@inproceedings{7da2995614b14803ba81f32909ef6e54,
title = "Short-term photovoltaic prediction by using H∞ filtering and clustering",
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 = "Clustering, Estimation, PV, Prediction, Short-term, Smart Grid, k-means",
author = "Yasuhiko Hosoda and Toru Namerikawa",
year = "2012",
month = jan,
day = "1",
language = "English",
isbn = "9781467322591",
series = "Proceedings of the SICE Annual Conference",
publisher = "Society of Instrument and Control Engineers (SICE)",
pages = "119--124",
booktitle = "2012 Proceedings of SICE Annual Conference, SICE 2012",
note = "2012 51st Annual Conference on of the Society of Instrument and Control Engineers of Japan, SICE 2012 ; Conference date: 20-08-2012 Through 23-08-2012",
}