2024, 2024
Short summary
Short summary
Recent papers have investigated the impact of turbine drag on local wind patterns, but these studies have not given a full explanation of the induced pressure field. The pressure field blocks and deflects the wind and in other ways modifies farm efficiency. Current gravity wave models are complex and provide no estimation tools. We dig deeper into the cause of the pressure field and provide approximate closed-form expressions for pressure field effects.
Cédric Raibaudo, Jean-Christophe Gilloteaux, and Laurent Perret
Wind Energ. Sci., 8, 1711–1725, https://doi.org/10.5194/wes-8-1711-2023, https://doi.org/10.5194/wes-8-1711-2023, 2023
Short summary
Short summary
The work presented here proposes interfacing experimental measurements performed in a wind tunnel with simulations conducted with the aeroelastic code FAST and applied to a floating wind turbine model under wave-induced motion. FAST simulations using experiments match well with those obtained using the inflow generation method provided by TurbSim. The highest surge motion frequencies show a significant decrease in the mean power produced by the turbine and a mitigation of the flow dynamics.
Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée
Wind Energ. Sci., 8, 1651–1658, https://doi.org/10.5194/wes-8-1651-2023, https://doi.org/10.5194/wes-8-1651-2023, 2023
Short summary
Short summary
Low-level jets (LLJs) are special types of non-ideal wind profiles affecting both wind energy production and loads on a wind turbine. However, among LLJ researchers, there is no consensus regarding which definition to use to identify these profiles. In this work, we compare two different ways of identifying the LLJ – the falloff definition and the shear definition – and argue why the shear definition is better suited to wind energy applications.
Serkan Kartal, Sukanta Basu, and Simon J. Watson
Wind Energ. Sci., 8, 1533–1551, https://doi.org/10.5194/wes-8-1533-2023, https://doi.org/10.5194/wes-8-1533-2023, 2023
Short summary
Short summary
Peak wind gust is a crucial meteorological variable for wind farm planning and operations. Unfortunately, many wind farms do not have on-site measurements of it. In this paper, we propose a machine-learning approach (called INTRIGUE, decIsioN-TRee-based wInd GUst Estimation) that utilizes numerous inputs from a public-domain reanalysis dataset, generating long-term, site-specific peak wind gust series.
Nikolas Angelou, Jakob Mann, and Camille Dubreuil-Boisclair
Wind Energ. Sci., 8, 1511–1531, https://doi.org/10.5194/wes-8-1511-2023, https://doi.org/10.5194/wes-8-1511-2023, 2023
Short summary
Short summary
This study presents the first experimental investigation using two nacelle-mounted wind lidars that reveal the upwind and downwind conditions relative to a full-scale floating wind turbine. We find that in the case of floating wind turbines with small pitch and roll oscillating motions (< 1°), the ambient turbulence is the main driving factor that determines the propagation of the wake characteristics.
Lukas Vollmer, Balthazar Arnoldus Maria Sengers, and Martin Dörenkämper
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-89, https://doi.org/10.5194/wes-2023-89, 2023
Revised manuscript accepted for WES
Short summary
Short summary
This study proposes a modification to a well-established wind farm parameterization used in mesoscale models. The wind speed at the location of the turbine, which is used to calculate power and thrust, is corrected to approximate the free wind speed. Results show that the modified parameterization produces more accurate estimates of the turbine’s power curve.
Julian Quick, Pierre-Elouan Rethore, Mads Mølgaard Pedersen, Rafael Valotta Rodrigues, and Mikkel Friis-Møller
Wind Energ. Sci., 8, 1235–1250, https://doi.org/10.5194/wes-8-1235-2023, https://doi.org/10.5194/wes-8-1235-2023, 2023
Short summary
Short summary
Wind turbine positions are often optimized to avoid wake losses. These losses depend on atmospheric conditions, such as the wind speed and direction. The typical optimization scheme involves discretizing the atmospheric inputs, then considering every possible set of these discretized inputs in every optimization iteration. This work presents stochastic gradient descent (SGD) as an alternative, which randomly samples the atmospheric conditions during every optimization iteration.
Sarah J. Ollier and Simon J. Watson
Wind Energ. Sci., 8, 1179–1200, https://doi.org/10.5194/wes-8-1179-2023, https://doi.org/10.5194/wes-8-1179-2023, 2023
Short summary
Short summary
This modelling study shows that topographic trapped lee waves (TLWs) modify flow behaviour and power output in offshore wind farms. We demonstrate that TLWs can substantially alter the wind speeds at individual wind turbines and effect the power output of the turbine and whole wind farm. The impact on wind speeds and power is dependent on which part of the TLW wave cycle interacts with the wind turbines and wind farm. Positive and negative impacts of TLWs on power output are observed.
Khaled Yassin, Arne Helms, Daniela Moreno, Hassan Kassem, Leo Höning, and Laura J. Lukassen
Wind Energ. Sci., 8, 1133–1152, https://doi.org/10.5194/wes-8-1133-2023, https://doi.org/10.5194/wes-8-1133-2023, 2023
Short summary
Short summary
The current turbulent wind field models stated in the IEC 61400-1 standard underestimate the probability of extreme changes in wind velocity. This underestimation can lead to the false calculation of extreme and fatigue loads on the turbine. In this work, we are trying to apply a random time-mapping technique to one of the standard turbulence models to adapt to such extreme changes. The turbulent fields generated are compared with a standard wind field to show the effects of this new mapping.
Mostafa Bakhoday Paskyabi
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-62, https://doi.org/10.5194/wes-2023-62, 2023
Revised manuscript accepted for WES
Short summary
Short summary
The momentum and energy exchange between atmosphere and ocean are controlled by the air-sea wavy processes, affecting the levels of atmospheric turbulence and wind profiles. A more accurate representation of key air-sea interaction processes, such as wind-wave-turbulence interactions, greatly influences the design and operation of offshore wind turbines/farms. Furthermore, accounting for wave effects plays a vital role in the numerical modeling of offshore wind energy related applications.
Mark Kelly and Maarten Paul van der Laan
Wind Energ. Sci., 8, 975–998, https://doi.org/10.5194/wes-8-975-2023, https://doi.org/10.5194/wes-8-975-2023, 2023
Short summary
Short summary
The turning of the wind with height, which is known as veer, can affect wind turbine performance. Thus far meteorology has only given idealized descriptions of veer, which has not yet been related in a way readily usable for wind energy. Here we derive equations for veer in terms of meteorological quantities and provide practically usable forms in terms of measurable shear (change in wind speed with height). Flow simulations and measurements at turbine heights support these developments.
Moritz Gräfe, Vasilis Pettas, Julia Gottschall, and Po Wen Cheng
Wind Energ. Sci., 8, 925–946, https://doi.org/10.5194/wes-8-925-2023, https://doi.org/10.5194/wes-8-925-2023, 2023
Short summary
Short summary
Inflow wind field measurements from nacelle-based lidar systems offer great potential for different applications including turbine control, load validation and power performance measurements. On floating wind turbines nacelle-based lidar measurements are affected by the dynamic behavior of the floating foundations. Therefore, the effects on lidar wind speed measurements induced by floater dynamics must be well understood. A new model for quantification of these effects is introduced in our work.
Robin Marcille, Maxime Thiébaut, Pierre Tandeo, and Jean-François Filipot
Wind Energ. Sci., 8, 771–786, https://doi.org/10.5194/wes-8-771-2023, https://doi.org/10.5194/wes-8-771-2023, 2023
Short summary
Short summary
A novel data-driven method is proposed to design an optimal sensor network for the reconstruction of offshore wind resources. Based on unsupervised learning of numerical weather prediction wind data, it provides a simple yet efficient method for the siting of sensors, outperforming state-of-the-art methods for this application. It is applied in the main French offshore wind energy development areas to provide guidelines for the deployment of floating lidars for wind resource assessment.
Wei Fu, Alessandro Sebastiani, Alfredo Peña, and Jakob Mann
Wind Energ. Sci., 8, 677–690, https://doi.org/10.5194/wes-8-677-2023, https://doi.org/10.5194/wes-8-677-2023, 2023
Short summary
Short summary
Nacelle lidars with different beam scanning locations and two types of systems are considered for inflow turbulence estimations using both numerical simulations and field measurements. The turbulence estimates from a sonic anemometer at the hub height of a Vestas V52 turbine are used as references. The turbulence parameters are retrieved using the radial variances and a least-squares procedure. The findings from numerical simulations have been verified by the analysis of the field measurements.
Daniel Hatfield, Charlotte Bay Hasager, and Ioanna Karagali
Wind Energ. Sci., 8, 621–637, https://doi.org/10.5194/wes-8-621-2023, https://doi.org/10.5194/wes-8-621-2023, 2023
Short summary
Short summary
Wind observations at heights relevant to the operation of modern offshore wind farms, i.e. 100 m and more, are required to optimize their positioning and layout. Satellite wind retrievals provide observations of the wind field over large spatial areas and extensive time periods, yet their temporal resolution is limited and they are only representative at 10 m height. Machine-learning models are applied to lift these satellite winds to higher heights, directly relevant to wind energy purposes.
Anna von Brandis, Gabriele Centurelli, Jonas Schmidt, Lukas Vollmer, Bughsin' Djath, and Martin Dörenkämper
Wind Energ. Sci., 8, 589–606, https://doi.org/10.5194/wes-8-589-2023, https://doi.org/10.5194/wes-8-589-2023, 2023
Short summary
Short summary
We propose that considering large-scale wind direction changes in the computation of wind farm cluster wakes is of high relevance. Consequently, we present a new solution for engineering modeling tools that accounts for the effect of such changes in the propagation of wakes. The new model is evaluated with satellite data in the German Bight area. It has the potential to reduce uncertainty in applications such as site assessment and short-term power forecasting.
Oliver Maas
Wind Energ. Sci., 8, 535–556, https://doi.org/10.5194/wes-8-535-2023, https://doi.org/10.5194/wes-8-535-2023, 2023
Short summary
Short summary
The study compares small vs. large wind farms regarding the flow and power output with a turbulence-resolving simulation model. It shows that a large wind farm (90 km length) significantly affects the wind direction and that the wind speed is higher in the large wind farm wake. Both wind farms excite atmospheric gravity waves that also affect the power output of the wind farms.
Regis Thedin, Eliot Quon, Matthew Churchfield, and Paul Veers
Wind Energ. Sci., 8, 487–502, https://doi.org/10.5194/wes-8-487-2023, https://doi.org/10.5194/wes-8-487-2023, 2023
Short summary
Short summary
We investigate coherence and correlation and highlight their importance for disciplines like wind energy structural dynamic analysis, in which blade loading and fatigue depend on turbulence structure. We compare coherence estimates to those computed using a model suggested by international standards. We show the differences and highlight additional information that can be gained using large-eddy simulation, further improving analytical coherence models used in synthetic turbulence generators.
Sheng-Lun Tai, Larry K. Berg, Raghavendra Krishnamurthy, Rob Newsom, and Anthony Kirincich
Wind Energ. Sci., 8, 433–448, https://doi.org/10.5194/wes-8-433-2023, https://doi.org/10.5194/wes-8-433-2023, 2023
Short summary
Short summary
Turbulence intensity is critical for wind turbine design and operation as it affects wind power generation efficiency. Turbulence measurements in the marine environment are limited. We use a model to derive turbulence intensity and test how sea surface temperature data may impact the simulated turbulence intensity and atmospheric stability. The model slightly underestimates turbulence, and improved sea surface temperature data reduce the bias. Error with unrealistic mesoscale flow is identified.
Brandon Arthur Lobo, Özge Sinem Özçakmak, Helge Aagaard Madsen, Alois Peter Schaffarczyk, Michael Breuer, and Niels N. Sørensen
Wind Energ. Sci., 8, 303–326, https://doi.org/10.5194/wes-8-303-2023, https://doi.org/10.5194/wes-8-303-2023, 2023
Short summary
Short summary
Results from the DAN-AERO and aerodynamic glove projects provide significant findings. The effects of inflow turbulence on transition and wind turbine blades are compared to computational fluid dynamic simulations. It is found that the transition scenario changes even over a single revolution. The importance of a suitable choice of amplification factor is evident from the simulations. An agreement between the power spectral density plots from the experiment and large-eddy simulations is seen.
Frédéric Blondel
Wind Energ. Sci., 8, 141–147, https://doi.org/10.5194/wes-8-141-2023, https://doi.org/10.5194/wes-8-141-2023, 2023
Short summary
Short summary
Accurate wind farm flow predictions based on analytical wake models are crucial for wind farm design and layout optimization. Wake superposition methods play a key role and remain a substantial source of uncertainty. In the present work, a momentum-conserving superposition method is extended to the superposition of super-Gaussian-type velocity deficit models, allowing the full wake velocity deficit estimation and design of closely packed wind farms.
Abdul Haseeb Syed, Jakob Mann, Andreas Platis, and Jens Bange
Wind Energ. Sci., 8, 125–139, https://doi.org/10.5194/wes-8-125-2023, https://doi.org/10.5194/wes-8-125-2023, 2023
Short summary
Short summary
Wind turbines extract energy from the incoming wind flow, which needs to be recovered. In very large offshore wind farms, the energy is recovered mostly from above the wind farm in a process called entrainment. In this study, we analyzed the effect of atmospheric stability on the entrainment process in large offshore wind farms using measurements recorded by a research aircraft. This is the first time that in situ measurements are used to study the energy recovery process above wind farms.
Kartik Venkatraman, Trond-Ola Hågbo, Sophia Buckingham, and Knut Erik Teigen Giljarhus
Wind Energ. Sci., 8, 85–108, https://doi.org/10.5194/wes-8-85-2023, https://doi.org/10.5194/wes-8-85-2023, 2023
Short summary
Short summary
This paper is focused on the impact of modeling different effects, such as forest canopy and Coriolis forces, on the wind resource over a complex terrain site located near Perdigão, Portugal. A numerical model is set up and results are compared with field measurements. The results show that including a forest canopy improves the predictions close to the ground at some locations on the site, while the model with inflow from a precursor performed better at other locations.
Ishaan Sood, Elliot Simon, Athanasios Vitsas, Bart Blockmans, Gunner C. Larsen, and Johan Meyers
Wind Energ. Sci., 7, 2469–2489, https://doi.org/10.5194/wes-7-2469-2022, https://doi.org/10.5194/wes-7-2469-2022, 2022
Short summary
Short summary
In this work, we conduct a validation study to compare a numerical solver against measurements obtained from the offshore Lillgrund wind farm. By reusing a previously developed inflow turbulent dataset, the atmospheric conditions at the wind farm were recreated, and the general performance trends of the turbines were captured well. The work increases the reliability of numerical wind farm solvers while highlighting the challenges of accurately representing large wind farms using such solvers.
Xiaoli Guo Larsén and Søren Ott
Wind Energ. Sci., 7, 2457–2468, https://doi.org/10.5194/wes-7-2457-2022, https://doi.org/10.5194/wes-7-2457-2022, 2022
Short summary
Short summary
A method is developed for calculating the extreme wind in tropical-cyclone-affected water areas. The method is based on the spectral correction method that fills in the missing wind variability to the modeled time series, guided by best track data. The paper provides a detailed recipe for applying the method and the 50-year winds of equivalent 10 min temporal resolution from 10 to 150 m in several tropical-cyclone-affected regions.
Yulong Ma, Cristina L. Archer, and Ahmadreza Vasel-Be-Hagh
Wind Energ. Sci., 7, 2407–2431, https://doi.org/10.5194/wes-7-2407-2022, https://doi.org/10.5194/wes-7-2407-2022, 2022
Short summary
Short summary
Wind turbine wakes are important because they reduce the power production of wind farms and may cause unintended impacts on the weather around wind farms. Weather prediction models, like WRF and MPAS, are often used to predict both power and impacts of wind farms, but they lack an accurate treatment of wind farm wakes. We developed the Jensen wind farm parameterization, based on the existing Jensen model of an idealized wake. The Jensen parameterization is accurate and computationally efficient.
Andrea N. Hahmann, Oscar García-Santiago, and Alfredo Peña
Wind Energ. Sci., 7, 2373–2391, https://doi.org/10.5194/wes-7-2373-2022, https://doi.org/10.5194/wes-7-2373-2022, 2022
Short summary
Short summary
We explore the changes in wind energy resources in northern Europe using output from simulations from the Climate Model Intercomparison Project (CMIP6) under the high-emission scenario. Our results show that climate change does not particularly alter annual energy production in the North Sea but could affect the seasonal distribution of these resources, significantly reducing energy production during the summer from 2031 to 2050.
Turið Poulsen, Bárður A. Niclasen, Gregor Giebel, and Hans Georg Beyer
Wind Energ. Sci., 7, 2335–2350, https://doi.org/10.5194/wes-7-2335-2022, https://doi.org/10.5194/wes-7-2335-2022, 2022
Short summary
Short summary
Wind power is cheap and environmentally friendly, but it has a disadvantage: it is a variable power source. Because wind is not blowing everywhere simultaneously, optimal placement of wind farms can reduce the fluctuations.
This is explored for a small isolated area. Combining wind farms reduces wind power fluctuations for timescales up to 1–2 d. By optimally placing four wind farms, the hourly fluctuations are reduced by 15 %. These wind farms are located distant from each other.
Graziela Luzia, Andrea N. Hahmann, and Matti Juhani Koivisto
Wind Energ. Sci., 7, 2255–2270, https://doi.org/10.5194/wes-7-2255-2022, https://doi.org/10.5194/wes-7-2255-2022, 2022
Short summary
Short summary
This paper presents a comprehensive validation of time series produced by a mesoscale numerical weather model, a global reanalysis, and a wind atlas against observations by using a set of metrics that we present as requirements for wind energy integration studies. We perform a sensitivity analysis on the numerical weather model in multiple configurations, such as related to model grid spacing and nesting arrangements, to define the model setup that outperforms in various time series aspects.
Xiaodong Zhang and Anand Natarajan
Wind Energ. Sci., 7, 2135–2148, https://doi.org/10.5194/wes-7-2135-2022, https://doi.org/10.5194/wes-7-2135-2022, 2022
Short summary
Short summary
Joint probability distribution of 10 min mean wind speed and the standard deviation is proposed using the Gaussian mixture model and has been shown to agree well with 15 years of measurements. The environmental contour with a 50-year return period (extreme turbulence) is estimated. The results from the model could be taken as inputs for structural reliability analysis and uncertainty quantification of wind turbine design loads.
Alex Rybchuk, Timothy W. Juliano, Julie K. Lundquist, David Rosencrans, Nicola Bodini, and Mike Optis
Wind Energ. Sci., 7, 2085–2098, https://doi.org/10.5194/wes-7-2085-2022, https://doi.org/10.5194/wes-7-2085-2022, 2022
Short summary
Short summary
Numerical weather prediction models are used to predict how wind turbines will interact with the atmosphere. Here, we characterize the uncertainty associated with the choice of turbulence parameterization on modeled wakes. We find that simulated wind speed deficits in turbine wakes can be significantly sensitive to the choice of turbulence parameterization. As such, predictions of future generated power are also sensitive to turbulence parameterization choice.
Lindsay M. Sheridan, Raghu Krishnamurthy, Gabriel García Medina, Brian J. Gaudet, William I. Gustafson Jr., Alicia M. Mahon, William J. Shaw, Rob K. Newsom, Mikhail Pekour, and Zhaoqing Yang
Wind Energ. Sci., 7, 2059–2084, https://doi.org/10.5194/wes-7-2059-2022, https://doi.org/10.5194/wes-7-2059-2022, 2022
Short summary
Short summary
Using observations from lidar buoys, five reanalysis and analysis models that support the wind energy community are validated offshore and at rotor-level heights along the California Pacific coast. The models are found to underestimate the observed wind resource. Occasions of large model error occur in conjunction with stable atmospheric conditions, wind speeds associated with peak turbine power production, and mischaracterization of the diurnal wind speed cycle in summer months.
Sebastian Brune and Jan D. Keller
Wind Energ. Sci., 7, 1905–1918, https://doi.org/10.5194/wes-7-1905-2022, https://doi.org/10.5194/wes-7-1905-2022, 2022
Short summary
Short summary
A post-processing of the wind speed of the regional reanalysis COSMO-REA6 in Central Europe is performed based on a combined physical and statistical approach. The physical basis is provided by downscaling wind speeds with the help of a diagnostic wind model, which reduces the horizontal grid point spacing by a factor of 8. The statistical correction using a neural network based on different variables of the reanalysis leads to an improvement of 30 % in RMSE compared to COSMO-REA6.
Rieska Mawarni Putri, Etienne Cheynet, Charlotte Obhrai, and Jasna Bogunovic Jakobsen
Wind Energ. Sci., 7, 1693–1710, https://doi.org/10.5194/wes-7-1693-2022, https://doi.org/10.5194/wes-7-1693-2022, 2022
Short summary
Short summary
As offshore wind turbines' sizes are increasing, thorough knowledge of wind characteristics in the marine atmospheric boundary layer (MABL) is becoming crucial to help improve offshore wind turbine design and reliability. The present study discusses the wind characteristics at the first offshore wind farm, Vindeby, and compares them with the wind measurements at the FINO1 platform. Consistent wind characteristics are found between Vindeby measurements and the FINO1 measurements.
Pradip Zamre and Thorsten Lutz
Wind Energ. Sci., 7, 1661–1677, https://doi.org/10.5194/wes-7-1661-2022, https://doi.org/10.5194/wes-7-1661-2022, 2022
Short summary
Short summary
To get more insight into the influence of the urban-terrain flow on the performance of the rooftop-mounted two-bladed Darrieus vertical-axis wind turbine, scale resolving simulations are performed for a generic wind turbine in realistic terrain under turbulent conditions. It is found that the turbulence and skewed nature of the flow near rooftop locations have a positive impact on the performance of the wind turbine.
Eduardo Weide Luiz and Stephanie Fiedler
Wind Energ. Sci., 7, 1575–1591, https://doi.org/10.5194/wes-7-1575-2022, https://doi.org/10.5194/wes-7-1575-2022, 2022
Short summary
Short summary
This work analyses a meteorological event, called nocturnal low-level jets (NLLJs), defined as high wind speeds relatively close to the surface. There were positive and negative impacts from NLLJs. While NLLJs increased the mean power production, they also increased the variability in the wind with height. Our results imply that long NLLJ events are also larger, affecting many wind turbines at the same time. Short NLLJ events are more local, having stronger effects on power variability.
Patrick Letzgus, Giorgia Guma, and Thorsten Lutz
Wind Energ. Sci., 7, 1551–1573, https://doi.org/10.5194/wes-7-1551-2022, https://doi.org/10.5194/wes-7-1551-2022, 2022
Short summary
Short summary
The research article presents the results of a study of highly resolved numerical simulations of a wind energy test site in complex terrain that is currently under construction in the Swabian Alps in southern Germany. The numerical results emphasised the importance of considering orography, vegetation, and thermal stratification in numerical simulations to resolve the wind field decently. In this way, the effects on loads, power, and wake of the wind turbine can also be predicted well.
Niels Troldborg, Søren J. Andersen, Emily L. Hodgson, and Alexander Meyer Forsting
Wind Energ. Sci., 7, 1527–1532, https://doi.org/10.5194/wes-7-1527-2022, https://doi.org/10.5194/wes-7-1527-2022, 2022
Short summary
Short summary
This article shows that the power performance of a wind turbine may be very different in flat and complex terrain. This is an important finding because it shows that the power output of a given wind turbine is governed by not only the available wind at the position of the turbine but also how the ambient flow develops in the region behind the turbine.
Sarah Barber, Alain Schubiger, Sara Koller, Dominik Eggli, Alexander Radi, Andreas Rumpf, and Hermann Knaus
Wind Energ. Sci., 7, 1503–1525, https://doi.org/10.5194/wes-7-1503-2022, https://doi.org/10.5194/wes-7-1503-2022, 2022
Short summary
Short summary
In this work, a range of simulations are carried out with seven different wind modelling tools at five different complex terrain sites and the results compared to wind speed measurements at validation locations. This is then extended to annual energy production (AEP) estimations (without wake effects), showing that wind profile prediction accuracy does not translate directly or linearly to AEP accuracy. It is therefore vital to consider overall AEP when evaluating simulation accuracies.
Cited articles
Almeida, L. B.: Multilayer perceptrons, in: Handbook of Neural Computation, CRC Press, ISBN 9780429142772, 1997. a
Al-Shaikhi, A., Nuha, H., Mohandes, M., Rehman, S., and Adrian, M.: Vertical wind speed extrapolation model using long short-term memory and particle swarm optimization, Energ. Sci. Eng., 10, 4580–4594, https://doi.org/10.1002/ese3.1291, 2022. a, b
Bali, V., Kumar, A., and Gangwar, S.: Deep Learning based Wind Speed Forecasting-A Review, in: IEEE 2019 9th International Conference on Cloud Computing, Data Science & Engineering, 10–11 January 2019, Noida, India, https://doi.org/10.1109/confluence.2019.8776923, 2019. a, b, c
Baquero, L., Torio, H., and Leask, P.: Machine Learning Algorithms for Vertical Wind Speed Data Extrapolation: Comparison and Performance Using Mesoscale and Measured Site Data, Energies, 15, 5518, https://doi.org/10.3390/en15155518, 2022. a, b, c
Beu, C. M. L.: cassiabeu/doi.org-10.5194-wes-2023-104: v1.1, Zenodo [code], https://doi.org/10.5281/zenodo.12168778, 2024. a
Beu, C. M. L. and Landulfo, E.: Turbulence Kinetic Energy Dissipation Rate Estimate for a Low-Level Jet with Doppler Lidar Data: A Case Study, Earth Interact., 26, 112–121, https://doi.org/10.1175/ei-d-20-0027.1, 2022. a, b, c
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32 https://doi.org/10.1023/a:1010933404324, 2001. a
Cheng, C.-H. and Tsai, M.-C.: An Intelligent Time Series Model Based on Hybrid Methodology for Forecasting Concentrations of Significant Air Pollutants, Atmosphere, 13, 1055, https://doi.org/10.3390/atmos13071055, 2022. a
Dalton, A. and Bekker, B.: Exogenous atmospheric variables as wind speed predictors in machine learning, Appl. Energy, 319, 119257, https://doi.org/10.1016/j.apenergy.2022.119257, 2022. a
Efron, B. and Tibshirani, R.: An Introduction to the Bootstrap, Chapman and Hall/CRC, https://doi.org/10.1201/9780429246593, 1994. a
He, J., Yang, H., Zhou, S., Chen, J., and Chen, M.: A Dual-Attention-Mechanism Multi-Channel Convolutional LSTM for Short-Term Wind Speed Prediction, Atmosphere, 14, 71, https://doi.org/10.3390/atmos14010071, 2022. a
Jesemann, A.-S., Matthias, V., Böhner, J., and Bechtel, B.: Using Neural Network NO2-Predictions to Understand Air Quality Changes in Urban Areas – A Case Study in Hamburg, Atmosphere, 13, 1929, https://doi.org/10.3390/atmos13111929, 2022. a
Jiang, H., Wang, X., and Sun, C.: Predicting PM2.5 in the Northeast China Heavy Industrial Zone: A Semi-Supervised Learning with Spatiotemporal Features, Atmosphere, 13, 1744, https://doi.org/10.3390/atmos13111744, 2022. a
Keras: Kerasguide, https://keras.io/api/layers/recurrent_layers/lstm/ (last access: 16 July 2023), 2023. a
Klockow, D. and Targa, H. J.: Performance and results of a six-year German/Brazilian research project in the industrial area of Cubatão/SP Brazil, Pure Appl. Chem., 70, 2287–2293, https://doi.org/10.1351/pac199870122287, 1998. a, b
Liu, B., Ma, X., Guo, J., Li, H., Jin, S., Ma, Y., and Gong, W.: Estimating hub-height wind speed based on a machine learning algorithm: implications for wind energy assessment, Atmos. Chem. Phys., 23, 3181–3193, https://doi.org/10.5194/acp-23-3181-2023, 2023. a
Liu, Y., Cai, J., and Tan, G.: Multi-Level Circulation Pattern Classification Based on the Transfer Learning CNN Network, Atmosphere, 13, 1861, https://doi.org/10.3390/atmos13111861, 2022. a
Medsker, L. and Jain, L. C. (Eds.): Recurrent Neural Networks, CRC Press, https://doi.org/10.1201/9781420049176, 1999. a
Mohandes, M. A. and Rehman, S.: Wind Speed Extrapolation Using Machine Learning Methods and LiDAR Measurements, IEEE Access, 6, 77634–77642, https://doi.org/10.1109/access.2018.2883677, 2018. a, b
Morellato, L. P. C. and Haddad, C. F. B.: Introduction: The Brazilian Atlantic Forest1, Biotropica, 32, 786–792, https://doi.org/10.1111/j.1744-7429.2000.tb00618.x, 2000. a
Mustakim, R., Mamat, M., and Yew, H. T.: Towards On-Site Implementation of Multi-Step Air Pollutant Index Prediction in Malaysia Industrial Area: Comparing the NARX Neural Network and Support Vector Regression, Atmosphere, 13, 1787, https://doi.org/10.3390/atmos13111787, 2022. a
Musyimi, P. K., Sahbeni, G., Timár, G., Weidinger, T., and Székely, B.: Actual Evapotranspiration Estimation Using Sentinel-1 SAR and Sentinel-3 SLSTR Data Combined with a Gradient Boosting Machine Model in Busia County, Western Kenya, Atmosphere, 13, 1927, https://doi.org/10.3390/atmos13111927, 2022. a
Nuha, H., Mohandes, M., Rehman, S., and A-Shaikhi, A.: Vertical wind speed extrapolation using regularized extreme learning machine, FME Trans., 50, 412–421, https://doi.org/10.5937/fme2203412n, 2022. a
O'Malley, T., Bursztein, E., Long, J., et al.: KerasTuner, GitHub [code], https://github.com/keras-team/keras-tuner (last access: 21 July 2023), 2019. a
Pintor, A., Pinto, C., Mendonca, J., Pilao, R., and Pinto, P.: Insights on the use of wind speed vertical extrapolation methods, in: 20th International Conference on Renewable Energies and Power Quality, RE & PQJ, Vigo, Spain, 27–29 July 2022, https://doi.org/10.24084/repqj20.410, 2022. a, b
Ribeiro, F. N., de Oliveira, A. P., Soares, J., de Miranda, R. M., Barlage, M., and Chen, F.: Effect of sea breeze propagation on the urban boundary layer of the metropolitan region of Sao Paulo, Brazil, Atmos. Res., 214, 174–188, https://doi.org/10.1016/j.atmosres.2018.07.015, 2018. a, b
Sánchez, M. P., de Oliveira, A. P., Varona, R. P., Tito, J. V., Codato, G., Ynoue, R. Y., Ribeiro, F. N. D., Filho, E. P. M., and da Silveira, L. C.: Observational Investigation of the Low-Level Jets in the Metropolitan Region of São Paulo, Brazil, Earth Space Sci., 9, e2021EA002190, https://doi.org/10.1029/2021ea002190, 2022. a, b
Schwegmann, S., Faulhaber, J., Pfaffel, S., Yu, Z., Dörenkämper, M., Kersting, K., and Gottschall, J.: Enabling Virtual Met Masts for wind energy applications through machine learning-methods, Energy AI, 11, 100209, https://doi.org/10.1016/j.egyai.2022.100209, 2023. a, b, c
Sherstinsky, A.: Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network, Physica D, 404, 132306, https://doi.org/10.1016/j.physd.2019.132306, 2020. a
Smagulova, K. and James, A. P.: A survey on LSTM memristive neural network architectures and applications, Eur. Phys. J. Spec. Top., 228, 2313–2324, https://doi.org/10.1140/epjst/e2019-900046-x, 2019. a
Smola, A. J. and Schölkopf, B.: A tutorial on support vector regression, Stat. Comput., https://doi.org/10.1023/b:stco.0000035301.49549.88, 2004. a
Song, Y. and Wang, Y.: Global Wildfire Outlook Forecast with Neural Networks, Remote Sens., 12, 2246, https://doi.org/10.3390/rs12142246, 2020. a
Soria-Ruiz, J., Fernandez-Ordoñez, Y. M., Ambrosio-Ambrosio, J. P., Escalona-Maurice, M. J., Medina-García, G., Sotelo-Ruiz, E. D., and Ramirez-Guzman, M. E.: Flooded Extent and Depth Analysis Using Optical and SAR Remote Sensing with Machine Learning Algorithms, Atmosphere, 13, 1852, https://doi.org/10.3390/atmos13111852, 2022. a
Standen, J., Wilson, C., Vosper, S., and Clark, P.: Prediction of local wind climatology from Met Office models: Virtual Met Mast techniques, Wind Energy, 20, 411–430, https://doi.org/10.1002/we.2013, 2016. a, b
Stull, R. B. (Ed.): An Introduction to Boundary Layer Meteorology, Springer Netherlands, https://doi.org/10.1007/978-94-009-3027-8, 1988. a
Torres, M. E., Colominas, M. A., and Schlotthauer: A complete ensemble empirical mode decomposition with adaptive noise, in: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 22–27 May 2011, Prague, Czech Republic, 4144–4147, https://doi.org/10.1109/ICASSP.2011.5947265, 2011. a, b
Tukur, A., Chidiebere, O., Shittu, F., and Lawal Abdulrahman, M.: Neural Network Ensemble for Medium Term Forecast of Wind Power Generation: A Review Keyword: Artificial Neural Network, Ensemble technique, Recurrent Neural Network, Deep Learning and Deep Recurrent neural Network, Int. J. Adv. Res. Innov. Idea. Educ., 8, 1856–1865, 2022. a
Türkan, Y. S., Aydoğmuş, H. Y., and Erdal, H.: The prediction of the wind speed at different heights by machine learning methods, Int. J. Optimiz. Control, 6, 179–187, https://doi.org/10.11121/ijocta.01.2016.00315, 2016. a, b
Vassallo, D., Krishnamurthy, R., and Fernando, H. J. S.: Decreasing wind speed extrapolation error via domain-specific feature extraction and selection, Wind Energ. Sci., 5, 959–975, https://doi.org/10.5194/wes-5-959-2020, 2020. a, b, c
Vieira, B. C. and Gramani, M. F.: Serra do Mar: The Most “Tormented” Relief in Brazil, in: World Geomorphological Landscapes, Springer Netherlands, 285–297, https://doi.org/10.1007/978-94-017-8023-0_26, 2015. a
Vieira-Filho, M. S., Lehmann, C., and Fornaro, A.: Influence of local sources and topography on air quality and rainwater composition in Cubatão and São Paulo, Brazil, Atmos. Environ., 101, 200–208, https://doi.org/10.1016/j.atmosenv.2014.11.025, 2015. a
Virtanen, P., Gommers, R., Oliphant, Reddy, T., Cournapeau, Peterson, P., Weckesser, van der Walt, Wilson, J., Millman, Nelson, A. R. J., Jones, Larson, E., Carey, Feng, Y., Moore, Laxalde, D., Perktold, Henriksen, I., Quintero, Archibald, Pedregosa, and SciPy 1.0 Contributors: SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python, Nat. Meth., 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Wang, J., Li, Q., and Zeng, B.: Multi-layer cooperative combined forecasting system for short-term wind speed forecasting, Sustain. Energ. Technol. Assess., 43, 100946, https://doi.org/10.1016/j.seta.2020.100946, 2021. a, b
Yu, Y., Si, X., Hu, C., and Zhang, J.: A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures, Neural Comput., 31, 1235–1270, https://doi.org/10.1162/neco_a_01199, 2019. a, b
Zhang, Y., Wang, Y., Zhu, Y., Yang, L., Ge, L., and Luo, C.: Visibility Prediction Based on Machine Learning Algorithms, Atmosphere, 13, 1125, https://doi.org/10.3390/atmos13071125, 2022. a
Zhou, F., Huang, Z., and Zhang, C.: Carbon price forecasting based on CEEMDAN and LSTM, Appl. Energy, 311, 118601, https://doi.org/10.1016/j.apenergy.2022.118601, 2022. a, b
Zhou, J., Feng, J., Zhou, X., Li, Y., and Zhu, F.: Estimating Site-Specific Wind Speeds Using Gridded Data: A Comparison of Multiple Machine Learning Models, Atmosphere, 14, 142, https://doi.org/10.3390/atmos14010142, 2023. a
Short summary
Extrapolating the wind profile for complex terrain through the long short-term memory model outperformed the traditional power law methodology, which due to its universal nature cannot capture local features as the machine-learning methodology does. Moreover, considering the importance of investigating the wind potential and the need for alternative energy sources, it is motivating to find that a short observational campaign can produce better results than the traditional techniques.
Extrapolating the wind profile for complex terrain through the long short-term memory model...
Altmetrics
Final-revised paper
Preprint