Concept for Intra-Hour PV Generation Forecast based on Distributed PV Inverter Data - An Approach Considering Machine Learning Techniques and Distributed Data

Stefan Übermasser, Simon Kloibhofer, Philipp Weihs, Matthias Stifter

2018

Abstract

The mass-introduction of small scale power generation units like photovoltaic systems at household levels increase the risk for system unbalances, due to their stochastic generation profile. Additionally, upcoming technologies such as electric vehicles, battery storage systems and energy management systems lead to a change from consumer households to prosumers with a significant different residual load profile. For optimizing the profile of future prosumers, especially the forecast for PV generation is crucial. Whilst traditional weather forecasts are based on a few hundred metering locations in the case of Austria, more than 55000 PV systems are currently connected to the Austrian Power grid. Due to the low areal coverage of common metering locations, weather forecasts do not take local phenomena like shadows from clouds into account. An approach using generation data from neighbouring PV systems together with machine learning methods provides a promising alternative for individual location based intra-hour forecasts. This paper describes the requirements and methods of such a concept and concludes with a first proof of concept.

Paper Citation

in Harvard Style

Übermasser S., Kloibhofer S., Weihs P. and Stifter M. (2018). Concept for Intra-Hour PV Generation Forecast based on Distributed PV Inverter Data - An Approach Considering Machine Learning Techniques and Distributed Data.In Proceedings of the 7th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS, ISBN 978-989-758-292-9, pages 286-293. DOI: 10.5220/0006775802860293

in Bibtex Style

@conference{smartgreens18,
author={Stefan Übermasser and Simon Kloibhofer and Philipp Weihs and Matthias Stifter},
title={Concept for Intra-Hour PV Generation Forecast based on Distributed PV Inverter Data - An Approach Considering Machine Learning Techniques and Distributed Data},
booktitle={Proceedings of the 7th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,},
year={2018},
pages={286-293},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006775802860293},
isbn={978-989-758-292-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,
TI - Concept for Intra-Hour PV Generation Forecast based on Distributed PV Inverter Data - An Approach Considering Machine Learning Techniques and Distributed Data
SN - 978-989-758-292-9
AU - Übermasser S.
AU - Kloibhofer S.
AU - Weihs P.
AU - Stifter M.
PY - 2018
SP - 286
EP - 293
DO - 10.5220/0006775802860293