This paper describes the performance, generated power flow distribution and redistribution for each power plant on the grid based on adapting load and weather forecasting data. Both load forecasting and weather forecasting are used for collecting predicting data which are required for optimizing the performance of the grid. The stability of each power systems on the grid highly affected by load...

The integration of wind farms in power networks has become an important problem. As electricity cannot be preserved because of the highest cost of storage, electricity production must following market demand, necessarily. Short-long term wind forecasting over different time steps is becoming an important process for the management of wind farms. Time series modelling of wind speeds is based on ...

Wind speed forecasting has an unsuperseded function in the high-efficiency operation of wind farms, and is significant in wind-related engineering studies. Back-propagation (BP) algorithms have been comprehensively employed to forecast time series that are nonlinear, irregular, and unstable. However, the single model usually overlooks the importance of data pre-processing and parameter optimiza...

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...

This paper studies the application of Kalman filtering as a post-processing method in numerical predictions of wind speed. Two limited-area atmospheric models have been employed, with different options/capabilities of horizontal resolution, to provide wind speed forecasts. The application of Kalman filter to these data leads to the elimination of any possible systematic errors, even in the lowe...

We present regression automata (RA), which are novel type syntactic models for time series forecasting. Building on top of conventional state-merging algorithms for identifying automata, RA use numeric data in addition to symbolic values and make predictions based on this data in a regression fashion. We apply our model to the problem of hourly wind speed and wind power forecasting. Our results...

Wind speed forecasting can accurately improve prediction efficiency of wind power in wind farm, decrease failure probability of wind turbine, and extend life cycle. An innovative algorithm is proposed to optimize both the parameters of least squares support vector machine (LSSVM) and the procedure of finding sparse support vector. Firstly, the defects of support vector are analyzed. Then inequa...