This paper proposes a methodology to evaluate the maximum power, which can be drawn out from a PV plant on the basis of its geographic location and local weather conditions. These evaluations are carried out through data analytics, starting from an experimental knowledge base, which is built through aggregation of data coming from electric characterization tests, temperature measurements and weather stations. The first part of this methodology is focused on the characterization of irradiance sensors, highlighting the dependency of its measurements on sun elevation angle. The evaluations are then focused on a real application, which consists in a Hetero Junction Intrinsic Thin Layer (HIT) PV panel equipped with temperature sensors. The estimations are obtained by using weather data coming from measurement stations located near the PV panel. The comparison with experimental results highlights the performance of the proposed power prediction method, especially during summer periods.

Data Analytics for Performance Modelling of Photovoltaic Systems in the Internet of Energy Scenario

Capasso C.
Writing – Review & Editing
;
Rubino L.
Writing – Review & Editing
;
2021

Abstract

This paper proposes a methodology to evaluate the maximum power, which can be drawn out from a PV plant on the basis of its geographic location and local weather conditions. These evaluations are carried out through data analytics, starting from an experimental knowledge base, which is built through aggregation of data coming from electric characterization tests, temperature measurements and weather stations. The first part of this methodology is focused on the characterization of irradiance sensors, highlighting the dependency of its measurements on sun elevation angle. The evaluations are then focused on a real application, which consists in a Hetero Junction Intrinsic Thin Layer (HIT) PV panel equipped with temperature sensors. The estimations are obtained by using weather data coming from measurement stations located near the PV panel. The comparison with experimental results highlights the performance of the proposed power prediction method, especially during summer periods.
2021
978-1-7281-8071-7
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11591/496629
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