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- W3146044663 abstract "Environmental protection is a highly concerned and thought-provoking issue, and the way of generating electricity has become a major conundrum for all mankind. Renewable or green energy is an ideal solution for environmentally friendly (eco-friendly) power generation. Among all renewable energy, solar-power generation is low cost and small footprint, which make solar-power generation available around our lives. The major uncontrollable factor in the solar-power generation is the amount of solar radiation, which completely dominates the electricity generated by solar panel. In this paper, we design prediction models for solar radiation, using data mining techniques and machine learning algorithms, and derive precision prediction models (PPM) and light prediction models (LPM). Experimental results that the PPM (LPM) with random forest regression can obtain R-squared of 0.841 (0.828) and correlation coefficient of 0.917 (0.910). Compared with highly cited researches, our models outperform them in all measurements, which demonstrates the robustness and effectiveness of the proposed models." @default.
- W3146044663 created "2021-04-13" @default.
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- W3146044663 date "2021-01-01" @default.
- W3146044663 modified "2023-10-18" @default.
- W3146044663 title "Forecasting System for Solar-Power Generation" @default.
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- W3146044663 doi "https://doi.org/10.1007/978-981-16-1685-3_6" @default.
- W3146044663 hasPublicationYear "2021" @default.
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