Stochastic analysis for wind speed forecasting

Abstract: The stability and availability required on the electrical power systems with wind sources are directly related to the accuracy of a short-term forecasting wind speed model. This paper presents a wind speed forecasting model based on one of the widely used time-series regression models, namely the Auto- Regressive Integrated Moving-Average (ARIMA). The method requires historical wind speed data for a given area, collected over a long time interval, in order to evaluate the required parameters of the wind speed ARIMA model

A statistical analysis of Cape Town wind profile

Abstract The increased integration of wind power into electric power systems presents new challenges for effective planning and operation of these systems. The Weibull distribution is a widely used distribution, especially for modelling the random variable of wind speed. In this respect, the authors present a comparative analysis of a number of methods used for estimating Weibull parameters. Results for a real-world database are presented in a case study format. The techniques require historical wind speed data, collected over a particular time interval, to establish the parameters of wind speed distribution for a specific location, namely Cape Town, South Africa

Assessment of wind power potential as a renewable energy source in Western Cape, South Africa

The worldwide utilisation of renewable energy in electric power systems is growing rapidly due to concerns about the environment and the depletion of the sources of conventional power generation. The Weibull distribution is a widely used distribution, especially for modelling the random variable of the wind speed. In this respect, the authors present a comparative analysis of a number of methods used for estimating Weibull parameters. Results for a real – word database, are presented in a case study format. The techniques require historical Wind speed data, collected over a particular time interval, to establish the parameters of wind speed distribution for a specific location, namely Cape Town, South Africa. As can be observed, the scale and shape parameters, from the whole database and from seasonal values, have the best estimation in case of maximum likelihood (MLE). Thus, it can be summarised that the maximum likelihood (MLE) is the best method used to estimate the parameters for the two-parameter Weibull distributions taking into consideration the mean bias error (MBE) and the root mean squares error (RMSE) as measurements of comparison, while the maximum likelihood (MLE) method is the least accurate method.Electrical and Mining EngineeringM.Tech (Engineering Electrical

Reliability evaluations of power systems including wind power generation

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