Wind speed forecasts are important for the operation and maintenance of wind farms and their profitable integration into power grids, as well as many important applications in shipping, aviation, and the environment. Modern machine learning techniques including neural networks have been used for...

This paper deals with the development of a neuro-fuzzy controller for a wind–diesel system composed of a stall regulated wind turbine with an induction generator connected to an ac bus-bar in parallel with a diesel generator set having a synchronous generator. A gasifier is capable of...

This paper presents a novel method for the forecasting of mean hourly wind speed data using time series analysis. The initial point for this approach is mainly the fact that none of the forecasting approaches for hourly data, that can be found in the literature, based on time series analysis or...

In this work, a new approach is tested by applying neural networks treatment to meteorological time-series data sets, recorded during 1991–2000 at certain Greek locations, in order to create fully appropriate solar data information. Neural networks, in this case, are used for creating missing...

This paper presents a development and implementation of a PC-based maximum power point tracker (MPPT) for PV system using neural networks (NN). The system consists of a PV module via a MPPT supplying a dc motor that drives an air fan. The control algorithm is developed to use the artificial NN...

A data-driven approach for maximization of the power produced by wind turbines is presented. The power optimization objective is accomplished by computing optimal control settings of wind turbines using data mining and evolutionary strategy algorithms. Data mining algorithms identify a...

Comparison of two techniques for wind speed forecasting in the South Coast of the state of Oaxaca, Mexico is presented in this paper. The Autoregressive Integrated Moving Average (ARIMA) and the Artificial Neural Networks (ANN) methods are applied to a time series conformed by 7 years of wind...

This paper presents a comparison of various forecasting approaches, using time series analysis, on mean hourly wind speed data. In addition to the traditional linear (ARMA) models and the commonly used feed forward and recurrent neural networks, other approaches are also examined including the...

The exploitation of the renewable energy sources plays a key role for achieving the CO2 emissions reduction targets established by the Kyoto Protocol, as well as for facing the shortage of world fossil fuels reserves.

This paper introduces support vector machines (SVM), the latest neural network algorithm, to wind speed prediction and compares their performance with the multilayer perceptron (MLP) neural networks. Mean daily wind speed data from Madina city, Saudi Arabia, is used for building and testing both...