@inproceedings{5575c14fc58640eda14ba6381876ff76,
title = "Solar forecasting by K-Nearest Neighbors method with weather classification and physical model",
abstract = "With the increasing penetration of solar photovoltaic (PV) generation in the power system, the reliability of the distribution system and efficiency of PV systems have garnered increasing attention in recent years. Forecasting the PV output is one way to decrease the uncertainty of such power systems. In this study, we present a K-Nearest Neighbors algorithm based forecasting model, which can provide the estimated PV output by utilizing numerical weather and solar irradiance prediction data. This forecasting model also includes a weather condition classification process and a physical model of PV units. Numerical results are evaluated by using data from an existing 128kW rooftop PV system.",
keywords = "k-nearest neighbors, photovoltaic power system, physical model, solar forecasting, weather condition classification",
author = "Zhao Liu and Ziang Zhang",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 48th North American Power Symposium, NAPS 2016 ; Conference date: 18-09-2016 Through 20-09-2016",
year = "2016",
month = nov,
day = "17",
doi = "10.1109/NAPS.2016.7747859",
language = "English",
series = "NAPS 2016 - 48th North American Power Symposium, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Gao, \{David Wenzhong\} and Jun Zhang and Amin Khodaei and Eduard Muljadi",
booktitle = "NAPS 2016 - 48th North American Power Symposium, Proceedings",
}