Solar radiation forecasting based on convolutional neural network and ensemble learning
| Autor: | Edoardo Patti, Enrico Macii, Andrea Acquaviva, Alessandro Aliberti, Davide Cannizzaro, Lorenzo Bottaccioli |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Computer science Energy Forecast 02 engineering and technology computer.software_genre Solar irradiance Convolutional neural network 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Production (economics) Variational Mode Decomposition Renewable Energy business.industry Convolutional Neural Networks Photovoltaic system General Engineering Ensemble learning Solar Radiation Forecast Computer Science Applications Renewable energy Random forest Smart grid 020201 artificial intelligence & image processing Data mining business computer Solar Radiation Forecast Convolutional Neural Networks Variational Mode Decomposition Energy Forecast Renewable Energy |
| Zdroj: | Expert Systems with Applications. 181:115167 |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2021.115167 |
| Popis: | Nowadays, we are moving forward to more sustainable energy production systems based on renewable sources. Among all Photovoltaic (PV) systems are spreading in our cities. In this view, new models are needed to forecast Global Horizontal Solar Irradiance (GHI), which strongly influences PV production. For example, this forecast is crucial to develop novel control strategies for smart grid management. In this paper, we present a novel methodology to forecast GHI in short- and long-term time-horizons, i.e. from next 15 min up to next 24 h. It implements machine learning techniques to achieve this purpose. We start from the analysis of a real-world dataset with different meteorological information including GHI, in the form of time-series. Then, we combined Variational Mode Decomposition (VMD) and two Convolutional Neural Networks (CNN) together with Random Forest (RF) or Long Short Term Memory (LSTM). Finally, we present the experimental results and discuss their accuracy. |
| Databáze: | OpenAIRE |
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