With increasing importance being attached to big data mining, analysis, and forecasting in the field of wind energy, how to select an optimization model to improve the forecasting accuracy of the wind speed time series is not only an extremely challenging problem, but also a problem of concern for economic forecasting. The artificial intelligence model is widely used in forecasting and data pro...

Exponential increase in power consumption leads to the global attention towards pollution free and renewable energy resources. For instance, wind turbines to produce electrical energy thru wind energy. For wind energy domain, wind speed forecasting is of great significance for wind farms design and planning, its operational control, and wind power prediction etc. Due to the impact of several en...

Wind power has become an important source of power for some countries because wind is renewable, wind power is clean and no pollutants are produced compared to fossil fuels which are mainly used for the generation of energy today. Because of these reasons also in the Netherlands attention towards the use of wind power has grown. In the past decade, a lot of research has been performed on the fo...

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...

aalto university school of electrical engineering abstract of the master's thesis Wind is an important source of renewable energy with abundant availability at many places of the world. The integration of wind energy into the electricity grid brings new challenges on the system reliability due to its intermittent nature. A robust tool with good accuracy for wind power forecasting is an essentia...

The wind speed forecasting in Hong Kong is more difficult than in other places in the same latitude for two reasons: the great affect from the urbanization of Hong Kong in the long term, and the very high wind speeds brought by the tropical cyclones. Therefore, prediction model with higher learning ability is in need for the wind speed forecast in Hong Kong. In this paper, we try to employ the ...

This paper demonstrates the application of multiple imputations by chained equations and time series forecasting of wind speed data. The study was motivated by the high prevalence of missing wind speed historic data. Findings based on the fully conditional specification under multiple imputations by chained equations, provided reliable wind speed missing data imputations. Further, the forecasti...

In this paper, the spatio-temporal (multi-channel) linear models, which use temporal and the neighbouring wind speed measurements around the target location, for the best short-term wind speed forecasting are investigated. Multi-channel autoregressive moving average (MARMA) models are formulated in matrix form and efficient linear prediction coefficient estimation techniques are first used and ...

Achieving relatively high-accuracy short-term wind speed forecasting estimates is a precondition for the construction and grid-connected operation of wind power forecasting systems for wind farms. Currently, most research is focused on the structure of forecasting models and does not consider the selection of input variables, which can have significant impacts on forecasting performance. This p...

As a kind of clean and renewable energy, wind power is winning more and more attention across the world. Regarding wind power utilization, safety is a core concern and such concern has led to many studies on predicting wind speed. To obtain a more accurate prediction of the wind speed, this paper adopts a new hybrid forecasting model, combing empirical mode decomposition (EMD) and the general r...