Wind Speed Data Research Articles

A Remote Monitoring System for Wind Speed and Direction Based on Non-Contact Triboelectric Nanogenerator

Wind speed and wind direction sensors are crucial sensor categories in the Internet of Things (IoT), particularly vital in fields such as meteorological monitoring, construction engineering, transportation engineering, and ocean engineering. However, power supply remains a key limiting factor for the widespread application of these sensors in large-scale sensor networks. In this paper, a self-powered, non-contact wind speed and direction sensor based on the triboelectric nanogenerator (SD-TENG) is proposed. The optimized wind speed measurement structure has a start-up wind speed as low as 0.2m/s and exhibits good linearity in the range of 0.2-29m/s. Additionally, the sensor demonstrates high temperature and humidity resistance, with a voltage attenuation of 2.4% at 45°C ambient temperature and 9.8% at 95% relative humidity. For wind direction measurement, the design of non-uniform electrodes enhances the recognition capability of different channels. To meet the demands of remote monitoring, we have designed an advanced signal processing circuit that can directly convert the raw output of a wind speed sensor into wind speed information and upload it to a cloud platform via a host. This system not only records and displays wind speed data in real-time but also features historical data storage and alarm functionalities, enhancing the intelligence and automation levels of wind speed monitoring. Additionally, users can access and analyze wind speed data through both computer and mobile devices, ensuring the system's efficiency and reliability. This work provides crucial technical support for the advancement of smart cities, clean energy, and environmental monitoring, thereby promoting the application and dissemination of IoT technology in the environmental field.

A comprehensive analysis of wind power integrated with solar and hydrogen storage systems: Case study of Java's Southern coast

Wind and solar energy are vital to the global transition toward sustainable energy systems, driven by the need to reduce fossil fuel dependence, mitigate climate change, and enhance energy security. This study aims to optimize system performance by integrating hydrogen technology and addressing capacity shortages and excess energy. First, a comprehensive analysis of wind characteristics in a strategically important area to meet unaccomplished Indonesia's 2023 wind energy targets, focusing on Java's southern coast using wind speed data from the System Advisor Model (SAM), was conducted. Four scenarios are then evaluated: wind turbine (WT)-battery energy storage system (BESS), WT-photovoltaic (PV)-BESS, WT-fuel cell (FC)-electrolyzer (EL)-hydrogen tank (H2T), and WT-PV–FC–EL-H2T. Key metrics analyzed include excess energy, Levelized Cost of Electricity (LCOE), Net Present Cost (NPC), capacity shortage (CS), and electrolyzer full-load hours (EFH). Results show that average wind speeds reach 4.2 m/s in Pandeglang, with the WT–FC–EL-H2T system being the most reliable, exhibiting a 5% capacity shortage and 68.5% excess electricity. The WT-PV–FC–EL scenario is the most cost-effective, with a COE of $0.508/kWh and an NPC of $2.6 million. These findings provide valuable insights for improving sustainable energy infrastructure in the region.

Regional frequency analysis of extreme wind in Pakistan using robust estimation methods

The quantile estimates of extreme wind speed are needed for various areas of interest using regional frequency analysis (RFA) and extreme value theory. These calculations are crucial for the coding of wind speed. The data was taken from the NASA official website at a 10-meter distance and measured in meters per second (m/s). RFA of annual maximum wind speed (AMWS) using L-moments is performed utilizing annual maximum wind speed data from sixteen sites (16) in Pakistan’s Khyber Pakhtunkhwa province. There are no sites that are found to be discordant. The wards method is used to construct a homogenous region and make two homogenous regions from 16 sites. The heterogeneity test justifies that both clusters are homogeneous. The most appropriate probability distribution from the Generalized Normal (GNO), Generalized Logistic (GLO), Pearson Type-3 (P3), Generalized Pareto (GPA), and Generalized Extreme Value (GEV) distributions is chosen to calculate regional quantiles. According to the L-moments diagram and Z statistics, the GEV for Cluster- Ι and GLO for Cluster- ΙΙ are the best suggestions from the others. Both clusters’ robustness is measured utilizing relative bias (RB) and relative root mean square error (RRMSE). Overall, GEV distribution is fit for cluster-Ι, and the GLO distribution is fit for cluster-ΙΙ. Utilizing the site mean and median as index parameters, we can also find at-site quantiles from regional quantiles. The study’s quantile estimates can be employed in codified structural designs with policy consequences.

Converging profile relationships for offshore wind speed and turbulence intensity

Abstract. This paper reduces uncertainty in the quantification of offshore wind speed and turbulence intensity. A range of industry standard relationships, including those from International Electrotechnical Commission (IEC) and International Organization for Standardization (ISO), are compared with an extensive set of met mast data collected offshore northwestern Europe over recent decades. Analysis initially focused on over 1000 independent storm peak events identified within the 10 min mean wind speed data. Time series and coherent vertical profiles were subjected to detailed scrutiny and analysis, considering wind speeds at various averaging periods, turbulence intensity and gust factors. Most peak events were associated with neutral atmospheric conditions, so they were well represented by the ISO Frøya profile, with a shear close to the IEC power law exponent of α= 0.11. A new pragmatic approximation of atmospheric stability in terms of relative shear is outlined, bringing together key elements of the IEC and ISO standards. New empirical relationships to quantify offshore wind ambient turbulence intensity are described. A simple generalised form of gust factor relationship is adopted, with a coefficient that varies with averaging period. Distinct recommendations are made for estimating peak 10 min mean winds from peaks in 1 or 3 h mean winds. Finally, a simplified workflow for the estimation of extreme offshore winds is used to show how IEC and ISO relationships can be effectively combined.

Evaluation and Long-Term Prediction of Annual Wind Farm Energy Production

A comparison and evaluation of the AEP(Annual Energy Production) of a wind farm were conducted in this study with a feasibility study and using the actual operation data from the S wind farm on Jeju Island from January 2020 to December 2022. The free wind speed data were selected from the data measured from a nacelle anemometer, the correlation equation between wind speed and AEP was obtained, and the annual average wind speed for the past 20 years was predicted using the MCP method. As a result, comparing the AEP from the operation data with that estimated in the feasibility study, we found that the AEP was reduced by approximately 2.40% in 2020 and 12.14% in 2021, and increased by 6.76% in 2022. The wind speeds over the 20-year lifetimes of the wind turbines were obtained, and the AEP that could be generated at the S wind farm indicated that it could be used for operation. In the future, the S wind farm will operate at between 25% and 30% availability for the remaining 17 years of operation. If the availability falls below 25%, there will be a need to check the reasons for the deterioration of wind turbine performance and the frequency of failures.

Assessing wind energy exploitation potential in several regions of Viet Nam using Kernel density estimation model

This article analyzes and assesses the potential for wind energy exploitation in six regions of Vietnam. The wind speed data are used to construct wind speed probability distributions (WSPDs) based on kernel density estimation (KDE). The KDE distribution, with six bandwidth selection methods, is implemented to generate probability density functions (PDFs) for each region's data to describe wind speed characteristics. The statistical tests Cramér-Von Mises (CvM), Anderson-Darling (A-D), and Kolmogorov-Smirnov (K-S) are applied to evaluate the PDFs' goodness-of-fit performance. The analysis results present the KDE distribution using the least-squares cross-validation (LSCV), and the Scott bandwidth selection method has outstanding fitting performance. Based on these PDF distributions, the wind turbine (WT) power curve is used to estimate and predict the amount of electricity that can be produced. This study also proposes a reliable method for wind power output planning based on wind speed that can be universally applied.

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.

A Climatology of the Comprehensive Climate Index and Input Values for Türkiye, 1985–2014

Spatial and temporal variations and trends in the Comprehensive Climate Index (CCI) and relevant input values are presented and discussed for fifteen locations across Türkiye. Environmental conditions that are stressful to livestock are captured in the CCI model metrics, which are determined using input of temperature, humidity, wind speed, and solar radiation data. The period from 1985 to 2014 is used to characterize the climatology of CCI across Türkiye. Given its midlatitude location, pronounced seasonality exists in the individual input values and in CCI index values. Differences across Türkiye in CCI are strongly influenced by elevation and moderated for coastal locations. Warming temperature trends and declining levels of humidity combine to create a pattern where CCI has increased at some locations and declined at other places since 1985.

Estimation methods for the variance of Birnbaum-Saunders distribution containing zero values with application to wind speed data in Thailand.

Thailand is currently grappling with a severe problem of air pollution, especially from small particulate matter (PM), which poses considerable threats to public health. The speed of the wind is pivotal in spreading these harmful particles across the atmosphere. Given the inherently unpredictable wind speed behavior, our focus lies in establishing the confidence interval (CI) for the variance of wind speed data. To achieve this, we will employ the delta-Birnbaum-Saunders (delta-BirSau) distribution. This statistical model allows for analyzing wind speed data and offers valuable insights into its variability and potential implications for air quality. The intervals are derived from ten different methods: generalized confidence interval (GCI), bootstrap confidence interval (BCI), generalized fiducial confidence interval (GFCI), and normal approximation (NA). Specifically, we apply GCI, BCI, and GFCI while considering the estimation of the proportion of zeros using the variance stabilized transformation (VST), Wilson, and Hannig methods. To evaluate the performance of these methods, we conduct a simulation study using Monte Carlo simulations in the R statistical software. The study assesses the coverage probabilities and average widths of the proposed confidence intervals. The simulation results reveal that GFCI based on the Wilson method is optimal for small sample sizes, GFCI based on the Hannig method excels for medium sample sizes, and GFCI based on the VST method stands out for large sample sizes. To further validate the practical application of these methods, we employ daily wind speed data from an industrial area in Prachin Buri and Rayong provinces, Thailand.

Spectral location for the universal scaling regime in Martian atmospheric turbulence

Atmospheric turbulence, irregular fluctuations of the fluid state, is studied on Mars. Universality of the turbulence spectrum underpins atmospheric models where computational requirements preclude full fidelity simulations of the smallest scales. However, there are discrepancies among reports on the existence and spectral location of universal scaling in Martian atmospheric data. Here, results indicate the smallest resolvable structures from Martian wind speed data are still associated with the energetic regime, which may ultimately explain why multiple reports have not found a consistent Kolmogorov-like spectral regime on Mars. Universal spectral scaling of wind data from Perseverance’s Mars Environmental Dynamics Analyzer is used to estimate the thresholds that separate three turbulence regimes: energetic, inertial, and molecular dissipation. Wind measurements at 2-Hz, the fastest sampling rate for direct wind sensor measurements on Mars, resolves turbulence in the energetic regime and approaches the inertial regime, which is consistent with reported Martian dust devil sizes.

Solar and Wind Energy Feasibility for a Water Treatment and Recirculation System Intended for Carniculture: Comparative Study Between the Cities Of Monteiro and Sousa – Paraíba – Brazil

Objective: This article presents a comparative study of renewable solar and wind energy production capacity between Monteiro and Sousa cities, located in the semi-arid region of Paraíba state, Brazil, to analyze the feasibility of using solar panels and/or wind turbines for energy supply in a water recirculation and treatment model applied to carciniculture. Method: A hypothetical-deductive method was employed and a bibliographic character was adopted in the research, taking as basis technical-scientific work information. Additionally, the research also presents quantitative theory, since it makes use of quantifiable results presented from measurable data that need to be properly expressed. A composite system was proposed, initially consisting of six identical units exposed in Figure 1, disposed under a roof, over which photovoltaic plates are placed for the supply of electrical energy to the hydraulic pumps contained in the system. The modeling study was based on wind speed and solar irradiance data in the cities of Monteiro and Sousa, with coordinates latitude -37.1214299 and longitude -7.8983769, and latitude -38.2285597 and longitude -6.7671611. These information were extracted from the Terraclimate database, using scripts written in Java and Python languages for extraction and processing. To predict wind power energy, the average wind speed in meters per second was used for each month, from January 1st, 2010 to December 31st, 2020. For modeling of photovoltaic power prediction, the average short-wave irradiance (W/m²) per month was considered, during the same period. To establish the monthly mean temperature, the average temperatures in Celsius were considered for the period from January 1st, 2010 to December 31st, 2020. For verification of the sufficiency of wind and photovoltaic systems for energy production maintenance, the energy demand of the System of Carciniculture was compared with the energy production by each of the sources considered, and based on the analysis made, it was defined which is the best energy production systemto be used and the best city where the defined system will be installed. Results and conclusions: The results show that the use of solar energy is economical, reduces social impacts generating employment and elevates environmental awareness, and has low environmental impact. The study found that solar energy generation capacity is sufficient and most suitable to keep the shrimp farming system running throughout the year, with observed variations between 226.8 kWh/month and 299.7 kWh/month in Souza city, and between 232.2 kWh/month and 369.0 kWh/month in Monteiro city, quantities more than enough for energy supply of the shrimp culture system, which presents a demand of 207.4 kWh/month. Implications of the research: The study points out that renewable energy can be an alternative more economical than other methods of energy generation, which could be a positive response to environmental and economic problems affecting the northeastern region, especially along the São Francisco River transposition, where Monteiro and Souza cities are among the last to receive it. Originality/Value: The use of solar energy is a viable solution for providing energy to shrimp culture. Additionally, the study also points out that this method may be more economical than other methods of energy generation.

Post-disaster investigations of damage feature of buildings and structures due to several local strong winds in China during 2021–2024

Local strong winds such as tornadoes and downbursts are rare in terms of measured wind speed data due to their rapid formation and strong destructive power. Post-disaster investigations have become an important approach for rating the strength of these two kind of winds and analyzing structural failure mechanism. From 2021 to 2024, several tornadoes and downbursts occurred in China. This study delineated changes in the intensity levels of wind disasters along their paths based on on-site disaster investigations and established standards for the level of structural damage based on the extent of functional loss and repair. This study focused on the damage feature to light steel structures, masonry structures, steel-concrete structures, and pole structures, and discussed the causes of the damage from the perspectives of construction and force transmission mechanisms. In addition, this study estimated the equivalent wind speeds based on the damage feature of pole structures and metal roof claddings. To reproduce the damage process for a group of high-rise buildings, the computational fluid dynamic (CFD) technology is used to simulate the airflow distribution inside buildings under two conditions of open and closed interior doors. The investigation results can provide useful information for the wind intensity classification and rating standards in China. Meanwhile, compared with the interior doors being open or closed, closing indoor doors during strong winds can significantly reduce indoor wind speeds, which is beneficial for ensuring safety.

A machine learning-based probabilistic wind speed prediction model with multi-resolution data, quantile regression and bound estimation

Wind speed prediction can be important for management of long-span cross-sea bridges, and probabilistic prediction models are needed to take uncertainty of future wind speed into consideration. However, there are two shortcomings of existing models. One is the lack of supplementary input of detailed fluctuations of wind speed, and the other is that fixed quantiles are usually selected when the predicted probability distribution is converted into a confidence interval. To this end, we propose a novel machine learning-based probabilistic model with multi-resolution data, quantile regression and bound estimation. The proposed model is composed of the distribution prediction part and the interval prediction part. In the distribution prediction part, we employ multi-resolution data as input, which can complement details of fluctuations of wind speed to reduce uncertainty. The interval prediction part employs the lower upper bound estimation method to postprocess predicted quantiles and output a superior interval than selecting fixed quantiles. The proposed model is validated on the monitoring wind speed data of two long-span cross-sea bridges, where the superiority of the distribution part and the effectiveness of the interval part are demonstrated.

Time resolution of wind speed data introduces errors in wind power density assessment

Wind energy is critical in the global shift towards renewable energy sources, necessitating accurate potential assessments for informed decision-making and strategic development in this transition. Despite the widespread use of wind speed data with varying time resolutions for estimating wind energy, the influence of this variability on the accuracy of wind energy assessment remains uncertain. In this study, we investigate the impact of wind speed data’s time resolution on wind power density (WPD) assessment, utilizing high-frequency, in-situ observations collected from eight anemometer towers in China. Through analysis of wind speed data at nine resolutions, ranging from 10-minute to monthly intervals, we uncover significant errors introduced by coarser time resolutions in WPD calculations. Specifically, it reveals a systematic underestimation of WPD with decreasing time resolution, highlighting the importance of time resolution in wind energy evaluation. While hourly wind speed data yield acceptable WPD errors, daily and monthly resolutions lead to substantial underestimation due to the smoothing effect of fluctuations. We also delve into the underlying mechanisms contributing to these errors, including variations in the power law exponent and changes in the Weibull distribution shape factor with time resolution. Furthermore, we propose a novel method to mitigate WPD calculation errors using coarse time resolution data by regressing Weibull distribution factors. The findings offer valuable insights into wind speed data selection and accurate wind energy assessment, with implications for renewable energy planning and policy-making in the context of the global energy transition.

Integrating autoencoder and decision tree models for enhanced energy consumption forecasting in microgrids: A meteorological data-driven approach in Djibouti

At this time, as the world and nations move to reduce the use of fossil fuels, research is oriented toward improving the energy consumption of people and buildings. Recent methods, mainly computing techniques such as deep learning, are proposed in the literature. This paper proposes a model that integrates the autoencoder with the decision tree model using six months of meteorological data, including solar radiation, wind speed, temperature, and other meteorological data. This study aims to advance the energy consumption prediction of loads connected to the microgrid. A case study of a microgrid park of a campus in Djibouti is presented to test the proposed model. Autoencoders were exploited to extract the main features of the meteorological data. Then the decision tree is proposed to predict the energy consumption using the resulting encoded features. The evaluation of the training of the autoencoder model gave a favorable result with a mean squared error of 0.002 and an R2 value of 0.99. Hyperparameter tuning scenarios facilitated the exploration of the decision tree model. Ensemble decision trees performed better than individual trees in this model, achieving a mean absolute error of 1.4 % and an R2 value of 0.997. It showed that hyperparameter tuning improved the results due to the best architecture fit for the decision tree. Moreover, the proposed model was validated by comparing it with the literature on KNN, SVR, and MLP models commonly used in microgrid energy management. The autoencoder decision tree outperformed other compared methods, achieving an explained variance of 0.997 and an MAE of 1.7 % in the standard decision tree regressor scenario. These results demonstrated the capabilities of machine learning and weather data. Combining an autoencoder and the decision tree model will open a new door to energy management improvement.

A novel hybrid model for multi-step-ahead forecasting of wind speed based on univariate data feature enhancement

Reliable multistep ahead wind speed forecasting (MAWSF) is critical for the energy management of wind farms and the long-term maintenance of wind power systems. However, relying on inherent meteorological features such as temperature and atmospheric pressure often fails to meet the deep learning model’s feature requirements for accurate wind speed forecasting (WSF). This paper introduces a hybrid multistep forecasting model that constructs a univariate wind speed feature enhancement framework, combining random forest (RF) and Transformer models for WSF. Initially, the hybrid enhancement framework decomposes the univariate wind speed data and extracts time-series features, effectively mining the latent features information. Subsequently, the RF feature selector filters out significant features contributing to WSF and eliminates redundant features to provide stable features data. Finally, Transformer model is utilized for both short-term and long-term MAWSF. This study conducted MAWSF on data with sampling intervals of 20 min, 30 min and 1 h. The results indicate that, compared to existing state-of-the-art models, the hybrid model in MAWSF tasks reduces the dependency of models on inherent meteorological features, achieving more accurate forecasting and faster computation speeds. Ultimately, the proposed model can provide reliable technical support for energy management and maintenance guidance in real-world wind farms.

Strategic analysis of wind energy potential and optimal turbine selection in Al-Jouf, Saudi Arabia

Wind power is considered one of the most environmentally friendly and rapidly growing form of renewable energy. This study aims at assessment of wind power potential for Al-Jouf region in Saudi Arabia. A long term historical wind speed data of 21 years (2000–2021) was analytically modeled using Weibull distribution function to determine the wind characteristics. The Weibull parameters and average wind speed for monthly and yearly were evaluated using MATLAB program. An online wind power calculator developed by Meteotest was used to evaluate the wind energy by selecting various turbines. Six types of commercial wind turbines of 3 MW capacity were selected for wind power assessment to optimize the turbine selection. Our analysis showed that average wind speed varies between 3.88 m/s and 4.99 m/s and an overall average wind speed is 4.38 m/s. The most frequent wind speed observed was 3.9 m/s with a probability of 20 % approximately. The Weibull distribution parameters, shape parameter “k” values ranged between 1.89 and 2.21 with an average of 1.98 and scale factor “c” values varied between 4.41 and 5.66 m/s with a mean value of 4.86 m/s. Performance evaluations of selected wind turbine models reveal that the Vestas V126 turbine outperforms others at the Al-Jouf site, generating an annual energy yield of 3779400 kWh at a capacity factor of 14.4 %. These results suggest that by constructing a wind farm consisting of 100 V126 turbines may compensate for the energy needs of 46000 individuals. These findings establish Al-Jouf as a viable location for utility-scale wind power projects, offering valuable insights for turbine manufacturers, developers, operators, and policymakers interested in deploying large-capacity turbines in the region. The method used in this study for wind power analysis and turbine selection is simple and helpful to provide an initial assessment about the site suitability and turbine selection without undergoing exhaustive efforts.

Evaluation of the effectiveness of an expressway sand protection system in a gobi region—case study of the Ceke–Ejina expressway, Ejina banner, China

Sand protection systems are widely used to shelter roads from blowing sand. Therefore, it's very important to evaluate their effectiveness. To provide some insights, we used field investigations, measurements of accumulated sand, work logs from sand-removal workers, and wind speed data to analyze a system's performance using China's Ceke–Ejina expressway as a case study. Our results demonstrated that the critical wind speed required to deposit sand on the expressway was 8.6 m s-1 (cumulative frequency 12.1%) before implementing the sand protection system. After implementing the system, the critical wind speed required to deposit sand on the road increased to 14.8 m s-1 (0.3%). However, the critical wind speed decreased to 11.1 m s-1 (3.5%) the next year. Additional work, such as digging ditches, increasing the fence height, and planting shrubs, would help the sand protection system retain its function. Nonetheless, the system continued to function well. The volume of sand removed decreased from ca. 10,000 m3 in 2015 to ca. 100 m3 in 2020. Our results quantify the effectiveness of the sand protection system and reveal how its effectiveness decreases over time. They therefore provide an empirical basis for improving the design and maintenance of sand protection systems.

Fueling Costa Rica’s green hydrogen future: A financial roadmap for global leadership

This study evaluates the financial viability and scalability of green hydrogen production in Costa Rica, focusing on solar and wind energy. The research analyzes seven key provinces using Global Horizontal Irradiance (GHI) and wind speed data to assess energy potential. Monte Carlo simulations were employed to calculate the Net Present Value (NPV), hydrogen production costs, and economic sustainability over multiple project lifetimes. Guanacaste, with solar irradiance of 5.49 kWh/m2/day and wind speeds of 6.59 m/s, emerges as the most favorable region. The analysis reveals an NPV of $1,519.79 USD for onshore wind and $2,320.24 USD for offshore wind over 10 years. These values increase significantly for longer lifetimes, with 25-year NPVs of $3,687.40 USD for onshore wind and $5,890.72 USD for offshore wind, and 50-year NPVs reaching $7,456.21 USD and $11,560.38 USD, respectively. Hydrogen production costs are estimated at $49,696.75 USD from solar and $14,923.19 USD from wind energy. Despite its potential, high costs remain a challenge, requiring policy incentives, international cooperation, and green bonds to drive down costs and scale production. The study offers crucial insights for policymakers, investors, and researchers to support Costa Rica’s leadership in the global green hydrogen economy. Statement of SignificanceThe global shift toward renewable energy is critical for meeting decarbonization goals, and green hydrogen is emerging as a vital solution for sectors that are hard to electrify, such as transportation and heavy industry. However, the high cost of green hydrogen production poses a significant barrier to its widespread adoption. This study tackles the critical research problem of how to make green hydrogen economically viable by evaluating the potential of Costa Rica’s renewable energy resources—specifically solar PV and wind energy—to support large-scale hydrogen production.By integrating Monte Carlo simulations with region-specific financial analysis, this study provides a thorough assessment of hydrogen production costs and infrastructure requirements across Costa Rica’s provinces. The significance of this work lies in its localized approach, offering actionable insights into how a developing country with abundant renewable energy can position itself as a global leader in green hydrogen production. This research provides evidence that while current hydrogen production costs are higher than global targets, Costa Rica’s renewable resources present a strong foundation for future growth with the right policy interventions.The study’s findings have substantial implications for future research, policy, and investment strategies. By highlighting the financial and technological challenges, as well as offering potential solutions through international partnerships and innovative financing models, this research paves the way for Costa Rica and similar countries to contribute meaningfully to the global green hydrogen economy.

Average Wind Speed Prediction in Giresun-Kümbet Plateau Region with Artificial Neural Networks

In order to estimate the electricity generation capacity and schedule the supply for vendor needs, wind speed prediction is crucial for wind power plant frameworks. Prior to the installation of the wind power plants, a reliable wind behaviour model is neccesary. To have such a model, wind data is recorded periodically. In this study, hourly recorded meteorological data of actual pressure, relative humidity, temperature, wind direction and average wind speed for the year 2023 were obtained from the General Directorate of Meteorology for the Kümbet plateau region of Giresun province. The data is used to accurately predict the future wind speed for the region. Matlab Artificial Neural Networks (ANN) is utilized. Actual pressure, relative humidity, temperature and wind direction parameters are defined as input in the prediction process. 85% of the data set is used as training data and remainin 15% data set is used for testing data. An optimization process is applied to determine the number of hidden layers to have the prediction value with the smallest error. Bayesian Regularization training process was performed by seeing that the hidden layer has the lowest error at 90 neurons. Performance evaluations are performed with Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Pearson Correlation Coefficient (R) metrics. The values of the metrics for the test data are 26.7137, 5.1685, 3.5055 and 0.7457 respectively. The results show that, ANN based model is useful for the wind speed prediction over the region.