@inproceedings{d3bfeb1aed6c4d57839f264aae6a369f,
title = "Notice of Removal: Impact of distributed PV on demand load forecasts",
abstract = "The growing penetration of distributed photovoltaic (DPV) solar power production 'behind the meter' has been making a substantial impact on electrical net load, increasing its variability and making it more difficult to predict. NCAR's DICast{\textregistered} provides accurate weather forecasts, which are harnessed by a statistical learning approach based on regression trees to forecast both DPV and net load. The system provides day-ahead forecasts within a couple percent of accuracy on average. Its design permits DPV predictions to be directly integrated into the net load forecast, enabling straightforward adaptation over time as DPV capacity increases. This approach will allow utilities and independent system operators to better deal with increasing penetration of distributed generation.",
keywords = "Distributed generation, Grid integration, Load forecasting, Photovoltaic power, Solar energy",
author = "Haupt, \{Sue Ellen\} and Susan Dettling and John Williams and Julia Pearson and Tara Jensen and Thomas Brummet and Branko Kosovic and Gerry Wiener and Tyler McCandless",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 44th IEEE Photovoltaic Specialist Conference, PVSC 2017 ; Conference date: 25-06-2017 Through 30-06-2017",
year = "2017",
doi = "10.1109/PVSC.2017.8366215",
language = "English",
series = "2017 IEEE 44th Photovoltaic Specialist Conference, PVSC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--6",
booktitle = "2017 IEEE 44th Photovoltaic Specialist Conference, PVSC 2017",
address = "United States",
}