%0 Journal Article
%T A New Hybrid Forecasting Strategy Applied to Mean Hourly Wind Speed Time Series
%A Stylianos Sp. Pappas
%A Dimitrios Ch. Karamousantas
%A George E. Chatzarakis
%A Christos Sp. Pappas
%J Journal of Wind Energy
%D 2014
%R 10.1155/2014/683939
%X An alternative electric power source, such as wind power, has to be both reliable and autonomous. An accurate wind speed forecasting method plays the key role in achieving the aforementioned properties and also is a valuable tool in overcoming a variety of economic and technical problems connected to wind power production. The method proposed is based on the reformulation of the problem in the standard state space form and on implementing a bank of Kalman filters (KF), each fitting an ARMA model of different order. The proposed method is to be applied to a greenhouse unit which incorporates an automatized use of renewable energy sources including wind speed power. 1. Introduction Energy is considered amongst the most significant factors that are closely related to both economic and social developments. It is also a fact that nowadays the majority of the electrical energy production is based on the fossil fuels, which on one hand are, without any doubt, highly efficient but on the other are responsible for the emission of greenhouse gases and their reserves are limited. Consequently renewable sources of energy, such as wind, biomass, solar power, and wave power, have been already adopted for electric power production. It is well known that the wind power generation raises issues of reliability due to the fact that the wind speed is significantly and directly affected by various factors such as the type of the terrain, the height, season of the year, atmospheric conditions, obstacles present, and many more. This leads to the conclusion that unless the reliability of the wind power generation is at an acceptable level, wind power is not eligible for constant electrical energy supply to the power system [1, 2]. Recent studies have shown that combined forecasting methods can offer robust solutions and can be efficiently implemented to various real-life problems in diverging fields such as chemical processes, economics, load forecasting, tourism demand, environmental issues, medicine, and many more [3¨C7]. In this study a hybrid model is presented that reveals the advantages of an ARMA and SVM model in wind speed modelling and prediction problem. Initially successful model identification and parameter estimation have to be performed in order to choose the most appropriate ARMA models. For tackling this task the well-established MMPA was used. This approach was introduced by Lainiotis [8, 9] and summarizes the parametric model uncertainty into an unknown, finite dimensional parameter vector whose values are assumed to lie within a known set of finite
%U http://www.hindawi.com/journals/jwe/2014/683939/