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

Energies

Guilherme Fonseca Bassous¹  Carlos Hall Barbosa¹  Rodrigo Flora Calili¹ 

[1] Graduate Programme in Metrology, Pontifical Catholic University of Rio de Janeiro—PUC-Rio, Rio de Janeiro 22451-900, Brazil;

关键词: solar energy;    neural networks;    sky-camera;    forecasting;    renewable energy;    energy quality;   

DOI  :  10.3390/en14196075

来源: DOAJ

【 摘 要 】

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 R² 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.

【 授权许可】

Unknown