Monthly Wind Research Articles

Potential climate predictability of renewable energy supply and demand for Texas given the ENSO hidden state.

Climate variability influences renewable electricity supply and demand and hence system reliability. Using the hidden states of the sea surface temperature of tropical Pacific Ocean that reflect El Niño-Southern Oscillation (ENSO) dynamics that is objectively identified by a nonhomogeneous hidden Markov model, we provide a first example of the potential predictability of monthly wind and solar energy and heating and cooling energy demand for 1 to 6 months ahead for Texas, United States, a region that has a high penetration of renewable electricity and is susceptible to disruption by climate-driven supply-demand imbalances. We find a statistically significant potential for oversupply or undersupply of energy and anomalous heating/cooling demand depending on the ENSO state and the calendar month. Implications for financial securitization and the potential application of forecasts are discussed.

Modeling spatiotemporal distribution of yellow rust wheat pathogen using machine learning algorithms: Insights from environmental assessment

The yellow rust pathogen (Puccinia striiformis Westend) poses a significant threat to wheat production in the world, necessitating a comprehensive understanding of its spatiotemporal distribution and the influence of climatic factors. In this study, we employed an ensemble of four prominent machine learning algorithms to assess the impact of various environmental and remote sensing variables on the spread of yellow rust at a national scale. Our analysis incorporated 55 climatic parameters, including monthly temperature, precipitation, solar radiation, and wind speed. The results demonstrated that the RF algorithm yielded robust predictions, with a Receiver Operator Characteristic (ROC) of 0.916 and True Skill Statistic (TSS) of 0.748. Furthermore, the study identified key influencing variables for wheat disease modeling, such as annual precipitation, temperature seasonality, and isothermality. Projections based on the model indicate a potential decrease in disease spread by 2050 in specific regions. The findings underscore the efficacy of ensemble modeling in predicting the spatiotemporal distribution of yellow rust on a large scale, offering valuable insights for the development of robust agricultural management strategies in the face of evolving climate conditions.

Assessment of Wind Energy Potential in Kolaka Using Era5-land Reanalysis Data

This research aims to evaluate the potential for wind energy in Kolaka Regency using ERA5-Land reanalysis data from the output of the European Center for Medium-Range Weather Forecasts (ECMWF) model, which has a spatial resolution of 9 km with hourly temporal resolution for the period of 2001 to 2020. This research also compared the quality of ERA5-Land reanalysis wind speed data on data from nearby meteorological stations using statistical assessment methods. The analysis results reveal that the highest average wind speed is discovered at a height of 80 to 150 meters with a range of 4.99 to 5.93 m/s. The spatial distribution map of wind power density indicates that the energy potential at a height of 80 to 150 meters can reach 60 to 170 W/m2. The largest wind energy potential in Kolaka Regency can be generated in the DJF period (December-January-February) with the highest average monthly wind power density in January and the JJA period (June-July-August) with the highest average in July. In general, Kolaka Regency can be classified as an area with first-class wind energy potential, so it tends to be more suitable for the development of small-scale power plants. The suitable locations for developing small-scale wind power plants in Kolaka Regency are the southern Pomalaa coast and the Tanggetada coast because both locations have better wind energy sources than other areas.

АНАЛИЗ КЛИМАТА И УЧЕТ ЕГО ОСОБЕННОСТЕЙ ПРИ ПРОЕКТИРОВАНИИ ЗДАНИЙ В Г.СИМФЕРОПОЛЬ

The purpose of the work is to develop typological recommendations for the designed buildings in Simferopol based on the architectural analysis of natural and climatic conditions. The study requires a detailed study of the temperature regime, temperature and humidity regime, temperature and wind regime, solar radiation and the bioclimatic characteristics of the construction area. Subject of the study: Human heat sensations are influenced by the interaction of factors such as temperature, solar radiation, air humidity and the speed of its movement. Depending on the combination of these factors, a person's heat perception can vary. Materials and methods: To achieve this goal, the following values were analyzed: average monthly outdoor temperature, relative humidity, wind speed and direction, and solar radiation. To assess the effect of climate on the human body, an equivalent-effective temperature and a normal equivalent temperature were calculated. AutoCAD and Microsoft Excel software were used to build the graphs. Results: The conducted research allowed us to determine the boundaries of comfort zones, the influence of the Simferopol climate on the human body. Conclusions: The results of the analysis of natural and climatic factors allow us to determine the typological requirements for the choice of architectural solutions: architectural planning, structural and engineering means of regulating the microclimate in buildings and buildings for Simferopol, taking into account its climate.

The Black Sea Upwelling System: Analysis on the Western Shallow Waters

Upwelling is due to the combined effect of the coastal divergence process and Ekman pumping. The author aims to investigate two new upwelling indices for the Black Sea, derived from climate reanalysis models and the following in situ data: (a) the Coastal Upwelling Transport Index (CUTI) that estimates the rate of vertical volume transport and (b) the Biologically Effective Upwelling Transport Index (BEUTI) that estimates the nitrate flux into the surface mixed layer. Average monthly wind by the European Centre for Medium-Range Weather Forecasts (ECMWF) and Copernicus Marine Services for the Black Sea basin is used to calculate the CUTI and BEUTI Indexes for over 26 years (1993–2019) to analyse the sites along the North-Western Black Sea where changes in divergence phenomena occur. From 2000 to 2018, 31 divergence processes were observed based on daily in situ data from the coastal monitoring stations, with significant predominance in late spring and early summer. Nitrate supply by coastal upwelling has been estimated by combining sea surface temperature and salinity for the in situ data for the North-Western Black Sea shallow waters, and BEUTI indices were determined. Comparing 18 years of data results, the calculated indices and the observed upwelling events showed significant correlations.

About Height of the Surface Air Layer by Sodar Data

Average empirical estimations of the surface air layer height in Moscow have been received by the data of long-term acoustic remote sensing of the atmosphere using MODOS Doppler sodar of METEK (Germany) production. Based on the assumption that the average conditions are close to neutral stratification, this height, as the top of the quasi-linear section of the average long-term wind velocity profile in semi-logarithmic coordinates, is 40–60 m. The wind rotation height, i. e. the height of intersection of day and night wind profiles is on average 95 meters per year. The roughness length in conditions of loose but high urban development in vicinity of Moscow State University in Moscow is 5 m. According to the criterion of the constant wind direction in the surface air layer, its height appears in the average monthly wind direction profiles over the “dead zone” of the sodar (40 m) in approximately one case out of three and usually amounts to 60 m, less often 80 or 100 m. In the rest cases, it is apparently masked by “dead zone”. The average height of the surface layer according to this approach is probably a little less than 50 m, which is close to the estimate obtained from the logarithmic distribution of wind velocity with height in this layer. The daily course of the surface air layer height is noted by the largest values in the afternoon (80–100 m in summer under conditions of prevailing unstable stratification and 60–80 m in winter) and the smallest ones (less than 40 m) in the late evening and at night in summer and from evening to noon in winter.

Impact of Model Resolution and Initial/Boundary Conditions in Forecasting Low-Level Atmospheric Fields over the Incheon International Airport

Abstract This study investigates the impact of initial conditions/boundary conditions (ICs/BCs) and horizontal resolutions on forecast for average weather conditions, focusing on low-level weather variables such as 2-m temperature (T2m), 2-m water vapor mixing ratio (Q2m), and 10-m wind speed (WS10). A Weather Research and Forecasting (WRF) Model is used for regional mesoscale model simulations and large-eddy simulations (LESs). The 6-h-interval forecast fields generated by the Global Forecast System of the National Centers for Environmental Prediction and the Korean Integrated Model of the Korea Meteorological Administration are utilized as ICs/BCs for the regional models. Numerical experiments are performed for 24 h starting at 0000 UTC on each day in April 2021 when the average monthly wind speed was strongest during 10 years (2011–20). A comparison of model simulations with observations obtained around the Yeongjong Island, where Incheon International Airport is situated, shows that the regional models capture the time series of T2m, Q2m, and WS10 more effectively than the global model forecasts. Moreover, the LES experiments with a 100-m horizontal grid spacing simulate higher Q2m and lower WS10 during the daytime compared to the 1-km WRF. This results in a deterioration of their time-series correlation with the observations. Meanwhile, the 100-m LES forecasts time series of T2m over ocean stations and Q2m over land stations, as well as probability density functions of low-level weather variables, more accurately than that of the 1-km WRF. Our study also emphasizes the need for caution when comparing high-resolution model results with observation values at specific stations due to the high spatial variability in low-level meteorological fields.

Unleashing the power of artificial neural networks: accurate estimation of monthly averaged daily wind power at Adama wind farm I, Ethiopia

Wind power plays a vital role in the electricity generation of many countries, including Ethiopia. It serves as a valuable complement to hydropower during the dry season, and its affordability is crucial for the growth of industrial centers. However, accurately estimating wind energy poses significant challenges due to its random nature, severe variability, and dependence on wind speed. Numerous techniques have been employed to tackle this problem, and recent research has shown that Artificial Neural Network (ANN) models excel in prediction accuracy. This study aims to assess the effectiveness of different ANN network types in estimating the monthly average daily wind power at Adama Wind Farm I. The collected data was divided into three sets: training (70%), testing (15%), and validation (15%). Four network types, namely Feedforward Backpropagation (FFBP), Cascade Feedforward Backpropagation (CFBP), Error Backpropagation (EBP), and Levenberg–Marquardt (LR), were utilized with seven input parameters for prediction. The performance of these networks was evaluated using Mean Absolute Percentage Error (MAPE) and R-squared (R2). The EBP network type demonstrated exceptional performance in estimating wind power for all wind turbines in Groups GI, GII, and GIII. Additionally, all proposed network types achieved impressive accuracy levels with MAPE ranging from 0.0119 to 0.0489 and R2 values ranging from 0.982 to 0.9989. These results highlight the high predictive accuracy attained at the study site. Consequently, we can conclude that the ANN model’s network types were highly effective in predicting the monthly averaged daily wind power at Adama Wind Farm I. By leveraging the power of ANN models, this research contributes to improving wind energy estimation, thereby enabling more reliable and efficient utilization of wind resources. The findings of this study have practical implications for the wind energy industry and can guide decision-making processes regarding wind power generation and integration into the energy mix.

Evolution the Relationship Between Physiologically Equivalent Temperature and Some Meteorological Parameters for Basra City, Iraq

Background: Physiologically equivalent temperatures (PET) is highly efficient in evaluating the thermal component of any given microclimate as a single thermal index. The assist stakeholders in understanding and interpreting existing bioclimatic, allowing them to make any required modifications and become more robust to predicted climate change. Objective: This study assesses the relationship between Physiologically equivalent temperatures (PET) and some Meteorological Parameters. Methods: The data for this study includes observed mean monthly air temperature values, relative humidity, and wind speed for the period (2001-2020). The data was acquired from “Iraqi Meteorological Organization and Seismology (IMOS)” stations for Basra city. The radiation and Human Bioclimatic (RayMan) model were used to simulate bioclimatic indices (SET, UTCI, PET, PMV). Results: The findings revealed that the greatest mean values for PMV (4.3), SET (36.3), PET (45.5), and UTCI (42.1) were seen in July and August. The measurements have a nice thermal sensation of PMV (-0.5 - 1.9), SET (19.2- 29), PET (18.1- 31.8), and UTCI (15.9 - 28.7) is most noticeable in the months of (March, April, and October). The results showed the PET values (cool) and (Slightly cool) between (9- 18.3) for January, February, March, and December. The value of UTCI was between (5.9- 8.5) within classes (Slight cold stress), the values of PMV (-2.6- (-2.1)) within classes (strong cold stress) and (Slight cold stress) and the value of SET was (10.2- 12.4) within classes (Slight cold stress). Conclusions: Correlations between PET and air temperature (0.99), indicating good agreement between PET and air temperature, and relative humidity (0.96). PET and wind speed (R2=0.56). The scattering pattern of PET and other thermal indices shows that all indexes have a significant correlation coefficient, between PET and UTCI (R2=0.99), between PET and SET (R2=0.98), and (R2=0.99) between PET and PMV.

Characterization of the offshore wind dynamics for wind energy production in the Gulf of Lion, Western Mediterranean Sea

A ground-based wind lidar system has been deployed on a coastal landmass off Marseille in the Gulf of Lion, western Mediterranean Sea. This location holds significant relevance as it anticipates the imminent deployment of floating wind farms. Over the course of one year, a comprehensive wind dataset was collected by a lidar profiler at altitudes ranging from 60 to 220 m above sea level, aiming to assess the wind resource potential. This study presents an analysis of monthly wind metrics, including key parameters such as wind speed, wind shear exponent, and turbulence intensity. Weibull distributions are provided, along with their scaling and shaping parameters, to elucidate the probability distribution of wind speeds. The focus extends to the diurnal variation of wind metrics and monthly wind speed vertical profiles. Our results indicate that, at an altitude of 140 m above sea level, the one-year average wind speed is 8.3 m/s, with a turbulence intensity (TI) of 13.0 %. The mean wind shear exponent is quantified at 0.077. Notably, the highest wind speeds are consistently observed between 12:00 and 15:00, coinciding with lower values of TI and wind shear exponent, while night-time reveals high levels of both TI and wind shear exponent. Furthermore, an examination of wind direction unveils a bi-modal pattern with a slight variation, specifically wind veer, at each measurement height relative to the 140 m altitude. Additionally, our study explores low-level jets (LLJs), revealing that 80 % of these phenomena occur at altitudes below 140 m, predominantly during the summer period. The mean LLJ core speed, averaged across all measurement heights, is found to be 8.4 m/s.

Estimation of the electricity to be generated at different wind speeds and turbines through fuzzy logic and ANN, A case study of Balıkesir

One of the most significant factors determining the development level of the world’s countries in the economic domain is energy. As technology makes progress, the need of countries for energy continuously increases in parallel with that. Meeting such increasing energy demand with fossil fuels for many years has damaged the living standards of all living beings. Both of these two circumstances have caused an increase in demand for Renewable Energy Resources (RER), with wind power being one of them. In the present study, monthly wind speed, temperature, and pressure measurement data obtained from the Wind Power Plant (WPP) located in the Gonen District of Balikesir Province were averaged out. Using this data and the output data of electricity amounts from different turbine types, an electric power production estimation model was formed through the Artificial Neural Network (ANN) and Fuzzy Logic (FL) methods. It was intended to determine the electric power required to be generated by the model formed through ANN and FL. When the estimations obtained by the ANN and FL were compared, it was observed that the results were correct and coherent.

An economic feasibility assessment of implementing photovoltaic module reflectors under Malaysian meteorological conditions

The use of a reflector can increase the solar radiation on the photovoltaic module (PV) surface, whereby the energy output can be improved. However, the economic feasibility may need to be considered too. This study is conducted, for the first time, due to the lack of studies regarding the economic feasibility assessment of implementing reflectors under the Malaysian meteorological conditions. The outcome will give information about the suitability for implementing a PV reflector in Malaysia through an experimental setup at a sewage treatment site, for two months in 2022. The Malaysian meteorological data, which include daily solar radiation, ambient temperature and wind velocity, were collected to study the output energy, efficiency and the economic perspective of a PV. In February 2022, the PV was operating without a reflector and the averaged values for the monthly solar radiation, ambient temperature and wind velocity were 539.9 MJ/m2, 28.4 °C and 2.2 m/s, respectively, which resulted in an output energy of 106.43 kWh. On the other hand, for April 2022, the PV was operating with a reflector. With the respective averaged input parameters 544.98 MJ/m2, 28.9 °C and 1.51 m/s, the output energy was 121.94 kWh. It is thus shown that the PV with a reflector increases the PV’s output energy by 14.57%. Also, it is shown that the cost-effective factor value is 0.955 which means that the PV reflector is economically feasible to be implemented under the Malaysian meteorological conditions. Hence, extensive research should be conducted to improve the performance of PV reflectors. The findings of this paper maybe useful for researchers and/or manufacturers of PV reflectors.

Relationship Between Winds with Surface Roughness and Carbon Dioxide Concentrations Over Iraq

The methodology used in the study is based on hourly and monthly rates (wind speed and direction, CO2, and surface roughness) obtained from the European Center for Numerical Weather Prediction at 30 sites in Iraq in 2020. The results showed that the maximum wind speed was 4 m/s at 12:00 noon, while the prevailing wind direction for all sites in Iraq was towards the northwest (NW) and the minimum wind speed was 2 m/s at 00:00 AM. By analysing the monthly wind speed and direction for some selected stations, it was found that the highest value of (SW), i.e., 64% was recorded at Rutba station, and the lowest value of (SW) at Basra station was 45%, where the prevailing direction was found to be towards the north-northwest (NNW). The spatial analysis concluded that the wind movement is directed from the north and northwest, explaining that the wind reverses its direction from the mountainous heights to flat lands due to the roughness of the surface in the northern regions above the stations of Iraq. Spearman’s test was carried out between wind speed and surface roughness, and between carbon dioxide and surface roughness. It was found that the correlation strength is weak, and the relationship is inverse between surface roughness and wind speed. This analysis is considered the best way to choose the best wind power plants.

Wind energy potential of weather systems affecting South Africa’s Eastern Cape Province

AbstractAs a percentage of the total global energy supply, wind energy facilities could provide 10% of the total global energy supply by 2050 as reported in IEA World Energy Outlook (2022). Considering this, a just transition to renewable and sustainable energy in South Africa is a genuine possibility if steps are taken immediately to achieve this. The Eastern Cape Province exhibits a strong wind resource which can be exploited towards expediting such a just energy transition. No research and related modelling have, to date, been undertaken in quantifying and relating the detailed P50 energy yield analyses of representative wind energy facilities in temporal and spatial dimensions to the occurrence of specific synoptic types in South Africa. To quantify this energy meteorology climatology for a suitably sized geospatial area in the Eastern Cape Province of South Africa (spatial focus area, latitude −30 to −35, longitude 20 to 30), the approach of using self-organising maps is proposed. These maps are used to identify the most common synoptic circulation types occurring in the Eastern Cape and can subsequently be mapped onto an equivalent time resolution wind energy production timeseries calculated based on probable wind energy facility sites. This paper describes comprehensive methodologies used to model the wind energy facilities, calculate with high confidence the P50 energy production, and then identify the predominant synoptic weather types responsible for the wind energy production in this spatial focus area. After quantifying the energy production, running a self-organising map software generates a purposely selected 35 node map that characterises archetypal synoptic patterns over the 10-year period. The synoptic types can be ranked by the highest energy production. It is shown that in this spatial area, monthly wind energy production is higher during the winter months. When the well-established high-pressure cells move northward, synoptic types associated with higher energy production are frequent and include tropical and temperate disturbances across South Africa, patterns resembling a ridging anticyclone off the west coast of South Africa and low-pressure cells occurring to the north and south. Low energy producing patterns show characteristics of the high-pressure cells moving southwards producing fine weather and mildly disturbed conditions. The purpose of this methodology is that it provides the foundation required to derive long-term frequency changes of these synoptic weather systems using global climate model ensembles and thus changes in wind energy production.

Statistical study of the influence of the data sampling interval on the estimation of wind turbine energy

Main deficiency for wind power is variability. It is really difficult to predetermine the wind’s potential since wind velocity cannot be controlled or predicted with pinpoint accuracy. In this paper, two different methods are applied in order to estimate the monthly wind energy for different sites; those estimations are done using Weibull distribution and calculating energy with direct integration methodology. In additional, the influence of the data sampling interval is studied. Results show that the computational method based on the integration of the power is more accurate. Also, it is shown that the hourly time resolution provides satisfactory accuracy in wind resource estimation, in comparison with one minute’s resolution. Results allow better design of wind or hybrid systems. Moreover, wind speed acquisitions are minimized. A shorter time of treatment and less expensive measurement equipments are required.

Histograms of the Caspian Sea Hydrometeorological Parameters

The article analyzes histograms of the distribution of the main hydrometeorological parameters of the Caspian Sea for 1980–2021 – air temperature, atmospheric pressure, absolute humidity, atmospheric precipitation and wind speed according to the MERRA-2 atmospheric reanalysis. Histograms were constructed for the entire study region of the Caspian Sea (36◦–48◦N, 45.625◦–55◦E), as well as separately for land and sea areas. The extremeness criteria were calculated based on the normal distribution and the real histogram for all 5 main meteorological parameters. A comparison was made of histograms obtained from MERRA-2 data and from weather station at Derbent located on the coast of the Caspian Sea. Distributions of average monthly wind speed, integral water vapor content and water vapor content in clouds over the Caspian Sea were also constructed according to microwave radiometry (SMMR, SSM/I) data for the time interval 1980–2021.

Wind speed prediction using non-gaussian model based on Kumaraswamy distribution

ABSTRACT Wind power is a clean source of energy that not only helps in meeting the growing electricity demand but can also play a profound role in transforming the global energy distribution to mitigate the impact of climate change. Accurate forecasting of wind speed is a crucial factor of power system management. The present endeavor focuses on the development of a non-gaussian framework, Kumaraswamy seasonal autoregressive moving average (KSARMA), to model and forecast the wind speed data of 12 locations across the Indian subcontinent. The monthly wind speed dataset during the period 2003 to 2019 has been utilized to perform the analysis. Diagnostic analysis, Akaike’s information criterion (AIC), and conditional maximum likelihood estimator have been used to select the best-fit model for each location. The correlation coefficient between observed and predicted values for all the locations ranges from 0.77 to 0.93. The root mean squared error (RMSE) was found to lie in 0.04 to 0.16 whereas the mean absolute percentage error (MAPE) was observed in 0.06 to 0.2. A comparison of the proposed KSARMA model with the recently developed beta seasonal autoregressive moving average (βSARMA) and Kumaraswamy autoregressive moving average (KARMA) models has also been facilitated. Error estimates such as root mean squared error (RMSE) and mean absolute percentage error (MAPE) reveal that the KSARMA model outperforms the βSARMA and KARMA models except at the Kanyakumari location.

Enhancing Long-Term Wind Power Forecasting by Using an Intelligent Statistical Treatment for Wind Resource Data

Wind power is an important energy source that can be used to supply clean energy and meet current energy needs. Despite its advantages in terms of zero emissions, its main drawback is its intermittency. Deterministic approaches to forecast wind power generation based on the annual average wind speed are usually used; however, statistical treatments are more appropriate. In this paper, an intelligent statistical methodology to forecast annual wind power is proposed. The seasonality of wind is determined via a clustering analysis of monthly wind speed probabilistic distribution functions (PDFs) throughout n years. Subsequently, a methodology to build the wind resource typical year (WRTY) for the n+1 year is introduced to characterize the resource into the so-called statistical seasons (SSs). Then, the wind energy produced at each SS is calculated using its PDFs. Finally, the forecasted annual energy for the n+1 year is given as the sum of the produced energies in the SSs. A wind farm in Mexico is chosen as a case study. The SSs, WRTY, and seasonal and annual generated energies are estimated and validated. Additionally, the forecasted annual wind energy for the n+1 year is calculated deterministically from the n year. The results are compared with the measured data, and the former are more accurate.

Climatic drivers of litterfall production and its components in two subtropical forests in South China: A 14-year observation

Understanding the climatic triggers of litterfall production is essential for simulating nutrient cycling in forest ecosystems. However, the mechanisms underlying litterfall dynamics in subtropical forests remain unclear because of the complex canopy phenological strategies. Herein, we explored this by investigating a unique litterfall dataset (including components such as leaves, branches, flowers and fruits, and miscellaneous, etc.) spanning a 14-year observation period (2005–2018) in two typical subtropical evergreen forest ecosystems in South China: a mixed conifer-broadleaf forest (MF) and a monsoon evergreen broadleaved forest (MEBF). We found that the annual total litterfall production, often dominated by leaf litterfall, was significantly higher in MF (10.21 ± 2.09 t∙hm−2) than in MEBF (7.74 ± 2.21 t∙hm−2) (P < 0.01). Generally, forest litterfall production occurred mostly during the wet season and was profoundly correlated with the monthly maximum wind speed (rMF = 0.70, rMEBF = 0.71, P < 0.01), it contributed exceeded 80% to the total litterfall and the leaf and branch components. Whereas during the dry season, the forest litterfall process was dramatically weakened, showing a significant correlation with the mean temperature (rMF = 0.48, rMEBF = 0.68, P < 0.01). Further analysis showed that during the dry season, the cumulative contribution rates of temperature factors to the total litterfall and the leaf and branch components exceeded 60%, indicating a leading role of temperature in triggering forest litterfall processes. Notably, when excluding the typhoon disturbances, it is observed that temperature, alongside wind speed, emerges as a significant determinant of forest litterfall production during the wet season. Those two factors collectively account for almost 60% of the total litterfall and the leaf and branch components. Our findings have potential significance in improving our understanding of carbon and nutrient cycling in subtropical forest ecosystems under climate change conditions.

Analysis and Comparison of Wind Potential by Estimating the Weibull Distribution Function: Application to Wind Farm in the Northern of Morocco

To assess wind energy potential in Northern Morocco, a validated approach based on the two-parameter Weibull distribution is employed, utilizing wind direction and speed data. Over a span of two years, from January 2019 to December 2020, measurements taken every 10 min are collected. This study is centered on a comprehensive and statistical analysis of electricity generated from a wind farm situated in the Tetouan region in Morocco. This wind farm boasts a total capacity of 120 MW, comprising 40 wind turbines, each with a 3 MW capacity, strategically positioned along the ridge. Among the available techniques for estimating Weibull distribution parameters, the maximum likelihood method (MLM) is chosen due to its statistical robustness and exceptional precision, especially for large sample sizes. Throughout the two-year period, monthly wind speed measurements fluctuated between 2.1 m/s and 9.1 m/s. To enhance accuracy, monthly and annual theoretical power densities were recalculated using the Weibull parameters and compared with actual measurements. This has enabled the detection of production disparities and the mitigation of forecast errors throughout the entire wind farm. In conclusion, over the two-year production period, turbines WTG 30 and WTG 33 displayed the most significant shortcomings, primarily attributed to orientation issues within the “Yaw system”.