Significant Renewable Energy Source Research Articles

High-precision prediction of microalgae biofuel production efficiency: employing ELG ensemble method

Microalgae biofuels are considered a significant source of future renewable energy due to their efficient photosynthesis and rapid growth rates. However, practical applications face numerous challenges such as variations in environmental conditions, high cultivation costs, and energy losses during production. In this study, we propose an ensemble model called ELG, integrating Empirical Mode Decomposition (EMD), Long Short-Term Memory (LSTM), and Gradient Boosting Machine (GBM), to enhance prediction accuracy. The model is tested on two primary datasets: the EIA (U.S. Energy Information Administration) dataset and the NREL (National Renewable Energy Laboratory) dataset, both of which provide extensive data on biofuel production and environmental conditions. Experimental results demonstrate the superior performance of the ELG model, achieving an RMSE of 0.089 and MAPE of 2.02% on the EIA dataset, and an RMSE of 0.1 and MAPE of 2.21% on the NREL dataset. These metrics indicate that the ELG model outperforms existing models in predicting the efficiency of microalgae biofuel production. The integration of EMD for preprocessing, LSTM for capturing temporal dependencies, and GBM for optimizing prediction outputs significantly improves the model’s predictive accuracy and robustness. This research, through high-precision prediction of microalgae biofuel production efficiency, optimizes resource allocation and enhances economic feasibility. It advances technological capabilities and scientific understanding in the field of microalgae biofuels and provides a robust framework for other renewable energy applications.

Resiliency-informed optimal scheduling of smart distribution network with urban distributed photovoltaic: A stochastic P-robust optimization

An increased worldwide emphasis on resilience has resulted in a greater concentration on the distribution networks' resiliency. Therefore, the conventional approach to advance scheduling for power distribution networks is no longer sufficient, thereby requiring the creation of resilient scheduling approaches. This paper examines a proactive day-ahead scheduling method for distribution networks that are linked with significant renewable energy sources. The suggested approach employs a probabilistic scenario-generating strategy to quantify the uncertainties related to wind speed, solar irradiation, catastrophe duration, and load fluctuations. This study utilizes a hybrid stochastic p-robust optimization technique to enhance the analysis and provide robust strategies against risks. The present methodology employs a regret-based metric to quantify the adverse impacts of uncertainty inside the proactive scheduling procedure. The suggested methodology is verified using an IEEE 33-bus test system that includes significant distributed photovoltaic sources. The findings indicate a significant decrease of 43% in the highest level of regret, accompanied by a relatively limited rise of 1.22% in the operational expenses of the distribution network. This framework efficiently manages natural occurrences in the day-ahead scheduling process, making it a very promising method for improving the robustness of power distribution networks.

Review on Solar Charged Batteries with Inbuilt Reverse Current Protection

Solar energy is environmentally friendly technology, a great energy supply, and one of the most significant renewable and green energy sources. It plays a substantial role in achieving sustainable development energy solutions. Therefore, the massive amount of solar energy attainable daily makes it an attractive resource for generating electricity. Both technologies, applications of concentrated solar power or solar photovoltaics, are always under continuous development to fulfill our energy needs. Hence, a large installed capacity of solar energy applications worldwide, in the same context, supports the energy sector and meets the employment market to gain sufficient development. This paper highlights solar energy applications and their role in sustainable development and considers renewable energy’s overall employment potential. Thus, it provides insights and analysis on solar energy sustainability, including environmental and economic development. Furthermore, it has identified the contributions of solar energy applications in sustainable development by providing energy needs, creating job opportunities, and enhancing environmental protection. Finally, the perspective of solar energy technology is drawn up in the application of the energy sector and affords a vision of future development in this domain.

Advancing bioethanol production with a new kinetic model based on structure-oriented lumping for cellulose conversion

Bioethanol, as a significant renewable energy source, plays a crucial role in alleviating the energy crisis and reducing greenhouse gas emissions. This research constructed a kinetic model for the hydrolysis of cellulose into bioethanol using the Structure-Oriented Lumping (SOL) method. Three vector units were defined to represent the structure of cellulose and its products, and five reaction rules were used to generate an 18-step reaction network. Reaction rate constants for these reactions were determined for various catalysts using genetic algorithms, and catalyst performance was analyzed based on these rate constants. Results demonstrated a high degree of agreement between experimental data and model predictions over time for the yield of cellulose and its products across four model applications using 16 different catalysts, effectively predicting yield variations with the maximum mean absolute error (MAE) not exceeding 6.3 % and the minimum being as low as 0.8 %. The predictive accuracy of the model provides a robust basis for analyzing catalyst performance through four distinct model applications: 1. In the catalyst hydrolysis of cellulose to ethanol by 5Ru-25 W/HZSM-5, the model identified the hydrogenolysis of EG (k13) as the rate-determining step (RDS). 2. The model explored the impact of Cu-WOx/SiO2 catalysts loaded with various metals (X = None, Pd, Au, Ru), demonstrating how different metal loadings influence the RDSs for the production of EG and ethanol from cellulose. Notably, catalysts loaded with Ru significantly enhanced the RDS (km) for EG, thereby increasing its yield. 3. The model highlighted subtle influences on the RDS for cellulose hydrolysis to EG under catalysts prepared using different methods and carriers. However, fundamentally, these changes did not alter the rate-limiting step (k7), but merely affected the magnitude of the RDS rate. 4. For multiple diols, the model revealed that different catalysts have distinct rate-determining steps, yet the yields of these products consistently correlate with their respective RDS rates. In addition, distinct from traditional thermodynamic analyses that explain catalyst reactivity and efficiency, this study delved into the nuanced association between catalysts and key reaction steps from a kinetic perspective, offering a new dimension of understanding of the catalytic process.

Thermocatalytic Pyrolysis of Waste Areca Nut into Renewable Fuel and Value-Added Chemicals.

Pyrolytic oil is currently in its early stages of production and distribution but has the potential to grow into a significant renewable energy source. It may be processed into a variety of useful substances, including chemicals, and used for heating, transportation, and energy production. The present investigation involves the production and characterization of pyrolytic oil from areca nut husk (ANH), with and without ZSM-5. The pyrolysis experiment was conducted in a semibatch tubular reactor at 600 °C and a heating rate of 80 °C min-1 using ZSM-5 at 20 wt %. The pyrolytic oil was examined via elemental analysis, viscosity, density, moisture content, GC-MS, FTIR, higher heating value (HHV), and ash content. The analysis of kinetics verified that the activation energy rises in proportion to the conversion rate. Additionally, employing ZSM-5 in catalytic pyrolysis at 20 wt % boosted the yield of pyrolytic oil by 11% compared to thermal pyrolysis. Employing ZSM-5 at 20 wt % resulted in a decrease in viscosity, oxygen content, and density by approximately 43.40 cSt, 15.20%, and 168 MJ kg-1, respectively. Moreover, it led to an increase in higher heating value (HHV) and carbon content by 11.71 MJ kg1- and 14.06%, respectively. An FTIR study of pyrolytic oil revealed the occurrence of hydrocarbons, aromatics, phenols, alcohols, and oxygenated chemicals. Moreover, GC-MS analysis indicated a significant increase in hydrocarbons (10.31%) and a decrease in phenols (2.36%), acids (6.38%), and oxygenated compounds with the introduction of the catalyst. Consequently, it can be inferred that utilizing ZSM-5 at 20 wt % during the pyrolysis of ANH aids in enhancing both the yield and characteristics of the resulting pyrolysis oil.

Reliability and Availability of Renewable Energy Sources in Kenya: A Review

The research sought to evaluate the reliability and availability of renewable energy sources in Kenya by examining the available literature regarding the state of energy production and consumption of the renewable energy sources. Several studies have been conducted to establish the capacity of the renewable electrical power sources including hydroelectric power, geothermal sources, wind power, solar energy and biofuels. Regarding production of electricity, the existing capacity is barely able to keep up with demand. The research used systematic review as the methodology of collecting relevant data for analysis. Given that more than 38% (2021) of Kenya’s electricity comes from hydropower, the situation is particularly difficult during the dry months of the year when water levels are low. Capacity gaps are then compensated by expensive thermal generation based on fossil fuels. Geothermal power has the capacity to be the most significant source of renewable energy with a potential of up to 7000MW. The reliability indices of the renewable energy sources, based on System Average Interruption Frequency Index (SAIFI) are at 1.98 (SAIFI) and at 4.37, based on the Customer Average Interruption Duration Index (CAIDI), which are below the international global standards. The research concluded that increased consumption of electricity, availability of alternate energy sources, amount of restoration time and number of planned interruptions have a significance influence on the reliability of a renewable energy source, with restoration time being the most significant. It is therefore recommended that new technologies and innovations that lead to reduced power interruptions and greenhouse gas emission be adopted.

Advances in Thermal Infrared Remote Sensing Technology for Geothermal Resource Detection

This paper discusses thermal infrared (TIR) remote sensing technology applied to the delineation of geothermal resources, a significant renewable energy source. The technical characteristics and current status of TIR remote sensing is discussed and related to the integration of geological structure, geophysical data, and geochemical analyses. Also discussed are surface temperature inversion algorithms used to delineate anomalous ground-surface temperatures. Unlike traditional geophysical and geochemical exploration methods, remote sensing technology exhibits considerable advantages in terms of convenience and coverage extent. The paper addresses the major challenges and issues associated with using TIR remote sensing technology in geothermal prospecting.

Research on the Accounting and Prediction of Carbon Emission from Wave Energy Convertor Based on the Whole Lifecycle

Wave energy, as a significant renewable and clean energy source with vast global reserves, exhibits no greenhouse gas or other pollution during real-sea operational conditions. However, throughout the entire lifecycle, wave energy convertors can produce additional CO2 emissions due to the use of raw materials and emissions during transportation. Based on laboratory test data from a wave energy convertor model, this study ensures consistency between the model and the actual sea-deployed wave energy convertors in terms of performance, materials, and geometric shapes using similarity criteria. Carbon emission factors from China, the European Union, Brazil, and Japan are selected to predict the carbon emissions of wave energy convertors in real-sea conditions. The research indicates: (1) The predicted carbon emission coefficient for unit electricity generation (EFco2) of wave energy is 0.008–0.057 kg CO2/kWh; when the traditional steel production mode is adopted, the EFco2 in this paper is 0.014–0.059 kg CO2/kWh, similar to existing research conclusions for the emission factor of CO2 for wave energy convertor (0.012–0.050 kg CO2/kWh). The predicted data on carbon emissions in the lifecycle of wave energy convertors aligns closely with actual operational data. (2) The main source of carbon emissions in the life cycle of a wave energy converter, excluding the recycling of manufacturing metal materials, is the manufacturing stage, which accounts for 90% of the total carbon emissions. When the recycling of manufacturing metal materials is considered, the carbon emissions in the manufacturing stage are reduced, and the carbon emissions in the transport stage are increased, from about 7% to about 20%. (3) Under the most ideal conditions, the carbon payback period for a wave energy convertor ranges from 0.28 to 2.06 years, and the carbon reduction during the design lifespan (20 years) varies from 238.33 t CO2 (minimum) to 261.80 t CO2 (maximum).

Scheduling of Electric Vehicle’s Power in V2G and G2V Modes Using an Improved Charge–Discharge Opportunity-Based Approach

With rise in the popularity of Electric Vehicles (EVs), challenges related to its charging infrastructure are also soaring. In the upcoming smart grid scenario with significant renewable energy sources and distributed generation, EVs have been identified as a key element for grid stability and its optimal operation, primarily due to energy storage capacity. With a large number of EVs in parking, it has huge potential with optimal scheduling. However, EV owners should have the motivation to participate in Vehicle-to-Grid (V2G). This paper proposes a scheduling mechanism using a novel Forward-Backward energy allocation, based on an improved charge-discharge opportunity approach. The proposed method (PM) addresses the scheduling of power flow in EVs to operate in V2G and Grid-to-Vehicle (G2V) modes considering EV owners’ preferences, optimal cost, and low battery degradation. Implementation of the PM is facile and flexible with the vehicle’s time of arrival and departure. A 40 kWh and 60 kWh battery capacity EVs with multiple real-life scenarios have been considered in this study to demonstrate the effectiveness of the PM. The performance and features of the PM have been compared with existing methods to distinguish its benefits.

Intelligent MPPT control for SEPIC-Luo converter in grid tied photovoltaic system

Grid connected solar photovoltaic (SPV) systems are becoming more and more common due to steadily rising energy demand. The advantages of photovoltaic power generation, such as its eco-friendliness, low maintenance requirements, and lack of noise, are making it as a significant renewable energy source (RES). This framework presents the modeling and control design of PV grid tied system implemented with integrated single ended primary inductance (SEPIC) Luo converter. The main goal of this work includes investigating solar PV system behaviour and creating an effective grid connected solar power. Solar PV module tracks maximum power, with an aid of chaotic cascaded fuzzy a maximum power point tracking (MPPT) has developed. The DC voltage obtained is fed to 1Φ voltage source inverter (VSI) for conversion of AC voltage. In comparison to typical PWM control, the spectrum performance of the examined voltages is improved by adjusting the nominal duty cycle of main switch of SEPIC-Luo converter. So that PV output impedance is equivalent to DC-DC converter's input resistance. Finally, the obtained AC voltage is supplied to 1Φ grid for further applications. With less THD, an efficiency of 96% is achieved when the implementation of the suggested system is carried out using MATLAB/Simulink.

Hydrodynamic characteristics of wing-in-ground effect oscillating hydrofoil on power extraction performance

The energy contained in the tidal motion of the seas and oceans has the potential to be a significant source of renewable energy. The oscillating hydrofoil current-energy turbine has a good performance to extract energy from the coupling of its heaving and pitching motions. In the present study, the wing-in-ground (WIG) effect has been considered to improve the power-extraction performance of the oscillating hydrofoils. The overset grid in the commercial computational fluid dynamic (CFD) software STAR CCM+ is applied to study the flapping hydrofoil with dynamic WIG effect between two hydrofoils. The simulation results show that the WIG effect can greatly improve the power extraction performance of the flapping hydrofoil. The WIG effect is asymmetric over the course of the foil moving toward or leaving from the symmetry plane. The distance of the gap has a major influence on the hydrodynamic performances of the flapping hydrofoil. For a moderate gap, the positive pressure on the lower surface enhances as the hydrofoil departs from the symmetry plane and causes an improvement of lift and moment coefficients. As the gap decreases further, the increasing negative pressure between the leading edge and the symmetry plane plays an essential role improving the power extraction as the hydrofoil approaches the symmetry plane. Compared to the case without the WIG effect, the power-extraction efficiency has an increment of 16.34% in the present study.

Implementing health and safety standards in Offshore Wind Farms

Offshore wind farms represent a significant source of renewable energy, but their operation poses unique health and safety challenges due to the harsh marine environment and remote locations. This review explores the implementation of health and safety standards in offshore wind farms, highlighting key challenges and proposing solutions to mitigate risks. The offshore environment presents numerous hazards to workers, including adverse weather conditions, rough seas, and complex machinery. Ensuring the health and safety of personnel working in such environments requires comprehensive risk assessments, stringent safety protocols, and robust emergency response plans. However, the remote nature of offshore wind farms complicates rescue and evacuation procedures, necessitating specialized training and equipment for personnel. One of the primary challenges in implementing health and safety standards is the dynamic nature of offshore operations, which demand continuous monitoring and adaptation to changing conditions. Furthermore, the integration of multiple stakeholders, including project developers, contractors, and regulatory bodies, requires effective communication and collaboration to ensure compliance with safety regulations. To address these challenges, innovative technologies such as remote monitoring systems and predictive analytics can enhance safety performance by providing real-time data on environmental conditions and equipment status. Additionally, the development of standardized safety protocols and training programs tailored to the offshore wind industry can improve the competence and readiness of personnel in emergency situations. Implementing health and safety standards in offshore wind farms is crucial for safeguarding the well-being of workers and minimizing operational risks. By addressing the unique challenges of the offshore environment and adopting proactive safety measures, the industry can ensure sustainable growth while prioritizing the health and safety of its workforce.

RNN-Based Time Series Analysis for Wind Turbine Energy Forecasting

One significant source of renewable energy is wind power, which has the potential to generate sustainable energy. However, wind turbines have many challenges, such as high initial investment costs, the dynamic nature of wind speed, and the challenge of locating wind-efficient energy regions. Wind power predicting is crucial for effective planning of wind power generation, optimization of power generation, grid integration, and security of supply. Therefore, highly accurate forecasts ensure the efficient and sustainable operation of the wind energy sector and contribute to energy security, economic stability, and environmental sustainability. This study proposes a deep learning (DL) approach based on recurrent neural networks (RNNs) for long-term wind power forecasting utilizing climatic data. The input data that forms the basis of this study is obtained directly from a wind turbine system operating under real-world conditions. The proposed model in this study is based on a multilayer back-propagation neural network (RNN) architecture specifically designed to effectively handle complex data sets and time-dependent series. The architecture of the model is built on an RNN consisting of four separate layers, each with 50 hidden neurons, carefully structured to increase its capacity to capture complex features. To improve the robustness of the model and avoid overlearning, each RNN layer is followed by a dropout (regularizing) layer that randomly deactivates 20% of the neurons to enhance the generalization ability of the network. To finalize the prediction capability of the model, a linear function was chosen in the last layer to directly match the actual values. Evaluating the model performance metrics, the proposed architecture achieved a prediction accuracy of 91% R2 on the test dataset. The findings indicate that proposed method based on multilayer RNN can successfully capture the relationships between the sequential data of the wind turbine.

A Study in Analysing the Major Factors Influencing in Raising Capital for Solar Photovoltaic Projects in Tamilnadu

Globally, environmental impact goals have been introduced, drawing attention to the need to transition to alternative energy sources that are capable of satisfying demand while simultaneously minimizing the amount of damage done to the environment. Solar energy is a significant renewable energy source that also provides an abundant supply of an environmentally friendly energy source. However, it is essential to analyze the factors that have an impact on the economic viability of the projects to provide assistance and inspiration to investors who are interested in installing solar energy systems to produce electricity. As a result, the purpose of this research is to investigate the primary factors that influence the procedure of acquiring financial support for solar photovoltaic projects. A comprehensive investigation will be carried out to achieve this objective, and primary data will be acquired via the use of questionnaires. The finance decisions that an organisation makes have a significant impact on the organization's capacity to continue its operations in the short and long term, including the returns that it provides to its owners and other relevant stakeholders. To maximise the well-being of its owners, often known as shareholders, the fundamental objectives of business organisations are to maximise their revenue and profits, reduce their expenses, and watch out for the interests of other essential stakeholders. The selection of the appropriate capital or finance structure is a significant financial decision that managers and directors who are entrusted with the achievement of an organization's objectives are required to make with great care to ensure that their goals are supported.

Solar Panel Self‐Cleaning Mechanisms and Its Effect on the Economic and Environmental Sustainability

The development of society and the economy depends on the wise use of renewable energy sources and reduced reliance on fossil fuels. In both industrialized and developing nations, solar energy, which is a significant source of renewable energy, is emerging as a dependable and cost‐effective replacement for fossil fuels. In several industries, including residential, commercial, and agricultural, there is an increasing demand for solar photovoltaic (PV) modules. However, dust buildup on solar panels can limit energy transmission and result in power loss in regions with significant amounts of sunlight. The solar cells are shaded by dirt, debris, and soil carried by the wind, which reduces the energy they receive and raises cell temperature, which further reduces solar cell efficiency. The natural accumulation of dust and heat significantly lowers the panel’s ability to produce electricity. This article is intended to develop an automatic self‐cleaning mechanism to solve this problem, which seeks to increase panel efficiency, monitor and control cell temperature, and provide a more affordable solution than the current alternatives. The experimental evaluation of cleaning system performance shows a 14.81% increase in output efficiency, demonstrating its effectiveness in preventing solar degradation. For PV modules, the suggested technique provides an accessible and low‐cost automatic self‐cleaning alternative.

WIND TURBINE FAULT MODELING AND CLASSIFICATION USING CUCKOO-OPTIMIZED MODULAR NEURAL NETWORKS

The wind turbine is rapidly becoming one of the world's most significant renewable energy sources. Wind turbines must be massive to increase amounts of electrical energy. The blades of a wind turbine are commonly made of fiber materials due to their low cost and low weight properties. However, blades are affected by gusts of wind, poor climate factors, uncertain wind loads, lightning storms, and gravity loads, resulting in a surface crack of the blade. As a result, it is important to monitor the state of each wind turbine and its location fault condition. In this research, a cuckoo-optimized modular neural network (COMNN) is proposed for detecting and classifying a crack in the blades of a wind turbine. The method is created using a piezoelectric accelerometer to calculate the blade vibration response when it is energized. Cuckoo optimization is applied to initialize and adjust the weight vector of the Modular Neural Network. The experimental result shows the COMNN accurately detects and classifies faults in an acceptable time. The proposed approaches classify the fault type with an accuracy 98.1 % higher than the existing techniques, such as convolutional neural networks (CNN), recurrent neural networks (RNN), and artificial neural network ANN + support vector machine (SVM) algorithms.

Practical procedures of faults and aging inspection to Photovoltaic system using time domain Reflectometry

Photovoltaic (PV) technology has emerged as a promising and significant renewable energy source in recent decades. However, the assessment of the on-site power generation performance of a PV system is often hindered by inline faults. Additionally, the degradation of the PV module's performance over time indirectly affects the operation of the power conditioner, leading to a decrease in the overall conversion efficiency of the power supply. Therefore, the accurate and efficient inspection of faults and aging status in series-connected PV modules is essential for ensuring reliable operation. This study proposes an improved Time Domain Reflectometry (TDR) technology as a standard inspection method. The TDR technology is initially employed to detect both open and shorted-circuits in series-connected PV panels with the suggested breakpoint simulation. Furthermore, the proposed TDR impedance log method enhanced the evaluation of the PV modules at different aging degrees using various monocrystalline silicon modules for practical verification. The study provides TDR as a comprehensive and effective technique for simultaneously identifying faults and inspecting the aging of PV systems.

Refining the Selection of Historical Period in Analog Ensemble Technique

A precise estimate of solar energy output is essential for its efficient integration into the power grid as solar energy becomes a more significant renewable energy source. Contrarily, the creation of solar energy involves fluctuation and uncertainty. The integration and operation of energy systems are complicated by the uncertainty in solar energy projection. As a post-processing technique to lower systematic and random errors in the operational meteorological forecast model, the analog ensemble algorithm will be introduced in this study. When determining the appropriate historical and predictive data required to use the approach, an optimization is conducted for the historical period in order to further maximize the capabilities of the analog ensemble. To determine statistical consistency and spread skill, the model is evaluated against both the raw forecast model and observations. The outcome lowers the uncertainty in the predicted data by demonstrating that statistical findings improve significantly even with 1-month historical data. Nevertheless, the optimization with a year’s worth of historical data demonstrates a notable decrease in the outcomes, limiting overestimation and lowering uncertainty. Specifically, analog ensemble algorithms calibrate analog forecasts that are equivalent to the latest target forecasts within a set of previous deterministic forecasts. Overall, we conclude that analog ensembles assuming a 1-year historical period offer a comprehensive method to minimizing uncertainty and that they should be carefully assessed given the specific forecasting aims and limits.

Scenarios for transitioning the electricity sector of the Republic of Serbia to sustainable climate neutrality by 2050

By ratifying the Paris Agreement, the Republic of Serbia has committed to reduce greenhouse gas emissions by 13.2% by 2030 compared to the 2010 levels. About 70% of electricity is generated in thermal power plants that use low-quality domestic lignite as fuel. Greenhouse gas emissions from electricity generation amounted to 51.5 Mt CO2eq in 2014. The Republic of Serbia has a significant renewable energy source (RES) potential for electricity generation. This research aims to define sustainable scenarios for the years 2030 and 2050 in the transition process of the electricity generation sector in the Republic of Serbia. These scenarios provide an opportunity to gradually reduce CO2 emissions by 2050, with the goal of zero-emission electricity generation in 2050. The scenarios were created using the hourly electricity balance of the Serbian power system in the EnergyPLAN software, with 2010 as the base year for the calculations. The proposed scenarios promote the sustainable use of RES for electricity generation in the Republic of Serbia. The results show a reduction in CO2 emissions of 35% and 59% in 2030 for the scenarios and 66% and 100% in 2050 compared to the reference year 2010.

Wind Speed Characteristics and Energy Potentials in Lafia, Nasarawa State, Nigeria

Renewable energy, specifically wind power, is vital for reducing fossil fuel dependency, greenhouse gas emissions, and improving energy security. With advancements in technology, wind energy has become cost-effective and competitive. Wind energy research in Lafia is essential to developing solutions in harnessing its potential as a significant renewable energy source for Nigeria, which has abundant but untapped wind energy resources. This study investigates the wind energy potential in Lafia, Nasarawa State, using daily averaged wind speed data from MERRA-2 measured at 2, 10, and 50-metre altitudes. The Weibull distribution function was applied to determine Lafia's wind energy potential, and the outcomes revealed that the highest yield for wind harvest occurred during the peak of the dry season in February, while the lowest yield for wind harvest occurred during the beginning of the dry season in October. Moreover, the monthly mean power densities peaked in February, 2001 at 341.87 W/m2 (class 3: 300 < PD ≤ 399), indicating moderate potential, and in January, 2002 at 454.12 W/m2 (class 4: 400 < PD ≤ 499), indicating good potential. However, the peak monthly mean power densities in December, 2003 and February, 2004 were poor and marginal, respectively, according to the Wind Resource Potential (WRP) classification. Furthermore, at 50 metres altitude, the recorded wind speeds were above the recommended minimum (3.00 – 4.00 m/s) for most wind turbines to start producing electricity. The findings indicate significant wind resource potential at high altitudes (50 metres) in Lafia, which suggests great potential for wind energy generation.