Long-term Wind Speed Research Articles

High-Resolution Wind Speed Estimates for the Eastern Mediterranean Basin: A Statistical Comparison Against Coastal Meteorological Observations

Wind speed (and direction) estimated from numerical weather prediction (NWP) models is essential to wind energy applications, especially in the absence of reliable fine scale spatio-temporal wind information. This study evaluates four high-resolution wind speed numerical datasets (UERRA MESCAN-SURFEX, CERRA, COSMO-REA6, and NEWA) against in situ observations from coastal meteorological stations in the eastern Mediterranean basin. The evaluation is based on statistical comparisons of long-term wind speed data from 2009 to 2018 and involves an in-depth statistical comparison as well as a preliminary wind power density assessment at or near the meteorological station locations. The results show that while all datasets provide valuable insights into regional wind variability, there are notable differences in model performance. COSMO-REA6 and UERRA exhibit higher variability in wind speed but tend to underestimate extreme values, particularly in the southern coastal areas, whereas CERRA and NEWA provided closer fits to observed wind speeds, with CERRA showing the highest correlation at most stations. NEWA data, where available, overestimate average wind speeds but capture extreme values well. The comparison reveals that while all datasets provide valuable insights into the spatial and temporal variability of wind resources, their performance varies by location and season, emphasizing the need for the careful selection and potential calibration of these models for accurate wind energy assessments. The study provides essential groundwork for leveraging these datasets in planning and optimizing offshore wind energy projects, contributing to the region’s transition to renewable energy sources.

Forecasting Short- and Long-Term Wind Speed in Limpopo Province Using Machine Learning and Extreme Value Theory

Forecasting Short- and Long-Term Wind Speed in Limpopo Province Using Machine Learning and Extreme Value Theory

Data extension of high-resolution wind speed database by fusing meteorological observation and local objective analysis data with POD–LSE

Newly generated high-resolution meteorological reanalysis data have been published over the past few years on short records. Producing high-resolution data in long-term time scales is challenging due to high computational costs and complex weather models. In contrast, urban meteorological observations have been continuously recorded for decades, although at a low spatial resolution. This study proposes a data fusion method using Proper Orthogonal Decomposition (POD) and Linear Stochastic Estimation (LSE). It fuses meteorological observations and high spatial resolution local objective analysis (LA) data to reproduce a high-resolution and long-term fusion database for urban areas without observations. First, the accuracy of LA data was verified by comparison with meteorological observations. Subsequently, the accuracy of the generated fusion data was investigated by comparison with Doppler lidar observations. Finally, the performance of single- and multi-time LSE in data fusion is discussed. These results indicate that the proposed method generates high-resolution and long-term fusion wind speed data that are similar to the Doppler lidar observations. In addition, the fusion model built using hourly data can accurately generate a highly sampled database at 10-min intervals. The single-time LSE method is recommended to be applied over the multi-time delay method for data fusion.

An Enhanced Long-Term Wind Speed Prediction Using Dynamic Unified Ensemble Learning and Data Assimilation Techniques: A Case Study in Tamil Nadu, India

Reliable wind speed prediction is essential for effective grid management since wind energy is a key component of renewable energy generation. However, controlling wind speed just right is no easy feat since the wind flow changes constantly. This paper presents an innovative method for predicting wind speeds in the future. It uses state-of-the-art data assimilation methods and a dynamic unified ensemble learning model. For efficient wind energy planning and monitoring, WSP accuracy is crucial. Data from a single site limited the accuracy of WSPs in previous research. An improved accuracy of long-term wind speed predictions is achieved by the proposed model via the integration of data assimilation and ensemble learning. To increase forecast accuracy, the model uses sophisticated data assimilation methods like the Kalman filter to combine data from many sources. Specifically, the model employs the Stacked CNN + BiLSTM with Data Assimilator (SCBLSTM+DA) technique, which integrates Wind Speed (WS) data from adjacent areas with the CNN + BiLSTM-based Ensemble Learning Model (ELM) and the Four-Dimensional Variational and Ensemble Kalman Filter (4DVar/EnKF) Data Assimilation method. Using real-world wind speed data from nine meteorological stations in Tamil Nadu, India, we find that current prediction models, including both classical statistical and cutting-edge machine learning models, perform better. Further, unlike standalone models, the suggested model shows less susceptibility to changes in prediction time scales. Promising a solution to improve long-term wind speed predictions accuracy, this study has significant consequences for wind energy management and production.

Comparison between the short-term observed and long-term estimated wind power density using Artificial Neural Networks. A case study

The economic feasibility of a wind project is dependent on the wind regime since it relies on the power output of the turbines over the installation’s working life. Consequently, the interannual variability of wind speed at a potential wind energy conversion site is an issue of capital importance. Usually a wind data measurement campaign is limited to a period no longer than one year (i.e. short-term). Therefore, the process of decision-making for wind farm constructors must be based in this short-term data. Various methods have been proposed in the scientific literature for estimation of the long-term wind speed characteristics at such sites. These methods use simultaneous measurements of the wind speed at the site in question and at one or several nearby reference sites with a long history of wind data measurements. In this paper, long-term wind power densities which have been estimated through the use Artificial Neural Networks (ANNs), will be compared to those which have been calculated by means of the short-term wind data (i.e. considered to be representative of long-term wind performance). Mean hourly wind speeds and directions calculated in a 10 year period of time at six weather stations located on six different islands in the Canarian Archipelago (Spain) were used in this study. Among the different conclusions which this study revealed, we can highlight that the wind resource estimation based on ANNs is better than that dependant on short-term wind data. This is true when the correlation coefficient between the reference and candidate weather station is of 0.6.

The univariate model for long-term wind speed forecasting based on wavelet soft threshold denoising and improved Autoformer

Accurate wind speed prediction is crucial for effective wind power grid integration and energy dispatching. Recent research has explored the combination of decomposition algorithms with forecast models to form hybrid models, aiming to enhance wind speed prediction accuracy. However, these traditional decomposition techniques often lead to high time costs in practical applications, as they require new wind speed sequences to be appended to historical long sequences for decomposition before entering the forecast model. To overcome this challenge, this study introduces and improves the Autoformer model, applying it for the first time to long-term univariate wind speed forecasting. By incorporating decomposition technology as a sub-module of the forecast model, Autoformer not only solves the high time cost issue found in conventional hybrid models but also retains the benefits of decomposition technology in time series processing.Furthermore, in this paper, the decomposition module of Autoformer is replaced with the Mixture of Expert Decomposition Module (MOEDecomp) to better extract complex trend elements of wind speed series. Combined with the auto-correlation mechanism, sequential attention is paid to wind speed for extracting time dependencies in long series. Additionally, the Wavelet Soft Threshold Denoising (WSTD) algorithm is utilised for noise reduction in wind speed sequences. To evaluate the model's performance, two multi-step forecasting strategies were employed to predict wind speeds for the forthcoming 24, 48, 72, 96, and 120 h using four datasets. Experimental results demonstrate that the proposed model surpasses 15 comparative models in terms of prediction accuracy, generalization ability, and handling uncertain data.

Probabilistic modeling of 10-min mean wind speed and its application in analytical simulation of snowdrift on building roofs

The typical resolution for long-term wind speed records that are publicly available in China is daily, this is too coarse for a sound analytical simulation of snowdrift on building roofs. Take Harbin, China as an example, an algorithm was proposed in this study to address this issue, where the commonly-used 2-parameter Weibull distribution was applied to fit the distribution of 10-min mean wind speed. A parameter estimation method, which combines the method of moment and cumulative probability, was proposed to estimate the parameters of Weibull distribution using very limited information on wind speed. The fitted probability model was validated using high-resolution wind speed data by comparing the snowdrift estimated by the modeled wind speed and that estimated by the actual wind speed. Finally, an analytical simulation of snowdrift on a flat roof was carried out to illustrate the application of the proposed model, and the probabilistic characteristic of the derived ground-to-roof conversion factors for snow loads were analyzed. It is indicated that the proposed model is easy to implement and provides a good estimation of the snowdrift on building roofs, and the derived conversion factors could be satisfactorily modeled using a Generalized Extreme Value (GEV) distribution or a normal distribution.

Offshore Wind Power Resource Assessment in the Gulf of North Suez

Growing population, industrialization, and power requirements are adversely affecting the environment through increased greenhouse gases resulting from fossil fuel burning. Global greenhouse gas mitigation targets have led nations to promote clean and self-renewable sources of energy to address this environmental issue. Offshore wind power resources are relatively more attractive due to high winds, less turbulence, minimal visualization effects, and no interaction of infrastructure. The present study aims at conducting an offshore wind power resource assessment (OWPRA) at some locations in the Gulf of North Suez. For this purpose, the long-term hourly mean wind speed (WS) and wind direction above mean sea level (AMSL), as well as temperature and pressure data near the surface, are used. The data is obtained from ERA5 (fifth generation global climate reanalysis) at six (L1–L6) chosen offshore locations. The data covers a period of 43 years, between 1979 and 2021. The WS and direction are provided at 100 m AMSL, while temperature and pressure are available near water-surface level. At the L1 to L6 locations, the log-term mean WS and wind power density (WPD) values are found to be 7.55 m/s and 370 W/m2, 6.37 m/s and 225 W/m2, 6.91 m/s and 281 W/m2, 5.48 m/s and 142 W/m2, 4.30 m/s and 77 W/m2, and 5.03 and 115 W/m2 and at 100 m AMSL, respectively. The higher magnitudes of monthly and annual windy site identifier indices (MWSI and AWSI) of 18.68 and 57.41 and 12.70 and 42.94 at the L1 and L3 sites, and generally lower values of wind variability indices, are indicative of a favorable winds source, which is also supported by higher magnitudes of mean WS, WPD, annual energy yields, plant capacity factors, and wind duration at these sites. The cost of energy for the worst and the best cases are estimated as 10.120 USD/kWh and 1.274 USD/kWh at the L5 and L1 sites, corresponding to wind turbines WT1 and WT4. Based on this analysis, sites L1, L3, and L2 are recommended for wind farm development in order of preference. The wind variability and windy site identifier indices introduced will help decision-makers in targeting potential windy sites with more confidence.

Evaluating the contribution of climate change and urbanization to the reversal in maximum surface wind speed decline: Case study in the Yangtze River Economic Belt, China

The drivers of variations in wind extremes, such as maximum surface wind speed (MSWS), are important considerations in long-term wind speed studies in urbanized regions, and the contribution of changed surface roughness has been well-examined. However, the contribution of other drivers of the variation in MSWS, including atmospheric circulation and increasing surface air temperature, remains largely unknown. This study integrated statistical methods, spatial mapping techniques, meteorological station data, and land use products to investigate the spatiotemporal influence and integrated contribution rates of surface air temperature, surface roughness, and atmospheric circulation on MSWS in the Yangtze River Economic Belt, China. The annual mean MSWS decreased (−0.662 m·s−1·10a−1) during 1973–2011 and increased (0.115 m·s−1·10a−1) during 2011–2020, whereas the seasonal changes differed significantly, with changes in the mean MSWS in winter being greater than in the other seasons. Moreover, increases in surface air temperature owing to surface roughness change, as well as atmospheric circulation (annual total explanatory power: 91.2%) contributed to MSWS changes from 1973 to 2020. The explanatory power of the integrated contribution was clearly influenced by urbanization, especially at the high urbanization level in the warm season (>86%), and the influence of atmospheric circulation was higher than that of urbanization in the cold seasons at the moderate level (>83%), implying that atmospheric circulation partially affected the key role of urbanization in the reversal of MSWS. The analysis framework of this study, which is based on the contribution rates of surface air temperature, surface roughness, and atmospheric circulation, is transferable to other cities and can be utilized to assist in both decreasing the risk of urban wind extremes and improving wind resource utilization.

An approach to on/off testing for wind farm noise assessment

In Victoria, assessment of operational wind farm noise is usually undertaken using a regression approach based on NZS6808. This adopts a comparison of long-term operational sound level and wind speed measurements with background sound levels measured prior to the construction of the wind energy facility (WEF), taken at sensitive receivers near to the site. Where no background noise levels have been measured or there are changes in the environment that may make background noise levels unrepresentative, it can be difficult to positively demonstrate compliance with required noise limits, and the results can be inconclusive. On/Off testing is described in NZS6808 as an alternative to conducting sound measurements correlated with wind data, and relies on the wind farm being turned off for a short period to allow a short-term background noise level to be determined, against which the “adjacent” operational noise levels can be compared. However, the approach is not documented in detail in NZS6808 and on/off testing is rarely undertaken in Australia. This paper describes a detailed approach that has been adopted for recent on/off testing undertaken at a large WEF in Australia and outlines the key aspects of the testing which are necessary to conduct a meaningful assessment.

Analysis of Dry-Wet Changes and the Driving Factors in Mainland China under Climate Change

Evaluation of changes in dry-wet climate is crucial in the context of global climate change to ensure regional water resources, ecosystem stability, and socio-economic development. Long-term daily meteorological data, including temperature, precipitation, relative humidity, wind speed, sunshine duration, and air pressure data from 1680 stations across mainland China from 1971 to 2019, were collected to investigate the temporal and spatial variations in aridity index (AI), precipitation (P), reference evapotranspiration (ET0), and the underlying driving climatic factors. Results indicated that the Northwest, Northeast, and Huang-Huai regions were undergoing significant wetting processes, while the Southwest and Southeast China were undergoing significant drying processes. The changing AI was mainly decided by the changing trends of ET0. For most regions, ET0 has undergone significant increases. The average increasing rate over mainland China was 3.76 mm/10a. Stations with decreasing trends were mainly located in the Tibet Plateau, Huang-Huai, and northern Northeast China. Trends in ET0 were negatively affected by the increasing changes in relative humidity and positively affected by the decreasing changes in wind speed and sunshine duration and the increasing changes in air temperature. Wind speed and relative humidity were found to be the main dominant factors driving the changes in ET0, and their contribution varied with regions. Huang-Huai and northern Northeast China showed a significant downward trend in ET0, mainly driven by the decrease in wind speed, while the increase in relative humidity was the primary contributor to the significant upward trends in ET0 across all other regions in China.

Duration of extreme synoptic wind speeds for North America in a changing climate and its engineering implementation

In modern wind engineering practice, wind load effects are often defined as a function of a basic design wind speed, an aerodynamic force coefficient, several multipliers to consider surrounding terrain, topographic effect, and the directionality of the wind climate. Among these variables, the basic design wind speed is associated with an averaging time (also known as gust duration), e.g., 0.02-second, 3-second, 10-minute, or one hour. The design level aerodynamic force coefficients are defined as a specific percentile within a reference period, representing the generic time duration of wind events, i.e., wind duration, which is assumed to be one hour. A recent study showed that the hurricane wind duration could differ from this assumption. There has not been any study that investigated synoptic wind duration and its engineering implementations comprehensively. It is also unclear whether climate change impacts the synoptic wind duration. Historical records, often less than 50 years, are insufficient to calculate the wind duration for threshold wind speeds at a higher return period. Therefore, this study employed 15 long-term hourly wind speed simulations covering most of North America, each of which has 70 years in the past (1050 years in total) and 80 years into a future changing climate (1200 years in total), to investigate the wind durations in the current and future changing climate. It was found that the durations for winds of about 700-year return period are close to the traditionally assumed one hour and have limited effects on the wind loads for the strength design. However, wind durations can be much longer than one hour for the lower return period level wind speeds, which can impact the wind responses for serviceability limit state design and cladding and component (C&C) design.

A unique Markov chain Monte Carlo method for forecasting wind power utilizing time series model

Concerns that impair human societies frequently include a heavy dependence on petroleum and coal and emissions of greenhouse gases. Thus, adopting renewable energy sources, such as wind power, has become a practical solution to this problem. Therefore, to carry out the research on wind velocity energy, a time-series structure is necessary. This study uses the Markov chain Monte Carlo approach and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to estimate short-term and long-term sustained winds. The significance of building a wind energy system is initially discussed, after which a wind velocity time-series framework based on a SARIMA is presented, followed by a short-term and Long-term wind speed projection. Furthermore, a methodology utilizing the Markov chain Monte Carlo method (MCMC) is suggested to establish a wind energy time-series analysis. This framework draws a Markov chain for time-series data on wind energy to maintain stochasticity and realize the probability transition matrix. Gibbs sampling is employed as well. The model's forecasting abilities were tested using the original database and various efficiency assessment measures, including Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) with efficiency of 13.09 and 1.03. In this study, a framework with the highest KGE and WI as well as the lowest RMSE and MAE was chosen. The findings demonstrate that the approach used in the operation provides outstanding predictability.

Assessment of Long Term Spatial-Temporal Variability of Wind Speed of Kalaburgi District, Karnataka, India

Accurate wind power estimates (based on variable wind speeds) are necessary for successful planning and investment in wind power development. In order to inform the development of wind energy, it is essential to acquire a trustworthy quantification and spatiotemporal characteristics of wind speeds. This research used the ArcGIS, Mann-Kendall (MK) test, and Sen's slope to analyze long-term wind speed data from 7 stations in the Kalaburgi District, North Eastern Region of Karnataka State, from 1981 to 2018. The findings of this study contribute to our knowledge of how wind speeds in Kalaburgi District vary and are distributed over long time scales. Over 35 years, there has been a slight and steady rise or decrease in the average wind speed (aveWS). Second, over a span of about 35 years, the annual aweWS varied greatly at various points within the Kalaburgi District. Additionally, the annual aveWS at each station showed a rising tendency. Last but not least, values of wind speeds varied geographically were not very consistent throughout the year, and gradually decreased.

Long-term uncertainty quantification in WRF-modeled offshore wind resource off the US Atlantic coast

Abstract. Uncertainty quantification of long-term modeled wind speed is essential to ensure stakeholders can best leverage wind resource numerical data sets. Offshore, this need is even stronger given the limited availability of observations of wind speed at heights relevant for wind energy purposes and the resulting heavier relative weight of numerical data sets for wind energy planning and operational projects. In this analysis, we consider the National Renewable Energy Laboratory's 21-year updated numerical offshore data set for the US East Coast and provide a methodological framework to leverage both floating lidar and near-surface buoy observations in the region to quantify uncertainty in the modeled hub-height wind resource. We first show how using a numerical ensemble to quantify the uncertainty in modeled wind speed is insufficient to fully capture the model deviation from real-world observations. Next, we train and validate a random forest to vertically extrapolate near-surface wind speed to hub height using the available short-term lidar data sets in the region. We then apply this model to vertically extrapolate the long-term near-surface buoy wind speed observations to hub height so that they can be directly compared to the long-term numerical data set. We find that the mean 21-year uncertainty in 140 m hourly average wind speed is slightly lower than 3 m s−1 (roughly 30 % of the mean observed wind speed) across the considered region. Atmospheric stability is strictly connected to the modeled wind speed uncertainty, with stable conditions associated with an uncertainty which is, on average, about 20 % larger than the overall mean uncertainty.

Trend analysis and inter-annual variability in wind speed in South Africa

Reliable forecasts of long-term longitudinal wind speed patterns may help predict air circulation changes. They are also essential for the planning and financing of wind projects for Independent Power Producers (IPPs). Long-term wind speed trends and variations in historical mean wind speed data were examined using Mann Kendall’s test and calculating inter-annual variability (IAV). The Wind Atlas for South Africa (WASA) provides high resolution quality wind data for research and applications locally. WASA has 19 meteorological stations in South Africa. The WASA project began in 2009 with the aim of mapping South Africa's wind resource, allowing stakeholders in the wind energy sector and industry to explore and prepare for utility-scale wind power generation. For the past ten years, WASA has been measuring and providing data.The results showed an increasing trend at WM01 and a decreasing trend at WM08. There was no clear trend at other wind masts. Satellite data was used to validate results and no trends were observed at most of the stations. IAV values for this study ranged between 1.088% and 3.353% which is relatively low compared to the commonly found IAV values of 5-6%, implying that mean annual wind speed variation at these stations is low. To confirm a clear trend in wind speed, it is recommended that further work be done with long term periods and substantiate our finding with the use of historical simulation and climate change scenarios.

Deep learning-based multistep ahead wind speed and power generation forecasting using direct method

Long-term effective and accurate wind power potential prediction, especially for wind farms, facilitates planning for the sustainable development of renewable energy. Accurate wind speed forecasting enhances wind power generation planning and reduces costs. Wind speed time series has nonlinearity, intermittence, and fluctuation, which makes the prediction difficult. Deep learning techniques can be beneficial when there is no specific structure to data. These techniques can predict wind speed with reasonable accuracy and reliability. In this study, four different algorithms, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and CNN-LSTM, for three different long-term horizons (6 months, 1 year, and 5 years) are successfully developed using the direct method. GRU method showed a higher degree of accuracy compared to other methods. In addition, it is confirmed that using a multivariate data set increases the model's accuracy compared to the univariate model. A computational cost analysis is also conducted to compare the proposed algorithms. Finally, the power production capacity of the wind farm at a given location, Zabol city, is calculated for the next five years, which is indispensable for planning, management, and economic analysis. The reasonable conformance between the real data and predicted ones is shown to confirm the capability of the proposed model to use in long-term wind speed forecasting.

Forecasting of Short-Term and Long-Term Wind Speed of Ras Gharib Using Time Series Analysis

Forecasting of Short-Term and Long-Term Wind Speed of Ras Gharib Using Time Series Analysis

Integrating Renewable Energy Sources in Italian Port Areas towards Renewable Energy Communities

The impact of ports on urban areas’ decarbonization to reduce air and environmental pollution effects to achieve sustainable development is undeniable, especially in the marine transportation sector. In this case, applied studies that can contribute to existing knowledge on increasing ports’ energy self-sufficiency using renewable energy sources (RESs) are critical and necessary. In this study, firstly, (1) the RESs assessment prioritization methodology was designed for Lazio ports. Additionally, (2) long-term solar radiation and wind speed were analyzed using the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) dataset of Lazio ports. Furthermore, (3) the time-series, normal-distribution and correlation methods were applied to the generated energy source, based on various parameters of the RESs used in the ports. Finally, (4) Italian port areas, towards renewable energy community (REC) scenarios, were analyzed and developed.

Time-variant post-processing method for long-term numerical wind speed forecasts based on multi-region recurrent graph network

Weather Research and Forecasting (WRF) is widely used for long-term wind speed prediction. To reduce the inherent systematic error of the WRF, a graph-based wind speed prediction model is proposed by post-processing the WRF. The proposed model contains four stages. In stage 1, the WRF model is applied to generate raw wind speed prediction results. In stage 2, Planar Maximally Filtered Graph (PMFG) is used to construct an informative graph of the wind field. In stage 3, Deep Autoencoder-like Nonnegative Matrix Factorization (DANMF) and Subgraph Alignment and Region Organization (SARO) are utilized to divide the graph into several regions. In stage 4, Multi-region Recurrent Graph Network (MRGN) model is proposed to build multiple regional models, aggregate them via time-variant ensemble weights, and generate improved long-term wind speed prediction results. The wind speed prediction results on 25 real meteorological monitoring nodes show that: (1) the proposed model outperforms WRF and 3 state-of-the-art models with a 95% confidence level; (2) the proposed model tends to produce better performance in strongly dynamic wind speed; (3) the proposed model has enough computational efficiency for practice. In conclusion, the proposed model is effective to improve WRF performance and has application potential.