scholarly journals Bayesian method for estimating Weibull parameters for wind resource assessment in the Equatorial region: a comparison between two-parameter and three-parameter Weibull distributions

2022 ◽  
Author(s):  
M. G. M. Khan ◽  
M. Rafiuddin Ahmed
Keyword(s):  
Weibull Distribution ◽  
Bayesian Approach ◽  
Resource Assessment ◽  
Equatorial Region ◽  
Weibull Parameters ◽  
Wind Speeds ◽  
Wind Resource Assessment ◽  
Low Wind Speeds ◽  
Wind Resource ◽  
Two Parameter

Abstract The two-parameter Weibull distribution has garnered much attention in the assessment of windenergy potential. The estimation of the shape and scale parameters of the distribution has broughtforth a successful tool for the wind energy industry. However, it may be inappropriate to use thetwo-parameter Weibull distribution to assess energy at every location, especially at sites wherelow wind speeds are frequent, such as the Equatorial region. In this work, a robust technique inwind resource assessment using a Bayesian approach for estimating Weibull parameters is firstproposed. Secondly, the wind resource assessment techniques using a two-parameter Weibulldistribution and a three-parameter Weibull distribution which is a generalized form of twoparameterWeibull distribution are compared. Simulation studies confirm that the Bayesianapproach seems a more robust technique for accurate estimation of Weibull parameters. Theresearch is conducted using data from seven sites in Equatorial region from 1o N of Equator to 19oSouth of Equator. Results reveal that a three-parameter Weibull distribution with non-zero shiftparameter is a better fit for wind data having a higher percentage of low wind speeds (0-1 m/s) andlow skewness. However, wind data with a smaller percentage of low wind speeds and highskewness showed better results with a two-parameter distribution that is a special case of threeparameterWeibull distribution with zero shift parameter. The results also demonstrate that theproposed Bayesian approach and application of a three-parameter Weibull distribution areextremely useful in accurate estimate of wind power and annual energy production.

scholarly journals Bayesian Method for Estimating Weibull Parameters for Wind Resource Assessment in the Equatorial Region: a Comparison Between Two-parameter and Three-parameter Weibull Distributions

2021 ◽  
Author(s):  
M. G. M. Khan ◽  
M. Rafiuddin Ahmed
Keyword(s):  
Weibull Distribution ◽  
Bayesian Approach ◽  
Resource Assessment ◽  
Equatorial Region ◽  
Weibull Parameters ◽  
Wind Speeds ◽  
Wind Resource Assessment ◽  
Low Wind Speeds ◽  
Wind Resource ◽  
Two Parameter

Abstract The two-parameter Weibull distribution has garnered much attention in the assessment of wind energy potential. The estimation of the shape and scale parameters of the distribution has brought forth a successful tool for the wind energy industry. However, it may be inappropriate to use the two-parameter Weibull distribution to accurately characterize wind speed at every location, especially at sites where the frequency of low speed is high, such as the Equatorial region. In this work, for the robustness in wind resource assessment, we first propose a Bayesian approach in estimating Weibull parameters. Secondly, we compare the techniques of wind resource assessment using both two and three-parameter Weibull distributions for different sites in the Equatorial region. The Bayesian inference approach is adopted using Markov Chain Monte Carlo (MCMC) algorithms. Simulation studies conducted in this research confirms that the Bayesian approach seems to be a new robust alternative technique for accurate estimation of Weibull parameters. An appropriate Weibull distribution and the application of the Bayesian approach in estimating distribution parameters were determined using data from six sites in the Equatorial region from 1° N of Equator to 19° South of Equator. Results revealed that a three-parameter Weibull distribution is a better fit for wind data having a greater percentage of low wind speeds (0-1 m/s) and low skewness. However, wind data with a smaller percentage of low wind speeds and high skewness showed better results using a two-parameter Weibull distribution. The results also demonstrate that the proposed Bayesian approach to estimate Weibull parameters is extremely useful in the analysis of wind power potential, as it provides more accurate results while characterizing lower wind speeds.

On the wind resource in Algeria: Probability distributions evaluation

2020 ◽  
pp. 095765092097588
Author(s):  
Houdayfa Ounis ◽  
Nawel Aries
Keyword(s):  
Wind Speed ◽  
Probability Distribution ◽  
Probability Distributions ◽  
Resource Assessment ◽  
Descriptive Statistics ◽  
The Other ◽  
Wind Speeds ◽  
Wind Resource Assessment ◽  
The Mean ◽  
Wind Resource

The present study aims to present a contribution to the wind resource assessment in Algeria using ERA-Interim reanalysis. Firstly, the ERA-Interim reanalysis 10 m wind speed data are considered for the elaboration of the mean annual 10 m wind speed map for a period starting from 01-01-2000 to 31-12-2017. Moreover, the present study intends to highlight the importance of the descriptive statistics other than the mean in wind resource assessment. On the other hand, this study aims also to select the proper probability distribution for the wind resource assessment in Algeria. Therefore, nine probability distributions were considered, namely: Weibull, Gamma, Inverse Gaussian, Log Normal, Gumbel, Generalized Extreme Value (GEV), Nakagami, Generalized Logistic and Pearson III. Furthermore, in combination with the distribution, three parameter estimation methods were considered, namely, Method of Moment, Maximum Likelihood Method and L-Moment Method. The study showed that Algeria has several wind behaviours due to the diversified topographic, geographic and climatic properties. Moreover, the annual mean 10 m wind speed map showed that the wind speed varies from 2.3 to 5.3 m/s, where 73% of the wind speeds are above 3 m/s. The map also showed that the Algerian Sahara is windiest region, while, the northern fringe envelopes the lowest wind speeds. In addition, it has been shown that the study of the mean wind speeds for the evaluation of the wind potential alone is not enough, and other descriptive statistics must be considered. On the other hand, among the nine considered distribution, it appears that the GEV is the most appropriate probability distribution. Whereas, the Weibull distribution showed its performance only in regions with high wind speeds, which, implies that this probability distribution should not be generalized in the study of the wind speed in Algeria.

scholarly journals Wind Speed using Improved Mixture Weibull Distribution

2019 ◽  
Vol 8 (6) ◽  
pp. 1871-1874
Keyword(s):  
Wind Speed ◽  
Probability Distribution ◽  
Weibull Distribution ◽  
Wind Farms ◽  
Resource Assessment ◽  
Estimation Of Parameters ◽  
Basic Properties ◽  
Wind Resource Assessment ◽  
Proposed Model ◽  
Wind Resource

Wind is random in nature both in space and in time. Several technologies are used in wind resource assessment (WRA).The appropriate probability distribution used to calculate the available wind speed at that particular location and the estimation of parameters is the essential part in installing wind farms. The improved mixture Weibull distribution is proposed model which is the mixture of two and three parameter Weibull distribution with parameters including scale, shape, location and weight component. The basic properties of the proposed model and estimation of parameters using various methods are discussed.

scholarly journals Year-to-year correlation, record length, and overconfidence in wind resource assessment

2016 ◽  
Vol 1 (2) ◽  
pp. 115-128 ◽  
Author(s):  
Nicola Bodini ◽  
Julie K. Lundquist ◽  
Dino Zardi ◽  
Mark Handschy
Keyword(s):  
Interannual Variability ◽  
Resource Assessment ◽  
Energy Estimates ◽  
Data Set ◽  
Wind Speeds ◽  
Wind Resource Assessment ◽  
Wide Range ◽  
Serial Correlations ◽  
Resource Levels ◽  
Wind Resource

Abstract. Interannual variability of wind speeds presents a fundamental source of uncertainty in preconstruction energy estimates. Our analysis of one of the longest and geographically most widespread extant sets of instrumental wind-speed observations (62-year records from 60 stations in Canada) shows that deviations from mean resource levels persist over many decades, substantially increasing uncertainty. As a result of this persistence, the performance of each site's last 20 years diverges more widely than expected from the P50 level estimated from its first 42 years: half the sites have either fewer than 5 or more than 15 years exceeding the P50 estimate. In contrast to this 10-year-wide interquartile range, a 4-year-wide range (2.5 times narrower) was found for "control" records where statistical independence was enforced by randomly permuting each station's historical values. Similarly, for sites with capacity factor of 0.35 and interannual variability of 6  %, one would expect 9 years in 10 to fall in the range 0.32–0.38; we find the actual 90  % range to be 0.27–0.43, or three times wider. The previously un-quantified effect of serial correlations favors a shift in resource-assessment thinking from a climatology-focused approach to a persistence-focused approach: for this data set, no improvement in P50 error is gained by using records longer than 4–5 years, and use of records longer than 20 years actually degrades accuracy.

scholarly journals Efficiency comparison of historical and newly developed Weibull parameters estimation methods

2020 ◽  
pp. 014459872095975
Author(s):  
Muhammad Sumair ◽  
Tauseef Aized ◽  
Syed Asad Raza Gardezi ◽  
Muhammad Waqas Aslam
Keyword(s):  
Accurate Method ◽  
Resource Assessment ◽  
Empirical Method ◽  
Parameters Estimation ◽  
Estimation Methods ◽  
Weibull Parameters ◽  
Wind Resource Assessment ◽  
Performance Evaluation Criterion ◽  
Efficiency Comparison ◽  
Wind Resource

This work compares the efficiency of historically used Weibull parameters estimation methods with newly developed method. Newly developed method has been termed as Combined Linearized Moment Method (CLMM). Five-year wind data at five locations namely Chaghi, Lehri, Badin, Hyderabad and Nankana Sahib (Pakistan) was used for the calculation of Weibull parameters. Efficiency was assessed and compared using R-Squared(R2), MSE, RMSE, wind error (WE), MAPE and Chi-test ([Formula: see text]). Each method was given a rank against each performance evaluation criterion and then an overall ranking was done. The study concluded that CLMM is the most accurate method among all while Empirical Method of Justus (EMJ) is the least accurate. Hence, CLMM can be used to estimate Weibull parameters for wind resource assessment with significant accuracy.

scholarly journals Year-to-year correlation, record length, and overconfidence in wind resource assessment

2016 ◽  
Author(s):  
Nicola Bodini ◽  
Julie K. Lundquist ◽  
Dino Zardi ◽  
Mark Handschy
Keyword(s):  
Serial Correlation ◽  
Resource Assessment ◽  
Wind Speeds ◽  
Wind Resource Assessment ◽  
Record Length ◽  
Future Production ◽  
Weather Stations ◽  
Resource Levels ◽  
Wind Resource ◽  
Control Sequences

Abstract. Wind resource assessments predict future production levels from historical data. To characterize how year-to-year variability in past wind speeds affects the certainty of future predictions, we analyze 62-year wind speed records of 60 weather stations in Canada, and compare the actual levels of each station's final 20 years to "predictions" made from previous periods of varying duration. We estimate both median (P50) and 10 % quantile (P90) levels using historical means and standard deviations, validating estimator performance on statistically-independent "control" sequences made by randomly permuting the 62 annual values of each station's record. Errors of estimates made from the control sequences always decline with record length; the central half of the stations’ exceedances falls within ranges of 44–55 % (P50) and 85–95 % (P90) for 42-year estimates. For the actual chronological records, on the other hand, error is lowest when estimates were made from short records (4–5 years) and increases with length after 15 years; for 42-year estimates the corresponding ranges are 0–45 % (P50) and 36–100 % (P90). The strong biases reflect a nearly nationwide downward trend in recorded wind speeds, but even a near-zero-trend subset of 30 stations exhibits interquartile ranges of 24–73 % (P50) and 80–100 % (P90), both twice as large as expected. These findings show that serial correlation in wind speeds can persist across decades, and, if ignored, results in substantial overconfidence in estimated resource levels.

scholarly journals Offshore Wind Energy Resource Assessment from Satellite Data Observations and WRF in Porto Santo Island

2020 ◽  
Author(s):  
Fabiola S. Pereira ◽  
Carlos S. Silva
Keyword(s):  
Wind Energy ◽  
Weibull Distribution ◽  
Energy Production ◽  
Energy Resource ◽  
Resource Assessment ◽  
Offshore Wind ◽  
Electricity Production ◽  
Offshore Wind Energy ◽  
Wind Resource Assessment ◽  
Wind Resource

Abstract. The vast majority of isolated electricity production systems such as Islands depends on fossil fuels. Porto Santo Island, a Portuguese UNESCO Biosphere Reserve candidate from Madeira Archipelago situated in the Atlantic Ocean, aims to become a sustainable territory in order to reduce its carbon footprint. A sustainable pathway goes through the integration of renewable energy in the electricity production system, in particular, the potential of offshore wind energy. The scope of this work has three main purposes: (1) the offshore wind resource assessment in Porto Santo Island, (2) the determination of a zone of interest regarding the combination of different parameters such us the bathymetry, distance to the coastline and integrated in the national situation plan of maritime space (3) the estimation of the annual energy production from the best-fitted Weibull Distribution. In the first place, a methodology for data analysis was defined processing netcdf data regarding a ten year wind hindcast from WRF (Weather Research and Forecasting) atmospheric model at 100 m above mean sea level from Ocean Observatory, annual and monthly mean offshore wind energy resource maps were created and a comparison with about 20 year times series of surface winds derived from remotely satellite scatterometer observations at different locations was made. Results show that the average annual mean wind speeds reach the range of 6.6–7.6 m/s in specific areas, situated in the northern part of Porto Santo Island with a Weibull distribution shape parameter (k) of 2.4–2.9. Based on the results, the wind resource assessment, the estimation of the annual wind energy production and capacity factors were calculated from the best-fitted Weibull distribution for each of the geographical coordinates selected. Comparisons with observational data show that WRF model is a proficient wind generating tool. The technical energy production potential and a priority zoning for offshore wind power development is performed using wind turbine generators of 3.3 MW–8.0 MW capacity, that could generate between 12 and 26 GWh of energy per year, while avoiding CO2 emissions. The results show that an offshore wind farm plan is an eligible choice, with an average annual wind power density reaching about 300  W/m2 at 100 m height in the north region.

The eastern-Iran wind-resource assessment through a planetary boundary layer simulation (2011–2015)

2019 ◽  
Vol 44 (3) ◽  
pp. 253-265
Author(s):  
Mohammadreza Mohammadpour Penchah ◽  
Hossein Malakooti
Keyword(s):  
Wind Power ◽  
Resource Assessment ◽  
Energy Potential ◽  
Wind Speeds ◽  
Eastern Iran ◽  
Wind Potential ◽  
East Of Iran ◽  
Wind Energy Potential ◽  
Low Wind Speeds ◽  
Wind Resource

Currently, in order to reduce the time and cost of wind measurement masts installation, numerical meteorological models are being utilized to simulate the regional wind climate. In this study, the Weather Research and Forecasting model was applied to assess the wind power potential in the East of Iran. After evaluating the model, the wind mean speed and the power map were prepared based on a 5-year simulation (2011–2015). The results indicated that there were high wind-energy-potential areas bordering Iran and Afghanistan. The model illuminated that Khaf city had a satisfactory wind potential, reasonably corresponding with the observations. The model error was greater for the low wind speeds. With regard to the wind resource map of the area, only 3% of the study area falls within class 3 or higher, and therefore, can be considered conformative to the wind power generation.

Practical Wind Resource Assessment for Tayside Region in Scotland

1995 ◽  
Vol 6 (4) ◽  
pp. 361-381
Author(s):  
G.S. Saluja ◽  
N.G. Douglas
Keyword(s):  
Renewable Energy ◽  
Wind Energy ◽  
Energy Resource ◽  
Resource Assessment ◽  
Wind Speeds ◽  
Wind Resource Assessment ◽  
Planning Policy ◽  
Policy Guideline ◽  
Wind Energy Development ◽  
Wind Resource

This paper presents the results of a study which estimates the practical wind energy resource for the Tayside Region of Scotland. The study considered all technical, environmental and legislative factors relevant to wind energy development. Due consideration was also given to National Planning Policy Guideline (NPPG6): Renewable Energy and Planning Advice Note 45 (PAN45): Renewable Energy Technology, issued by the Scottish Office Environment Department, in addition to the policies of the planning authorities within Tayside with regards to such matters as development in National Scenic Areas and other designated areas. An area of 1290 km2 was identified as being the minimum practical resource which is free from environmental and technical constraints and which has sufficiently high wind speeds to make extraction of energy from the wind commercially viable. This area could accommodate an installed wind energy capacity of 9675 MW and produce 24.6 TWh of wind generated electricity per annum.

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