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Airborne wind turbine technology is rapidly growing in purpose to overcome limitation of wind turbines working at low altitude. The high-altitude wind is strong to efficient power generation. Under varying wind conditions, wind forecasting in real time is necessary to be implemented for flight stabilization and power generation. This study is to investigate three widely-used forecasting models for...
In this paper a new artificial neural network (ANN) method is proposed to forecast wind speed forecasting. The use of wind power generation (WPG) is expected to reduce CO2 as the framework of environmental preservation. However, output of WPG is affected by the meteorological conditions significantly. As the first stage of research, this paper focuses on wind speed that affects the output of WPG significantly...
In this study, the wind speed prediction model is created that gives a minimum error for different hidden layer neuron numbers and delay step numbers. Using the one-minute time series, the prediction of the next wind speed is performed with the NAR neural network model. The predicted values of wind speed obtained are compared with predicted values of wind speed obtained with filter methods. For different...
Power generation from wind generators is always associated with some intermittency due to wind speed and other weather parameters variation, and accurate short-term forecasts are essential for their efficient and effective operation. This can well support transmission and distribution system operators and schedulers to enhance the power network control and management in the smart grid context. This...
We modeled in this paper the variation of wind speed as a renewable energy in Mediterranean Sea of Libya (North of Africa) using an artificial neural network (ANN). We developed multi-layer, feed-forward, back-propagation artificial neural networks for prediction monthly mean wind speed. The monthly mean wind speed data of 25 cities in Libya were monitored during the period of six years from 2010...
Wind power can be defined as the power produced by using wind as resource. This has a non-negligible impact which brings a lot of appreciable perks to the power supply and generation industry. An accurate forecast about the available wind energy production for the forthcoming hours is very crucial, so that exact planning and scheduling of the power generation from conventional units can be performed...
In this paper using Artificial Neural Network (ANN) are presented forecasting results of PM10 concentrations for the city of Sarajevo. Input data of the proposed model are meteorological variables (wind speed, humidity, temperature and pressure) and pollution variable (PM10 concentration) recorded in the Federal Institute for Hydrometeorology from 2010 to 2013. The proposed model is tested on the...
Forecasting of wind speed and wind power generation is indispensable for the effective operation of a wind farm and the optimal management of revenue and risks. Hybrid forecasting of time series data is considered to be a potentially effective alternative compared with the conventional single forecasting modeling approaches such as autoregressive integrated moving average (ARIMA) and artificial neural...
This paper introduces artificial neural network (ANN) for long term wind speed prediction. The online available dataset of 26 cities from NASA are used to evaluate the performance of ANN model. Data of 22 cities are used for training the neural network and remaining 4 cities data samples are used for testing purpose. Air temperature, earth temperature, relative humidity, daily solar radiation, elevation,...
Wind energy is found to be a huge power resource ever since the time human civilization has evolved. Its importance has increased manifold as fossil fuel resources are depleting off quickly. However, the availability and variability of wind energy resource poses a high degree of hindrance to their integration into power grids leading to various power issues such as, deregulation of supply - load balance,...
The intermittent and unstable nature of wind raises significant challenges for the operation of wind power systems, either residential installations or utility-scale implementations, necessitating the development of reliable and accurate wind power forecasting techniques. Given that wind speed forecasting is typically considered the intermediate step for wind power forecasting, the present work proposes...
A multistage advanced statistical method has been proposed for the real-time wind-electric power generation forecast of wind power plants (WPPs) based on a combination of artificial neural network (ANN) and support vector machine (SVM) models. In the first stage, output data of wind speed and wind direction from different numerical weather prediction (NWP) models are chosen among a set of grid points...
In modern years, wind energy has a significant development in the world. However, one of the major issues of power generated from wind is its uncertainty and resultant power. To solve the above- said problem, few approaches have been presented. In recent times, the Artificial Neural Networks (ANN) as a heuristic method has more applications for this propose. The Back-propagation (BP) neural network...
Rapid growth of wind power generation in addition to its high penetration in electrical power systems has brought wind power prediction into play. Wind power is a complex signal for modeling and forecasting. In this paper, wind power prediction model based on neural network and imperialist competitive algorithm (ICA) is presented to forecast wind power generation of wind farm of Alberta. Finally,...
Wind energy is one of the most promising renewable energy sources for power generation. As India has wind energy potential of around 45195 MW and the installed capacity is 17967 MW only. Keeping in view of the aforesaid prediction of wind energy is an important study for harnessing the wind energy potential. Various conventional and intelligent models are available in the literature for the prediction...
Wind turbine power output is totally intermittent in the nature. For grid connected wind turbine generators, power system operators (transmission system operators) need reliable and robust wind power forecasting system. Rapid changes in the wind generation relative to the load require proper energy management system to maintain the power system stability and of course to balance the power generation,...
Wind power is a significant alternate energy in times of energy crisis. In virtue of its intermittency and fluctuation, it poses several operational challenges to grid interfaced wind energy systems. This paper introduced autoregressive integrated moving average (ARIMA) model and artificial neural network (ANN) to forecast the hourly wind speed one to four hours ahead. The models are applied to wind...
Wind power presented a remarkable growth in the first decade of the 21st century, highly sustained by the economical and ecological benefits of this technology. Not only has it significantly contributed to reduce the dependence on fossil fuels in the production of electrical energy, wind power has also allowed to save great amounts of greenhouse gases emissions. This growth leads to an inevitable...
In this experiment, by using the method of artificial neural network and DPS DATA PROCESSING SYSTEM combined with the meteorological data of air temperature, relative air humidity, solar radiation, wind speed, soil water content and dew point temperature as the input variable, the author established the artificial neural network system to forecast the seedling water consumption of P.×euramericana...
In recent years, environmental considerations have prompted the use of wind power as a renewable energy resource. However, the biggest challenge in integrating wind power into the electric grid is its intermittency. One approach to deal with wind intermittency is forecasting future values of wind power production. Thus, several wind power or wind speed forecasting methods have been reported in the...
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