Exploring the Impact of Data Quality and Availability on PV Power Plant Yield Prediction Using Machine Learning and Analytical Models

Alexander Prüller; Marcus Rennhofer; Bernhard Kubicek; Martin Gröschl

Exploring the Impact of Data Quality and Availability on PV Power Plant Yield Prediction Using Machine Learning and Analytical Models

Alexander Prüller, Marcus Rennhofer (Vortragende:r), Bernhard Kubicek, Martin Gröschl

Power and Renewable Gas Systems

Publikation: Posterpräsentation ohne Beitrag in Tagungsband › Posterpräsentation ohne Eintrag in Tagungsband

Abstract

While using machine learning models for predictions of PV yield
is increasing in popularity, attention and awareness of the used
training data is becoming essential. This study investigates the
robustness of photovoltaic (PV) power plant yield predictions
under diverse data scenarios

Originalsprache	Englisch
Publikationsstatus	Angenommen/Im Druck - 25 Sept. 2024

Research Field

Hybrid Power Plants

Schlagwörter

Photovoltaics
Machine Learning
Yield
Forecast
sparse data

Andere Dateien und Links

PVSEC_2024_MRennhofer_Poster_ML-Reduce-Data

Diese Publikation zitieren

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title = "Exploring the Impact of Data Quality and Availability on PV Power Plant Yield Prediction Using Machine Learning and Analytical Models",

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keywords = "Photovoltaics, Machine Learning, Yield, Forecast, sparse data",

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TY - CONF

T1 - Exploring the Impact of Data Quality and Availability on PV Power Plant Yield Prediction Using Machine Learning and Analytical Models

AU - Prüller, Alexander

AU - Kubicek, Bernhard

AU - Gröschl, Martin

A2 - Rennhofer, Marcus

PY - 2024/9/25

Y1 - 2024/9/25

N2 - While using machine learning models for predictions of PV yieldis increasing in popularity, attention and awareness of the usedtraining data is becoming essential. This study investigates therobustness of photovoltaic (PV) power plant yield predictionsunder diverse data scenarios

AB - While using machine learning models for predictions of PV yieldis increasing in popularity, attention and awareness of the usedtraining data is becoming essential. This study investigates therobustness of photovoltaic (PV) power plant yield predictionsunder diverse data scenarios

KW - Photovoltaics

KW - Machine Learning

KW - Yield

KW - Forecast

KW - sparse data

M3 - Poster presentation without proceedings

ER -