Short and Mid-Term Wind PowerPlants ForecastingWith ANN Javad Mahmoudi MSc student of Electrical Engineering of Sharif University Tehran, Iran E-mail: Jmahmoudi66@gmail.com Majid Jamil Material and Energy Research Center(MERC), Department of Energy Karaj, Iran E-mail: m-jamil@merc.ac.ir Hossein Balaghi MSc student of Mechanical Engineering of Sharif University Tehran, Iran E-mail: Balaghi.hosein@gmail.com Abstract—In recent years, wind energy has a remarkable growth in the world, but one of the important problems of power generated from wind is its uncertainty and corresponding power. For solving this problem, some approaches have been presented. Recently, the Artificial Neural Networks (ANN) as a heuristic method has more applications for this propose. In this paper, short-term (1 hour) and mid-term (24 hours) power forecasting are presented for a sample wind power plant by multilayer ANN. The needed inputs data are temperature and wind speed for forecastingthe power. A case study has presented. Keywords-wind power forecasting; artificial neural network; power prediction I. INTRODUCTION Due to rising consumption of electricity, reduction of fossil fuel and its high pollution in world the orientation of Generation Companies (GenCo) of electricity energy have accelerated to renewable energies more than ever. Wind energy is important and available in most regions but due touncertainty wind speed and direction,the prediction has a significant role in power generation[1]. The basic problem of connection wind power to grid utility is forecastingin competitive market.Thereforpower forecasting tools will be needed [2],[3]. Precise prediction of power helps dispatch easily wind power and determines that how much reserve system should be consider for supporting of wind power units [4].For the purpose of maintenance, the forecasting is important for electricity industry, e.g. when one turbine must be disconnected or repaired for improvement of system performance. The important of wind forecasting is clear for dispatchability and cost of producing electricity from wind. Current and future challenges to forecasting wind power are integrating and automating regional forecasts with electricity scheduling systems and incorporating climate change impacts on wind projects[5].As the amount of wind energy requiring integration into the grid increase, short-Term forecasting became more important to both wind farm owners and the transmission as well as distribution operators[6]. II. HEURISTIC METHODES Due to high performance of heuristic methods for algorithms identification, they can be used to predict output power of wind turbines. Artificial neural networks classify information content as significant by analysis of input and output (I/O) data. Outputs are forecasted after training system with I/O data by new inputs [7]. In this paper, a new method is presented for one hour and one day ahead forecasting of wind turbine output power. III. ARTIFICIAL NEURAL NETWORK(ANN) The ANN is commonly used between heuristic methods. The first neural cell was used by Mac Lorch and Pittz in 1943. This method can be trained and extract nonlinear complicated relationbetween input and output. Since 1990s, ANN is entered in engineering science vastly [8]. These methods have high performance in estimation and approximation of real engineering systems [9]. Figure 1 shows structure of a neural processing unit and relation units. All inputs (X N ) are weighted with W N at first part of cell. Combination Function (+) sumsall weighted inputs at every nodes and adds a threshold value or bias () for changing its status. The bias increase and decrease sum of weighted inputs Figure 1. Structure of a neural processing unit and parameteres[8] Transfer Function 2012 Second Iranian Conference on Renewable Energy and Distributed Generation 978-1-4673-0665-2/12/$31.00 ©2012 IEEE 167