A trivariate maximum entropy distribution of significant wave height, wind speed and the relative direction is proposed here. In this joint distribution, all the marginal variables follow modified maximum entropy distributions, and they are combined by a correlation coefficient matrix based on...

Wind power, as a promising renewable energy source, has environmental benefits, as well as economic and social ones. To evaluate wind energy properly and efficiently, this study proposes a hybrid forecasting approach that combines the Extreme Learning Machine (ELM), which rarely presents in...

This study proposes a hybrid forecasting approach that consists of the EWT (Empirical Wavelet Transform), CSA (Coupled Simulated Annealing) and LSSVM (Least Square Support Vector Machine) for enhancing the accuracy of short-term wind speed forecasting. The EWT is employed to extract true...

Reliable wind modelling is of crucial importance for wind farm development. The common practice of using sector-wise Weibull distributions has been found inappropriate for wind farm layout optimization. In this study, we propose a simple and easily implementable method to construct joint...

Electricity markets may become more sensitive to weather conditions because of a higher penetration of renewable energy sources and climatic changes. We investigate whether weather conditions had a growing influence on the average daily day-ahead price in the Dutch electricity market in the...

This work investigates the possibility of determining the degree of atmospheric pollution over the small Greek city of Serres based solely on measurements of global solar radiation and some simple meteorological variables. Hourly data of solar irradiance on an horizontal plane are recorded...

Wind speed forecasting is one of the most important technologies to guarantee the wind energy integrated into the whole power system smoothly. In this paper a hybrid model named EMD–ANN for wind speed prediction is proposed based on the Empirical Mode Decomposition (EMD) and the Artificial...

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...

The steady growth in the utilisation of wind power for electricity generation has led to increased interest in methods for synthetically generating wind speeds that are able to more accurately determine the site potential.

An important factor to consider when evaluating wind energy potential is the wind speed persistence. In this study, persistence of the wind speed in Peninsular Malaysia is investigated based on the hourly data available at 10 wind stations from 2007 to 2009. To determine the degree of...