The aim of this work is to forecast wind energy by fitting the wind speed logged data, that have been measured over a year period (Nov. 2019-Mar. 2021), on a unique probability density function selected among a number...
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The aim of this work is to forecast wind energy by fitting the wind speed logged data, that have been measured over a year period (Nov. 2019-Mar. 2021), on a unique probability density function selected among a number of similar probability functions, as it is not always possible to select one distribution function that fits all wind speed regimes. The wind speed and direction data were measured at Fujairah site, which are affected by long-term fluctuation of +/- 10% of wind speed, and short-term fluctuation of more than +/- 20%. Based on the foregoing measurements, five different probability density functions can be fitted, namely Weibull, Rayleigh, Gamma, Lognormal and Exponential, with their associated parameters. A procedural algorithm is proposed for wind speed forecasting with best selected fitting distribution function, using a procedural forecast-check method, in which forecasting is performed with time on the most suitable distribution function that fits the foregoing data, depending on minimum errors accumulated from preceded measurements. Different error estimation methods are applied. The algorithm of selecting different distribution functions with time, makes energy prediction more accurate depending on the fluctuation of wind speed. A detailed probabilistic analysis is carried out to predict probable wind speed, and hence wind energy, based on variations of the parameters of the selected fitting distribution function.
A combination of procedural algorithms and predicate logic formalisms is proposed to generalize complex coastlines. The advantage of this combined strategy is to cover both the geometric and conceptual aspects of the ...
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The aim of this work is to find the most efficient and suitable input features to be selected for forecasting monthly wind energy accurately. Machine learning is employed for a modular pipelined neural network, compos...
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The aim of this work is to find the most efficient and suitable input features to be selected for forecasting monthly wind energy accurately. Machine learning is employed for a modular pipelined neural network, composed of time-delayed and feedforward networks with features of metrological variables such as atmospheric temperature, humidity, wind direction, and wind speed frequency distribution parameters. Logged data over a year's period at a UAE site are analyzed on daily and monthly bases depending on their variation characteristics, in which standard Weibull probability distribution function is used for the feedforward neural network together with wind direction data, while daily average ambient temperature and humidity are attempted for the composite time delay networks. Different network abstractions of input features are compared, and it is found that wind direction data offer a better wind speed forecast. Wind energy is calculated based on monthly forecasting. A detailed adaptive probabilistic analysis is conducted to predict thresholds in variations of the forecast analysis. Error estimation tools are performed for adopting this method.
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