Currently, among renewable distributed generation systems, wind generators are receiving a great deal of interest due to the great economic, technological, and environmental incentives they involve. However, the uncertainties due to the intermittent nature of wind energy make it difficult to operate electrical power systems optimally and make decisions that satisfy the needs of all the stakehol...

Of the various renewable energy resources, wind power is widely recognized as one of the most promising. The management of wind farms and electricity systems can benefit greatly from the availability of estimates of the probability distribution of wind power generation. However, most research has focused on point forecasting of wind power. In this paper, we develop an approach to producing dens...

The short-term forecast of the wind power of a wind farm is of great significance for the security and stability of a grid-connected generation system. An accurate forecast may reduce the spinning reserve of a grid while providing reliable references for operation dispatch of a wind farm. In order to improve the accuracy of short-term forecasts, introducing the phase-space reconstruction techni...

Studies have demonstrated that changes in the climate affect wind power forecasting under different weather conditions. Theoretically, accurate prediction of both output and using statistics-based models is difficult. In practice, traditional machine learning can perform long-term with a mean absolute percentage error (MAPE) 10% to 17%, which does not meet engineering requirements for our renew...

Forecasting wind power generation up to a few hours ahead is of the utmost importance for efficient operation systems and participation in electricity markets. Recent statistical learning approaches exploit spatiotemporal dependence patterns among neighbouring sites, but their requirement sharing confidential data with third parties may limit use practice. This explains recent interest distribu...

In this article we present an approach that enables joint wind speed and wind power forecasts for a wind park. We combine a multivariate seasonal time varying threshold autoregressive moving average (TVARMA) model with a power threshold generalized autoregressive conditional heteroscedastic (power-TGARCH) model. The modeling framework incorporates diurnal and annual periodicity modeling by peri...

The dayahead short term load forecasting (STLF) is a necessary daily task for power dispatch. Short term load forecasting is essential for unit commitment, economic allocation of generation, maintenance schedules. This paper presents a solution methodology using fuzzy logic for short term load forecasting. Fuzzy logic approach is implemented on weather sensitive data and historical load data fo...