Efficiency comparison of historical and newly developed Weibull parameters estimation methods

Sumair, Muhammad; Aized, Tauseef; Gardezi, Syed Asad Raza; Aslam, Muhammad Waqas

doi:10.1177/0144598720959758

Cited by 12 publications

(8 citation statements)

References 34 publications

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“…Metode yang digunakan adalah distribusi Weibull karena cocok digunakan untuk analisis data kecepatan angin di Pulau Jawa guna mengetahui potensi energi angin di daerah tersebut selama periode waktu 2011-2015. Seperti yang dijelaskan dalam jurnal (Sumair et al, 2021;Rodrigo and Larico, 2021) bahwa distribusi Weibull bisa digunakan untuk mengetahui potensi energi angin di daerah tertentu selama periode waktu tertentu.…”

Section: Pendahuluanunclassified

Analisis Data Kecepatan Angin di Pulau Jawa Menggunakan Distribusi Weibull

Dewi¹,

Handini²

2022

JSA

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Wind is one of the renewable energy products that is environmentally friendly and has a great opportunity as a source of meeting the energy needs of the world's population. In the use of wind as renewable energy, research must be carried out first to determine wind conditions in an area and the method that can be used is the Weibull distribution. In this journal, what will be discussed is the analysis of wind speed data on the island of Java using the Weibull distribution. From the results of the analysis, the wind speed results by province are as follows, Banten Province 2 m/s with a probability of 65%, DKI Jakarta 3 m/s with a probability of 30%, West Java 3 m/s with a probability of 90%, Central Java 8 m/s s with a probability of 11%, East Java 7 m/s with a probability of 7%, DI Yogyakarta is less than 1 m/s with a probability of 98%. The results of the plot of the probability solid function of the Weibull distribution show that the shape of the provincial velocity data curve that corresponds to the Weibull distribution curve is only the curve of East Java Province with a value of 1 < k < 2, namely k = 1.74.

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Section: Pendahuluanunclassified

Analisis Data Kecepatan Angin di Pulau Jawa Menggunakan Distribusi Weibull

Dewi¹,

Handini²

2022

JSA

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“…Besides, in the study that was published by Sumair, Aized, Gardezi, and Aslam [10], they have compared some new and old parameter estimation methods in five different regions of Pakistan. These methods were Combined Linearized Moment Method (CLMM) and Method of Moment (MOM).…”

Section: A Related Workmentioning

confidence: 99%

Performance Comparison of New Generation Parameter Estimation Methods for Weibull Distribution to Compute Wind Energy Density

Onay

Dokur

Kurban

2021

ELEKTRON ELEKTROTECH

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To install a wind energy conversion system to a region, the wind speed characteristics of that region must be identified. The two-parameter Weibull distribution is highly efficient in modeling wind speed characteristics. In this study, the wind speed data of 32 cities in three different regions of Turkey have been comparatively analysed to estimate Weibull distribution function parameters by the use of three well-known methods (Graphical Method (GM), Maximum Likelihood Method (MLM), Justus Moment Method (JMM)) and three new parameter estimation methods (Energy Pattern Factor Method (EPFM), Wind Energy Intensification Method (WEIM), Power Density Method (PD)) which have been proposed in recent years. Three years of hourly wind speed data of the specified regions have been used. The performance metrics of these analyses have been compared using Wind Energy Error (WEE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2). The results have shown that while the PD method has high model performance, the JMM is closely competitive with the MLM. Besides, the wind energy densities that were estimated by using actual data have been compared with the resulting Weibull distribution. It has been clear that the method that has the closest estimation to the actual values is the PD method.

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“…Energy utilization has marked effects on environmental conservation; any effort made to develop and utilize energy sources can only be justified if, along with technical and economic feasibility, it does not pose environment to risk. [1][2][3][4][5] In the wake of global inclination towards cutting down the conventional energy sources (fossil fuels etc.) because of environmental concerns, green energy sources are seeking huge attention these days.…”

Section: Introductionmentioning

confidence: 99%

Computing optimum solar tilt angles for photovoltaic applications through python code simulation

Sumair

Pasha

Aized

et al. 2023

Env Prog and Sustain Energy

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Carrying out the dual objectives of computing the optimized tilt angles for multiple locations in Pakistan along with recommending a generalized approach for the estimation of optimum tilt angle for any location in country, this study was conducted. Python code was developed for the optimization purpose of daily, monthly, seasonally, biannually and annual solar tilt angle. Detailed analysis was presented for one location (Karachi) and a comparative analysis of optimum tilt angle conditions has been conducted for all investigated sites. Daily optimum tilt angles were found to vary from −12.0° to 55.5°. Similarly, monthly optimized tilt angles were found to vary from −11.5° (in June) to 55.0° (in December). Furthermore, seasonal tilt angles were found as 46.0 °, 2.0 °, 0.5 ° and 45.0 ° in winter, spring, summer and autumn respectively. Finally, annual optimum tilt angle (fixed throughout the year) was found as 25.5° (closer to the latitude value of Karachi). It has been found that daily adjustment of tilt angle causes a gain of 6.85% in annual energy yields when compared with fixed angle throughout the year. Similarly, energy gains were found as 0.15%, 1.24% and 6.83%, when tilt angle was adjusted daily as compared to monthly, seasonal and biannual adjustment cases respectively. The optimum tilt angle for three pairs of locations having comparable latitudes were compared and it was found that the sites having comparable latitudes do not have any marked difference in their optimum tilt angle values.

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Efficiency comparison of historical and newly developed Weibull parameters estimation methods

Cited by 12 publications

References 34 publications

Analisis Data Kecepatan Angin di Pulau Jawa Menggunakan Distribusi Weibull

Analisis Data Kecepatan Angin di Pulau Jawa Menggunakan Distribusi Weibull

Performance Comparison of New Generation Parameter Estimation Methods for Weibull Distribution to Compute Wind Energy Density

Computing optimum solar tilt angles for photovoltaic applications through python code simulation

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