Imperial College London
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Dr Salvador Acha

Faculty of Engineering, Department of Chemical Engineering

Senior Research Fellow
 
 
 
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Contact

 

+44 (0)20 7594 3379salvador.acha Website CV

 
 
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Location

 

453AACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Acha:2019:10.1109/IAS.2019.8912410,
author = {Acha, Izquierdo S and Le, Brun N and Shah, N and Bird, M},
doi = {10.1109/IAS.2019.8912410},
publisher = {IEEE},
title = {Assessing the modelling approach and datasets required for fault detection in photovoltaic systems},
url = {http://dx.doi.org/10.1109/IAS.2019.8912410},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Reliable monitoring for photovoltaic assets (PVs) is essential to ensuring uptake, long term performance, and maximum return on investment of renewable systems. To this end this paper investigates the input data and machine learning techniques required for day-behind predictions of PV generation, within the scope of conducting informed maintenance of these systems. Five years of PV generation data at hourly intervals were retrieved from four commercial building-mounted PV installations in the UK, as well as weather data retrieved from MIDAS. A support vector machine, random forest and artificial neural network were trained to predict PV power generation. Random forest performed best, achieving an average mean relative error of 2.7%. Irradiance, previous generation and solar position were found to be the most important variables. Overall, this work shows how low-cost data driven analysis of PV systems can be used to support the effective management of such assets.
AU  - Acha,Izquierdo S
AU  - Le,Brun N
AU  - Shah,N
AU  - Bird,M
DO  - 10.1109/IAS.2019.8912410
PB  - IEEE
PY  - 2019///
TI  - Assessing the modelling approach and datasets required for fault detection in photovoltaic systems
UR  - http://dx.doi.org/10.1109/IAS.2019.8912410
UR  - http://hdl.handle.net/10044/1/78407
ER  -
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