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.

With advances in solar energy research and increasingly accurate forecast techniques, intermittency no longer stands as a barrier to the adoption of solar energy. Coupling reliable data and knowledge on the inherent variability of the solar resource with advanced learning and forecast models, renewable energy can take an even bigger role in today’s energy paradigm. The objective of this work is to develop and test a low-cost data acquisition system able to provide relevant data for solar energy forecast models. The results yielded from the performed tests indicate high correlation between image derived attributes and power measurements 20 s ahead.

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