The growing demand for wind power has resulted in a market that naturally favors development of new wind farms over improvement of their performance. A chain of opportunities for performance improvement of any wind energy project parallels the supply chain activities. Raising energy and transportation costs are a complicating factor of the performance improvement projects. One of the weakest po...

Predicting short term wind speed is essential in order to prevent systems in-action from the effects of strong winds. It also helps in using wind energy as an alternative source of energy, mainly for Electrical power generation. Wind speed prediction has applications in Military and civilian fields for air traffic control, rocket launch, ship navigation etc. The wind speed in near future depend...

The wind power prediction plays an essential role in operation, planning, taking part in open access and real time balancing of power system. Various forecasting methodologies have been proposed in number of research papers since last few decades. Therefore, on the basis of available literature, this review analyses new and current developments in the area of wind power & prediction of its deri...

A nonreactive Lagrangian atmospheric diffusion model is used for the simulation of SO2 concentration around the As Pontes 1400-MW power plant located in northwestern Spain. This diffusion model has two kinds of input: 1) diagnostic wind fields from real measurements and 2) forecast wind fields from a 24-h mesoscale prediction. This model-based system is applied for a particular day around the A...

In the recent years, due to the increase in the average temperature and environmental pollution and also the demand for energy, finding new resources for energy generation has been a big challenge for the governments. Among the various renewable energy resources, the energy derived from the wind farms has absorbed a great deal of attention. Due to the increase in the power generated by the wind...

—In this paper, we present a modeling and simulation framework for conducting the adequacy assessment of a wind-integrated power system accounting for the associated uncertainties. A multi-perceptron artificial neural network (NN) is trained by a non-dominated sorting genetic algorithm–II (NSGA-II) to forecast point-values and prediction intervals (PIs) of the wind power and load. The output of...

The bootstrap resampling method is applied to an ensemble artificial neural network (ANN) approach (which combines machine learning with physical data obtained from a numerical weather prediction model) to provide a multi-ANN model super-ensemble for application to multi-stepahead forecasting of wind speed and of the associated power generated from a wind turbine. A statistical combination of t...

We compare structural different methods of the artificial intelligence for wind power prediction modeling and build additionally ensembles of the models. As input variables for these prediction methods weather data of a numerical weather prediction model are used. The performance of the presented methods is compared to the predictions of the neural network based model.

In the past researches, scholars have carried out short-term prediction for wind speed. The present work deals with long-term wind speed prediction, required for hybrid power generation design and contract planning. As the total database is quite large for long-term prediction, feature extraction of data by application of Lifting wavelet coefficients are exploited, along with soft computing tec...

For a sustainable integration of wind power into the electricity grid, precise and robust predictions are required. With increasing installed capacity and changing energy markets, there is a growing demand for short-term predictions. Machine learning methods can be used as a purely data-driven, spatio-temporal prediction model that yields better results than traditional physical models based on...