Title :
Short-term photovoltaic prediction by using H∞ filtering and clustering
Author :
Hosoda, Yasuhiko ; Namerikawa, Toru
Author_Institution :
Dept. of Syst. Design Eng., Keio Univ., Kanagawa, Japan
Abstract :
This paper deals with prediction algorithm applying for photovoltaic (PV) systems in smart grid. This prediction is aim to predict the amount of the next day of generation using the previous data and the weather forecast which get from Japan Meteorological Agency. The procedure of prediction consists of two steps, the data processing and the unknown parameters estimation. In the data processing, our proposed method considers the characteristics of PV generation using cluster ensemble. We propose the cluster ensemble based on k-means to choose the groups with a correlation with previous data. In the unknown parameters estimation, we provide the regression model for PV generation and the unknown parameters are estimated via H∞ filtering. The effectiveness of the proposed prediction method is demonstrated through numerical simulations.
Keywords :
electric power generation; filtering theory; load forecasting; photovoltaic power systems; regression analysis; smart power grids; H∞ filtering; PV generation; cluster ensemble; data processing; k-means method; regression model; short-term photovoltaic prediction; smart grid; unknown parameter estimation; weather forecast; Equations; Estimation; Mathematical model; Noise; Parameter estimation; Power generation; Prediction algorithms; Clustering; Estimation; H∞ Filtering; PV; Prediction; Short-term; Smart Grid; k-means;
Conference_Titel :
SICE Annual Conference (SICE), 2012 Proceedings of
Conference_Location :
Akita
Print_ISBN :
978-1-4673-2259-1