The prediction of wind farm output power is considered as an effective way to increase the wind power capacity and improve the safety and economy of power system. It is one of the hot research topics on wind power. The wind farm output power is related to many factors such as wind speed, temperature, etc., which is difficult to be described by some mathematical expression. In this paper, Back P...

The present study analyzes air quality for Carbon monoxide (CO), in Esfahan with the measurements taken in three different locations to prepare average data in the city. The average concentrations have been measured every 24 hours, every month and every season with the results showing that the highest concentration of CO occurs generally in the morning and at the beginning of night, while the l...

Abstract In order to improve the wind power consumption level of new system with energy as main body, it is necessary accurately predict speed. The key refine dynamic trend and potential physical structure in speed sequence. Firstly, on basis library Koopman dynamics theory encoder structures, a physically constrained spatio-temporal neural network built, which generates linear evolution moment...

Improving short-term wind speed prediction accuracy and stability remains a challenge for forecasting researchers. This paper proposes new variational mode decomposition (VMD)-attention-based spatio-temporal network (VASTN) method that takes advantage of both temporal spatial correlations speed. First, VASTN is hybrid model combines VMD, squeeze-and-excitation (SENet), attention mechanism (AM)-...

Nowadays, propagation of wind turbines make challenges to supply safe power to the grid. Because of wind speed changes, supervisors are concerned to wind turbines, be able to produce appropriate electric power during the wind speed changes. As a matter of fact, investors are mostly like to invest on offshore wind farms, because of their more stable and continuous wind speed rather than onshore ...

The motivation for this work is the study and prediction of wind ramp events occurring in a large-scale wind farm located in the US Midwest. In this paper we introduce the SHREA framework, a stream-based model that continuously learns a discrete HMM model from wind power and wind speed measurements. We use a supervised learning algorithm to learn HMM parameters from discretized data, where ramp...

Careful planning of the electrical power sector is of great importance since the decisions to be taken involves the commitment of large resources, with potentially serious economic risks for the electrical utility and the economy as a whole. There are different types of techniques available for analysis and prediction of randomly varying parameters. They are classified as statistical, intellige...

according to this fact that wind is now a part of global energy portfolio and due to unreliable and discontinuous production of wind energy; prediction of wind power value is proposed as a main necessity. in recent years, various methods have been proposed for wind power prediction. in this paper the prediction structure involves feature selection and use of artificial neural network (ann). in ...

Results are presented on the performance of Adaptive NeuroFuzzy Inference system (ANFIS) for wind velocity forecasts in the Isthmus of Tehuantepec region in the state of Oaxaca, Mexico. The data bank was provided by the meteorological station located at the University of Isthmus, Tehuantepec campus, and this data bank covers the period from 2008 to 2011. Three data models were constructed to ca...