Development of a Low-Cost Data Acquisition System for Very Short-Term Photovoltaic Power Forecasting
| Autor: | Carlos Hall Barbosa, Guilherme Fonseca Bassous, Rodrigo Flora Calili |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Technology
Control and Optimization Computer science solar energy Energy Engineering and Power Technology forecasting Time horizon Cloud computing computer vision law.invention law Intermittency short-term forecasting energy quality multilayer perceptron Electrical and Electronic Engineering Engineering (miscellaneous) Artificial neural network Renewable Energy Sustainability and the Environment business.industry Photovoltaic system neural networks renewable energy sky-camera Reliability engineering Term (time) metrology Multilayer perceptron business Energy (signal processing) Energy (miscellaneous) |
| Zdroj: | Energies Volume 14 Issue 19 Energies, Vol 14, Iss 6075, p 6075 (2021) |
| ISSN: | 1996-1073 |
| DOI: | 10.3390/en14196075 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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