Potential Of Wind Power Generation Research Articles

Towards Sustainable Energy Generation: Analyzing Wind Power Potential, Financial Viability, and Environmental Friendliness at Nok Kundi, Baluchistan

Pakistan is blessed with versatile renewable energy resources, solar and wind energies. Coastal areas of Sindh and Balochistan are the richest sources of wind energy, where adequate wind speeds are available for electricity generation. The Nok Kundi region of Balochistan has outstanding year-round wind speeds and tremendous potential for wind power generation. This study explores wind power potential, financial viability, and mitigation of carbon footprints. In this regard, simulations on the analysis of wind power potential, and financial viability are conducted in the ETAP and HOMMER software respectively. The data regarding the energy demand of Nok Kundi is obtained from Quetta Electric Supply Company (QESCO) Quetta and the data on wind speeds is obtained from the World Weather Online platform. The ETAP software is used to conduct the load flow analysis of a grid-connected system. The full financial analysis considered the state-of-the-art wind power installation site of 40 megawatts capacity to meet Nok Kundi's energy demand. Furthermore, the simulations are conducted in two scenarios i.e. isolated and grid-connected systems. Financial assessments (including the calculation of required capital and operating expenses) are conducted for both isolated and grid-connected systems by using HOMER software. According to the findings of the research, the grid-connected system provided significant benefits in terms of revenues and seamless grid connections. The proposed wind power plant can potentially help reduce the carbon footprints. The present research concluded that the proposed wind power plant can produce 47,581,913 kWh/year and at the same time can mitigate the generation of 47.20 million Kg of carbon/year and an average of 3.93 million Kg/month. This research contributes to providing a detailed feasibility of wind power plants from financial and environmental perspectives. This study addresses Sustainable Development Goal# 13 (SDG 13: Climate Action).

The effect assessment of wind turbine capacity on offshore wind power generation potential in guangdong

Abstract The installed capacity of offshore wind power directly impacts the number of turbines and the total electricity generation. This study uses wind speed and offshore wind power planning data, and Guangdong Province is a case study. Three scenarios are set (7.5 MW, 9.5 MW, and 15 MW) to explore the influence of turbine installed capacity on electricity generation. The results indicate that when the turbine capacity is 9.5 MW, it generates the highest electricity, which accounts for approximately 162718.9 GWh per year.

On Predicting Offshore Hub Height Wind Speed and Wind Power Density in the Northeast US Coast Using High-Resolution WRF Model Configurations during Anticyclones Coinciding with Wind Drought

We investigated the predictive capability of various configurations of the Weather Research and Forecasting (WRF) model version 4.4, to predict hub height offshore wind speed and wind power density in the Northeast US wind farm lease areas. The selected atmospheric conditions were high-pressure systems (anticyclones) coinciding with wind speed below the cut-in wind turbine threshold. There are many factors affecting the potential of offshore wind power generation, one of them being low winds, namely wind droughts, that have been present in future climate change scenarios. The efficiency of high-resolution hub height wind prediction for such events has not been extensively investigated, even though the anticipation of such events will be important in our increased reliance on wind and solar power resources in the near future. We used offshore wind observations from the Woods Hole Oceanographic Institution’s (WHOI) Air–Sea Interaction Tower (ASIT) located south of Martha’s Vineyard to assess the impact of the initial and boundary conditions, number of model vertical levels, and inclusion of high-resolution sea surface temperature (SST) fields. Our focus has been on the influence of the initial and boundary conditions (ICBCs), SST, and model vertical layers. Our findings showed that the ICBCs exhibited the strongest influence on hub height wind predictions above all other factors. The NAM/WRF and HRRR/WRF were able to capture the decreased wind speed, and there was no single configuration that systematically produced better results. However, when using the predicted wind speed to estimate the wind power density, the HRRR/WRF had statistically improved results, with lower errors than the NAM/WRF. Our work underscored that for predicting offshore wind resources, it is important to evaluate not only the WRF predictive wind speed, but also the connection of wind speed to wind power.

Suitability analysis for implementing wind and solar farms based AHP method: Case study in Inner Mongolia, China

Abstract. As important green energy, wind power and photovoltaic power have great development prospects. The suitability evaluation of wind and solar power plants is a popular research field, which is related to the sustainable and healthy development of wind and solar power generation. In this paper, based on multiple dimensions such as land types, climatic conditions, topographic features and policy environment, we selected 10 indicators and combined (analytical hierarchy process) AHP method to build a suitability assessment model for evaluating the suitability of solar and wind power in Inner Mongolia, China. The findings revealed that, Inner Mongolia has a great potential to generate wind and solar electricity, for wind power, the category of ‘excellent’ regions covers 83855 km2 and represents 7.10% of the total surface area; for solar power, 7.66% (nearly 90420 km2) are classified as ‘excellent’. The suitability of both solar and wind energy in the western region is considered to have the most suitable development region, parts of Alxa League and Bayannur City have great potential for combined wind and solar power generation. The research results could provide important technical and data support for capacity evaluation and power station location decision.

Probabilistic assessment of wind power plant energy potential through a copula-deep learning approach in decision trees

In the face of environmental degradation and diminished energy resources, there is an urgent need for clean, affordable, and sustainable energy solutions, which highlights the importance of wind energy. In the global transition to renewable energy sources, wind power has emerged as a key player that is in line with the Paris Agreement, the Net Zero Target by 2050, and the UN 2030 Goals, especially SDG-7. It is critical to consider the variable and intermittent nature of wind to efficiently harness wind energy and evaluate its potential. Nonetheless, since wind energy is inherently variable and intermittent, a comprehensive assessment of a prospective site's wind power generation potential is required. This analysis is crucial for stakeholders and policymakers to make well-informed decisions because it helps them assess financial risks and choose the best locations for wind power plant installations. In this study, we introduce a framework based on Copula-Deep Learning within the context of decision trees. The main objective is to enhance the assessment of the wind power potential of a site by exploiting the intricate and non-linear dependencies among meteorological variables through the fusion of copulas and deep learning techniques. An empirical study was carried out using wind power plant data from Turkey. This dataset includes hourly power output measurements as well as comprehensive meteorological data for 2021. The results show that acknowledging and addressing the non-independence of variables through innovative frameworks like the Copula-LSTM based decision tree approach can significantly improve the accuracy and reliability of wind power plant potential assessment and analysis in other real-world data scenarios. The implications of this research extend beyond wind energy to inform decision-making processes critical for a sustainable energy future.

Assessing the Wind Power Potential in Naama, Algeria to Complement Solar Energy through Integrated Modeling of the Wind Resource and Turbine Wind Performance

In the context of the escalating global climate crisis and the urgent need for sustainable energy solutions, this study explores the integration of wind energy as a supplementary source to solar photovoltaic energy in Naama, Algeria. The research utilizes a decade-long anemometric dataset, along with concurrent solar radiation data, to investigate the potential of harnessing wind energy, particularly during periods of low solar irradiance. Employing advanced statistical methods, including the Weibull distribution, the study assesses the wind power generation potential of a 2 kW/day turbine. The research highlights an average evening increase in wind speeds, which inversely correlates with the diminished solar energy production after sunset. This seasonal pattern is further substantiated by a significant negative correlation between wind speed and solar radiation for most of the year (January to May and September to December), with Pearson coefficients ranging from −0.713 to −0.524 (p < 0.05). However, the study also notes an absence of a notable correlation during the summer months (June to August) attributed to seasonal wind variations and the peak of solar irradiance. These findings confirm Naama as an ideal location for integrated renewable energy systems, thereby demonstrating the natural synergy between solar and wind energy. This synergy is particularly effective in mitigating the intermittency of solar power, thus highlighting the potential of wind energy during periods of low solar activity.

Research on Wind Turbine Location and Wind Energy Resource Evaluation Methodology in Port Scenarios

Wind energy is widely distributed in China as a renewable energy source. Aiming to alleviate the issues resulting from fossil fuel consumption faced by developing and developed countries (e.g., climate change) and to meet development needs, this study innovatively proposed methods for the location selection of wind farms and wind turbines in port areas based on the fuzzy comprehensive evaluation method. Considering that the wind turbine location is crucial to wind power generation, this paper focuses on locating wind turbines within a specific set of sea ports. The primary objectives of this paper are to evaluate the potential of wind power generation under different port scenarios and develop a method for assessing the potential of wind energy resources in wind farm areas. Firstly, a method is proposed for identifying the boundaries of wind farms in the port areas and locating wind turbines at sea ports. Furthermore, this study used the National Aeronautics and Space Administration (NASA) wind speed database to test the proposed method with the real-world wind power projects of the Ports of Tianjin, Shanghai, Xiamen, Shenzhen, and Hainan, which are top ports within five major coastal port clusters in China. It is found that the potential power generation capacity of the wind power farms at the above ports is 30.71 GWh, 19.82 GWh, 16.72 GWh, 29.45 GWh, and 24.42 GWh, respectively. Additionally, sensitive results for different types of wind turbines are conducted in the following experiment. The results of this study are fundamental for enriching the research of evaluating wind energy resources of sea ports and promoting the development and use of clean energy in practical environments. Further, the method proposed in this study is essential for optimizing the location and construction of wind turbines, which may help ports in adopting a low-carbon and green development path, thereby mitigating air pollution, and promoting sustainable development.

Techno-economic and environmental analysis of an off-grid hybrid system using solar panels, wind turbine, diesel generator, and batteries for a rural health clinic considering

Abstract Middle East has significant potential for independent solar and wind power generation due to its vast land area and dispersed settlements. Enhancing the standard of living in remote areas and meeting the increasing demand for healthcare services worldwide are crucial objectives. Finding the most reliable and affordable method of supplying energy and clean water to rural healthcare institutions is the main goal of the research. The aim of this research is to evaluate the financial and environmental impacts of employing a hybrid energy system to supply power to a clinic in Rijal Almaa, Saudi Arabia. Utilizing the HOMER software, the investigation determined that the most efficient hybrid configuration includes 360 batteries, a 25 kW DG, a 2 kW wind turbine, 33.3 kW of solar panels, and an 18.4 kW converter. The NPC (Net Present Cost) associated with this optimized system amounts to $109 307, while its COE is 0.103 $/kWh. It was found that this efficient system necessitates an initial capital outlay of $72 281, coupled with an annual operational expense of $2361. The renewable fraction (RF) of 84.7%, excess electricity generation of 8.81%, and fuel consumption of 4135 L/yr are notable features of the system. The system also exhibits the lowest annual CO2 emissions at 10825 kg/yr, indicating a positive environmental impact. The findings can be applied globally, particularly in hot, arid regions. The analysis suggests that reducing the costs of hybrid solar panels, DG, wind turbine, and battery systems could significantly reduce overall costs, making them a feasible solution for developing nations.

Wind energy potential and its current status in India

Currently, India’s population is growing at a rate comparable to other countries, which means we need to provide more energy. Most of the world’s energy is generated by coal-fired thermal power plants, despite the fact that this method results in a significant increase in the number of pollutants released into the atmosphere. Wind power is a kind of environment-friendly, pollution-free green energy that comes from renewable sources. India is one of the top five producers of wind power in the world. This article throws light upon the current status of wind energy in India, as well as its potential and regulations governing wind energy. India has a significant untapped potential for wind power generation, and this article details that potential as well as wind power generation in various states of India. When it comes to the production of wind power in India, Tamil Nadu is the most productive states.

Evaluating offshore wind power potential in the context of climate change and technological advancement: Insights from Republic of Korea

Offshore wind power is constantly being invested and expanded around the world. National plans for wind power generation must be evaluated in light of climate change and technological advancement since wind speed changes at sea level due to climate change along with continuous technological development significantly affect the wind power generation potential. Republic of Korea has set ambitious plans to develop and expand large-scale offshore wind power generation and with the aim of accurately predicting domestic wind power generation potential, the current study analyzed the patterns and consequences of changes in wind speed due to climate change. Furthermore, the contribution of developing a 15 MW large-scale wind power generator to the national target for offshore wind power generation was evaluated. Changes in wind speed under RCP 4.5 and RCP 8.5 scenarios were examined for 10-year interval changes and for seasonal variabilities from 2010 to 2100. Results indicate that the wind speed would continuously decrease in the RCP 8.5 scenario, suggesting that failing to reduce greenhouse gas emission would eventually lead to loss of wind resources and resultant decline in wind power generation. Analyses also revealed that installing 3 MW wind turbines (utilization rate of 22%) or 15 MW wind turbines (utilization rate of 29%) can respectively achieve 40% or 55% of the national electricity production target for 2030. Assuming an aggressive deployment in high wind speed areas, it was found feasible to achieve up to 64% of the 2030 target, and even 100% but with a high utilization rate of 42%.

Development of a Background-Oriented Schlieren (BOS) System for Thermal Characterization of Flow Induced by Plasma Actuators

Cold climate regions have great potential for wind power generation. The available wind energy in these regions is about 10% higher than in other regions due to higher wind speeds and increased air density. However, these regions usually have favorable icing conditions that lead to ice accumulation on the wind turbine blades, which in turn increases the weight of the blades and disrupts local airflow, resulting in a reduction in wind turbine performance. Considering this problem, plasma actuators have been proposed as devices for simultaneous flow control and deicing. These devices transfer momentum to the local airflow, improving the aerodynamic performances of the turbine blades while producing significant thermal effects that can be used to prevent ice formation. Considering the potential application of plasma actuators for simultaneous flow control and deicing, it is very important to investigate the thermal effects induced by these devices. However, due to the significant electromagnetic interference generated by the operation of these devices, there is a lack of experimental techniques that can be used to analyze them. In the current work, a background-oriented Schlieren system was developed and is presented as a new experimental technique for the thermal characterization of the plasma-induced flow. For the first time, the induced flow temperatures are characterized for plasma actuators with different dielectric materials and different dielectric thicknesses. The results demonstrate that, due to the plasma discharge, the temperature of the plasma-induced flow increases with the increase of the applied voltage and may achieve temperatures five times higher than the room temperature, which proves the potential of plasma actuators for deicing applications. The results are presented and discussed with respect to the potential application of plasma actuators for simultaneous flow control and deicing of wind turbine blades.

Bluff body vortex-induced vibration control of floating wind turbines based on a novel intelligent robust control algorithm

Given the great potential of the offshore wind power generation in renewable energy sources, it will bring unprecedented significant development opportunities. Meanwhile, the installed capacity of floating wind turbines (FWTs) is huge. However, as one of the important parts of that, FWTs are always subjected to complex environmental loads during operation, which will critically affect the stability of wind power generation. Hence, it is urgent to analyze and control its stability for the safe operation of wind turbines. It is accepted that vortex-induced vibration (VIV) of a bluff body structure is the leading cause of structural damage to FWTs. For this reason, a radial basis function neural network sliding mode control (RBFNNSMC) is proposed to improve the modeling accuracy of bluff body VIV control. Then, the joint numerical analysis system was designed to achieve the completely coupled fluid structure vibration control of bluff body. The numerical results indicate that RBFNNSMC can better control the forward/cross-flow vibration of bluff body. In addition, the controller is not responsive to changes in system parameters and has strong robustness.

Integrating stability and complementarity to assess the accommodable generation potential of multiscale solar and wind resources: A case study in a resource-based area in China

Renewable energy generation is a crucial route to achieve carbon neutrality. In this paper, a methodology integrating richness, stability, and complementarity to assess accommodable potential of solar and wind power generation was proposed and implemented in Ordos, a resource-based city in northwestern China. Results showed wind and especially solar resources were abundant, with maximum of 6.74 kWh/m2/day in summer and 6.9 m/s in spring based on hourly data from 1981 to 2020. Availability of solar (>60%) and wind resources (>80%) was best in the abovementioned seasons. A comprehensive evaluation of stability showed these resources were both stable in spring. Continuity of wind resources was stronger than that of solar (94.5% > 91.3%), and it's variability was also higher (1.9 > 1.4). Then, using correlation coefficients, complementarity degree of these resources was qualitatively identified as moderate. Aiming for stability, the optimal complementary ratio of solar and wind power generation was quantified at 1:0.27, with instability reduced by 10.4%–44.4%, and their power generation could be maximally accommodated by the grid. Scenario analysis predicted a maximum of 2.46% of Ordos land could meet the demand for renewable energy development in 2025. This study provides methodology and model research on orderly replacement of fossils by renewable energy in northwestern China as well as in similar regions globally.

Assessment of wind and photovoltaic power potential in China

Decarbonization of the energy system is the key to China’s goal of achieving carbon neutrality by 2060. However, the potential of wind and photovoltaic (PV) to power China remains unclear, hindering the holistic layout of the renewable energy development plan. Here, we used the wind and PV power generation potential assessment system based on the Geographic Information Systems (GIS) method to investigate the wind and PV power generation potential in China. Firstly, the high spatial-temporal resolution climate data and the mainstream wind turbines and PV modules, were used to assess the theoretical wind and PV power generation. Then, the technical, policy and economic (i.e., theoretical power generation) constraints for wind and PV energy development were comprehensively considered to evaluate the wind and solar PV power generation potential of China in 2020. The results showed that, under the current technological level, the wind and PV installed capacity potential of China is about 56.55 billion kW, which is approximately 9 times of those required under the carbon neutral scenario. The wind and PV power generation potential of China is about 95.84 PWh, which is approximately 13 times the electricity demand of China in 2020. The rich areas of wind power generation are mainly distributed in the western, northern, and coastal provinces of China. While the rich areas of PV power generation are mainly distributed in western and northern China. Besides, the degree of tapping wind and PV potential in China is not high, and the installed capacity of most provinces in China accounted for no more than 1% of the capacity potential, especially in the wind and PV potential-rich areas.

Efficient Physical and Multidisciplinary Approaches in Combinations of Nanoparticles towards Power Generations, Energies Production Methods and Photocatalysis Mechanism

To progress toward sustainable growth, future hydro-power development must pay a bit more attention towards how changing climate may impact hydropower output, as well as make massive action to limit the environmental costs borne by people who live near dams. Long term projections help in providing data for power network risk analysis as well as identifying wind power generation potential in particular places, delivering important data for electricity planners. Hydro-electric power system is an enhanced energy process because it effectively converts mechanical power into grid power, but both are high-energy methods. The utilization of wind turbines for renewable power source has turned out to be one of the greatest viable alternate wells-prings of electricity age because of the little recompense, for instance, being profitable and efficient and environment friendly. Many foundations, organizations, associations, and experts have demonstrated that wind turbines are presumed to comprehend the imperativeness order. In compared to conventional power plants, although wind-power plants have little impact on the environment, concern exists over the noise produced by the turbine blades and visual impacts to the landscape and turbine blade. While wind turbines are connected to the small scale or isolated network, the power product from another source should be varied in reply to these variant and change in sequence to retain system density and voltage with in pre-set limit.

A k-nearest neighbor space-time simulator with applications to large-scale wind and solar power modeling

SummaryWe develop and present a k-nearest neighbor space-time simulator that accounts for the spatiotemporal dependence in high-dimensional hydroclimatic fields (e.g., wind and solar) and can simulate synthetic realizations of arbitrary length. We illustrate how this statistical simulation tool can be used in the context of regional power system planning under a scenario of high reliance on wind and solar generation and when long historical records of wind and solar power generation potential are not available. We show how our simulation model can be used to assess the probability distribution of the severity and duration of energy “droughts” at the network scale that need to be managed by long-duration storage or alternate energy sources. We present this estimation of supply-side shortages for the Texas Interconnection.

Wind power resource assessment and wind-hydrogen generation potential: a case study

ABSTRACT A case study of assessing wind power generation potentials for seven locations in a district of India is presented in the paper with special focus on small wind turbines with low cut in speeds operating in hybrid complementary mode with a hydrogen generation system. The major motivation for the study is to accelerate the efforts for the region toward carbon neutral status. The wind speeds are obtained for one hour time intervals for all days for 20 years during the time period 2001–2020 for the seven locations from European Center for Medium-Range Weather Forecasts Reanalysis v5 (ERA-5) data. Mean wind speeds for the period are analyzed and wind rose diagrams are generated using the WindroseAxes library in Python environment. Wind power potential assessment has been carried out using the data. From the analysis, it has been noted that the coastal and southern regions of the district get higher wind speeds and have higher power generation potential. Analysis has shown that based on the Beaufort scale, usually the available wind speed is in the calm to moderate breeze types (<0.3–8 m/s). Study has shown that deployment of small wind turbines may be beneficial, to potentially facilitate decentralized power generation. A small wind turbine with cut-in speed 2.5 m/s and cutoff speed 15.5 m/s is selected for the study. The region has a generic upper speed availability of 8 m/s. The corresponding wind power potential values are 0.84 and 27.58 kW. Hydrogen, considered as an energy carrier, and a clean fuel for the future, is proposed as an option for complementary energy storage. Hydrogen, generated from wind energy through electrolysis, may be stored and used to meet the peak power demands. A preliminary analysis of the hydrogen generation potential of the region via the power-to-gas route is also presented. The analysis also points to the necessity for developing small wind turbines, which can generate power at lower cut-in speeds, for maximum utilization of wind power generation potential. The methodology can be adapted to other regions.

Comparative Study of Three Methods for Determining Weibull Parameters in Pauh Putra, Perlis

This paper studied about analysis characteristics of wind speed at Pauh Putra, Perlis, where nearest to Chuping station, Perlis, Malaysia. The wind speed characteristics consist of monthly and annual wind speed in Perlis, Malaysia. By using Weibull distribution, three different methods to calculate the potential of wind power generation and analysis the characteristics of wind speed at Pauh Putra, Perlis. The results present the means wind speed is 1.0790 m/s and 1.1321 m/s for 2018 and 2019, respectively. The highest monthly mean wind speed occurred in February for both years, 2018 and 2019. Besides, the lowest monthly wind speed for 2018 in May and for 2019 in October. The Weibull distribution summarized the highest probability density is 120% in the wind speed, 1.1 m/s using the Maximum Likelihood Method (MLM) method for these two years. Furthermore, this research found that the Energy Pattern Factor (EPF) Method is stretched to the right, and its height decreased from other methods for both years based on the graph of the wind speed of probability density function. The Maximum Likelihood Method (MLM) for these two years is higher because its shape parameters are relatively higher based on the graph of the wind speed of probability density function.

A thorough investigation for development of hydrogen projects from wind energy: A case study

Increasing energy demand has led to a substantial growth in the use of wind energy across the world, which can be attributed to the low initial and running costs and rapid and easy deployment of this technology. The development of hydrogen from wind energy is an excellent way to store the excess wind power produced, as the produced hydrogen can be used not only as clean fuel but also as input for various industries. Considering the good wind potentials of Yazd province, the variety of industries that are active in this area, and the central location of this province in Iran, which gives it ample access to major transport routes and other industrial hubs, hydrogen production from wind power in this province could benefit not only this region but the entire country. Given these considerations, we conducted a technical, economic, and environmental assessment of the potential for wind power generation and hydrogen production in Yazd province. Overall, the assessments showed that the best locations for harvesting wind energy in this province are Bahabad and Halvan stations. For these two stations, it is recommended to use EWT DW 52-900 turbine to take advantage of its higher nominal capacity to achieve higher electricity and hydrogen output and emission reduction. For Abarkoh and Kerit stations, which have a low wind energy potential, it is recommended to use small turbines such as Eovent EVA120 H-Darrieus. Also, economic and technical assessments showed that it is not economically justified to harvest wind energy in Ardakan station. The results of ranking the stations with the Step-wise Weight Assessment Ratio Analysis (SWARA) and Evaluation based on Distance from Average Solution (EDAS) techniques showed that Bahabad station was introduced as the best place to produce hydrogen from wind energy.

Forecasting Wind Power Generation Using Artificial Neural Network: “Pawan Danawi”—A Case Study from Sri Lanka

Wind power, as a renewable energy resource, has taken much attention of the energy authorities in many countries, as it is used as one of the major energy sources to satisfy the ever-increasing energy demand. However, careful attention is needed in identifying the wind power potential in a particular area due to climate changes. In this sense, forecasting both wind power generation and wind power potential is essential. This paper develops artificial neural network (ANN) models to forecast wind power generation in “Pawan Danawi”, a functioning wind farm in Sri Lanka. Wind speed, wind direction, and ambient temperature of the area were used as the independent variable matrices of the developed ANN models, while the generated wind power was used as the dependent variable. The models were tested with three training algorithms, namely, Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), and Bayesian Regularization (BR) training algorithms. In addition, the model was calibrated for five validation percentages (5% to 25% in 5% intervals) under each algorithm to identify the best training algorithm with the most suitable training and validation percentages. Mean squared error (MSE), coefficient of correlation (R), root mean squared error ratio (RSR), Nash number, and BIAS were used to evaluate the performance of the developed ANN models. Results revealed that all three training algorithms produce acceptable predictions for the power generation in the Pawan Danawi wind farm with R &gt; 0.91, MSE &lt; 0.22, and BIAS &lt; 1. Among them, the LM training algorithm at 70% of training and 5% of validation percentages produces the best forecasting results. The developed models can be effectively used in the prediction of wind power at the Pawan Danawi wind farm. In addition, the models can be used with the projected climatic scenarios in predicting the future wind power harvest. Furthermore, the models can acceptably be used in similar environmental and climatic conditions to identify the wind power potential of the area.