Thermal Monitoring of Photovoltaic module using Optical Fiber Sensors

Santolin, Edson Antonio; Lourenço, Ivo de; Corte, Vinícius Dalla; Silva, Jean Carlos Cardozo da; Oliveira, Valmir de

doi:10.1590/2179-10742016v15i4710

Cited by 4 publications

(1 citation statement)

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“…In [34], the authors propose monitoring the PV module by attaching it to the cover glass of an optical fiber sensor for measuring the temperature. This can be further extended to attaching this type of sensor at the photovoltaic cell level.…”

Section: Literature Reviewmentioning

confidence: 99%

PV Forecasting Using Support Vector Machine Learning in a Big Data Analytics Context

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et al. 2018

Symmetry

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Renewable energy systems (RES) are reliable by nature; the sun and wind are theoretically endless resources. From the beginnings of the power systems, the concern was to know “how much” energy will be generated. Initially, there were voltmeters and power meters; nowadays, there are much more advanced solar controllers, with small displays and built-in modules that handle big data. Usually, large photovoltaic (PV)-battery systems have sophisticated energy management strategies in order to operate unattended. By adding the information collected by sensors managed with powerful technologies such as big data and analytics, the system is able to efficiently react to environmental factors and respond to consumers’ requirements in real time. According to the weather parameters, the output of PV could be symmetric, supplying an asymmetric electricity demand. Thus, a smart adaptive switching module that includes a forecasting component is proposed to improve the symmetry between the PV output and daily load curve. A scaling approach for smaller off-grid systems that provides an accurate forecast of the PV output based on data collected from sensors is developed. The proposed methodology is based on sensor implementation in RES operation and big data technologies are considered for data processing and analytics. In this respect, we analyze data captured from loggers and forecast the PV output with Support Vector Machine (SVM) and linear regression, finding that Root Mean Square Error (RMSE) for prediction is considerably improved when using more parameters in the machine learning process.

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Section: Literature Reviewmentioning

confidence: 99%