We study the problem of short term wind speed prediction, which is a critical factor for effective wind power generation. This is a challenging task due to the complex and stochastic behavior of the wind environment. Observing various periods in the wind speed time series present different patterns, we suggest a nonlinear adaptive framework to model various hidden dynamic processes. The model i...

Due to the intermittency of wind power generation, it is very hard to manage its system operation and planning. In order to incorporate higher wind power penetrations into power systems that maintain secure and economic power system operation, an accurate and efficient estimation of wind power outputs is needed. In this paper, we propose the stochastic prediction of wind generating resources us...

With the expansion of wind farm installations in most countries all over the world, the power generation has already significantly influenced on the stability and security of the power grid after gridconnection. Wind power forecasting is an effective method for guarantees stability of the power output from wind farm. This paper proposed an improved GM(1,1) based prediction method, and focuses o...

The accuracy of short-term wind speed prediction is very important for wind power generation. In this paper, a hybrid method combining ensemble empirical mode decomposition (EEMD), adaptive neural network based fuzzy inference system (ANFIS) and seasonal auto-regression integrated moving average (SARIMA) is presented for short-term wind speed forecasting. The original wind speed series is decom...

Wind energy is rapidly increasing and it is becoming a significant contributor to the electricity grid. Wind speed is an important factor in wind power production and integration. This paper presents a wind speed forecasting using feature selection method and bagging neural network. Feature selection plays an essential role in the machine learning environment and especially in the prediction ta...

The empirical mode decomposition (EMD) is well known for predicting wind speed.However, but the joint application of relevance vector machine (RVM) and empirical mode decomposition in wind speed forecasting is seldom found in the field. This paper proposes a relevance vector machine model based on empirical mode decomposition to predict the wind speed. Before the wind speed forecasting with RVM...

Wind energy has become a main challenge of conventional relic fuel energy, chiefly with the flourishing operation of multi-megawatt sized wind turbines. Though, wind with sensible speed is not sufficiently sustainable all over to construct an inexpensive wind farm. The probable site has to be systematically investigated at least with respect to wind speed profile and air density. Modelling and ...

In this paper, models for shortand long-term prediction of wind farm power are discussed. The models are built using weather forecasting data generated at different time scales and horizons. The maximum forecast length of the short-term prediction model is 12 h, and the maximum forecast length of the long-term prediction model is 84 h. The wind farm power prediction models are built with five d...

A multilayered artificial neural network has been used for predicting the mean monthly wind speed in regions of Cyprus where data are not available. Data for the period 1986-1996 have been used to train a neural network, whereas data for the year 1997 were used for validation. Both learning and prediction were performed with adequate accuracy. Two network architectures of the similar type have ...

ozone concentration in metropolitan areas frequently exceed regulatory standards now. since it is harmful on human health and environment, its modeling and visualization are of vital important. modeling and prediction of ozone is required by urban managers to control and prevent its effects. in this paper, important parameters influencing the hourly ozone concentration are estimated by using da...