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Three notebooks_solar_energy_forecast_four_cities_Amazonas_state

Authors: Marques, Andre;

Three notebooks_solar_energy_forecast_four_cities_Amazonas_state

Abstract

These three notebooks deals with: a) Data Exploratory Analysis (DEA) of the input dataset, which was obtained from NASA Satellites, covering 2013 - nov2022. b) Machine Learning Algorithms, without explicit time dependence, selected to predict the solar irradiance (all sky - short wave). c) Vector Autoregression (VAR), Time Series, analysis, associated to statistics tests: ADF, Granger, Durbin-Watson, Johansen, used to predict the solar irradiance (all sky - short wave).

specific instructions to set the notebooks to run the specific cases are available within the notebook instructions.

Related Organizations
Keywords

Solar Energy; Machine Learning; Amazon Basin; Time Series; Mean Absolute Error; Gradient Boosting; Random Forest.

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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