Comparison of Optimum Spline-Based Probability Density Functions to Parametric Distributions for the Wind Speed Data in Terms of Annual Energy Production

Munir Elfarra,Mustafa Kaya

doi:10.3390/en11113190

Munir Elfarra, Mustafa Kaya

Open Access

https://doi.org/10.3390/en11113190

Copy DOI

Journal: Energies	Publication Date: Nov 17, 2018
Citations: 7	License type: CC BY 4.0

Affiliation: Ankara Yıldırım Beyazıt University

Abstract

The common approach to wind energy feasibility studies is to use Weibull distribution for wind speed data to estimate the annual energy production (AEP). However, if the wind speed data has more than one mode in the probability density, the conventional distributions including Weibull fail to fit the wind speed data. This highly affects the technical and economic assessment of a wind energy project by causing crucial errors. This paper presents a novel way to define the probability density for wind speed data using splines. The splines are determined as a solution of constrained optimization problems. The constraints are the characteristics of probability density functions. The proposed method is implemented for different wind speed distributions including multimodal data and compared with Weibull, Weibull and Weibull and Beta Exponentiated Power Lindley (BEPL) distributions. It is also compared with two other nonparametric distributions. The results show that the spline-based probability density functions produce a minimum fitting error for all the analyzed cases. The AEP calculated based on this method is considered to have high fidelity, which will decrease the investment risk.

Highlights

Population and industry increase in the world which means that the demand for energy increases too
The results show that the spline-based probability density functions produce a minimum fitting error for all the analyzed cases
A new method is proposed for the probability distribution function of the observed wind speed data

Summary

Introduction

Population and industry increase in the world which means that the demand for energy increases too. Fossil-fuel based energy sources have negative effects on the environment and they are running out. The greenhouse gases, such as carbon dioxide (CO2 ), Methane (CH4 ) and Nitrous Oxide (N2 O), are the main factors of increasing global warming [1,2]. Those gases arise from burning fossil fuels. Alternative renewable sources must be developed to decrease the emission of greenhouse gases. Wind energy is one of the renewable energy sources which is rapidly growing in most of the countries due to its relatively low cost [3]

Methods

Results

Conclusion