Development of a Low-Cost Data Acquisition System for Very Short-Term Photovoltaic Power Forecasting

Guilherme Fonseca Bassous, Rodrigo Flora Calili and Carlos Hall Barbosa
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Guilherme Fonseca Bassous: Graduate Programme in Metrology, Pontifical Catholic University of Rio de Janeiro—PUC-Rio, Rio de Janeiro 22451-900, Brazil
Rodrigo Flora Calili: Graduate Programme in Metrology, Pontifical Catholic University of Rio de Janeiro—PUC-Rio, Rio de Janeiro 22451-900, Brazil
Carlos Hall Barbosa: Graduate Programme in Metrology, Pontifical Catholic University of Rio de Janeiro—PUC-Rio, Rio de Janeiro 22451-900, Brazil

Energies, 2021, vol. 14, issue 19, 1-28

Abstract: The rising adoption of renewable energy sources means we must turn our eyes to limitations in traditional energy systems. Intermittency, if left unaddressed, may lead to several power-quality and energy-efficiency issues. The objective of this work is to develop a working tool to support photovoltaic energy forecast models for real-time operation applications. The current paradigm of intra-hour solar-power forecasting is to use image-based approaches to predict the state of cloud composition for short time horizons. Since the objective of intra-minute forecasting is to address high-frequency intermittency, data must provide information on and surrounding these events. For that purpose, acquisition by exception was chosen as the guiding principle. The system performs power measurements at 1 Hz frequency, and whenever it detects variations over a certain threshold, it saves the data 10 s before and 4 s after the detection point. A multilayer perceptron neural network was used to determine its relevance to the forecasting problem. With a thorough selection of attributes and network structures, the results show very low error with R 2 greater than 0.93 for both input variables tested with a time horizon of 60 s. In conclusion, the data provided by the acquisition system yielded relevant information for forecasts up to 60 s ahead.

Keywords: solar energy; neural networks; sky-camera; forecasting; renewable energy; energy quality; multilayer perceptron; computer vision; short-term forecasting; metrology (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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