A nonparametric Bayesian model for prediction of solar power generation
Abstract
In the last few years, there happened a significant increasing of solar energy generation so as by photovoltaics plants as by residences with board photovoltaic instaled on the roofs. This is mainly due to intereset of the society and governments for clean and renewable energies that reduces the amount of C02 emission. Do to this, to each day more photovoltaic plants are being connected to the electrical systems of the cities. However, according [1] and [2], this may causes instability to the grid, making it the greatest challenge to the industry. [...]
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References
M. AlKandari e I. Ahmad. “Solar power generation forecasting using ensemble approach based on deep learning and statistical methods”. Em: Applied Computing and Informatics (2020), pp. 1–20. doi: 10.1016/j.aci.2019.11.002.
H. Sharadga, S. Hajimirza e R. S. Balog. “Time series forecasting of solar power generation for large-scale photovoltaic plants”. Em: Renewable Energy 150 (2020), pp. 797–807. doi: 10.1016/j.renene.2019.12.131.
