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A Review for Green Energy Machine Learning and AI Services
by
Xu, Rui
, Wu, Jane
, Gao, Jerry
, Mehta, Yukta
, Lim, Benjamin
in
Accuracy
/ Algorithms
/ Alternative energy sources
/ Artificial intelligence
/ Costs
/ Data mining
/ Electricity
/ Electricity distribution
/ Energy consumption
/ Energy industry
/ Energy management
/ Energy resources
/ Energy storage
/ energy usage
/ Forecasting
/ Forecasting techniques
/ green AI services
/ Green technology
/ Humidity
/ load forecasting
/ load profiling
/ Machine learning
/ price forecasting
/ Renewable resources
/ smart-grid
/ Solar energy
/ Taxonomy
/ Wind power
2023
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A Review for Green Energy Machine Learning and AI Services
by
Xu, Rui
, Wu, Jane
, Gao, Jerry
, Mehta, Yukta
, Lim, Benjamin
in
Accuracy
/ Algorithms
/ Alternative energy sources
/ Artificial intelligence
/ Costs
/ Data mining
/ Electricity
/ Electricity distribution
/ Energy consumption
/ Energy industry
/ Energy management
/ Energy resources
/ Energy storage
/ energy usage
/ Forecasting
/ Forecasting techniques
/ green AI services
/ Green technology
/ Humidity
/ load forecasting
/ load profiling
/ Machine learning
/ price forecasting
/ Renewable resources
/ smart-grid
/ Solar energy
/ Taxonomy
/ Wind power
2023
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Do you wish to request the book?
A Review for Green Energy Machine Learning and AI Services
by
Xu, Rui
, Wu, Jane
, Gao, Jerry
, Mehta, Yukta
, Lim, Benjamin
in
Accuracy
/ Algorithms
/ Alternative energy sources
/ Artificial intelligence
/ Costs
/ Data mining
/ Electricity
/ Electricity distribution
/ Energy consumption
/ Energy industry
/ Energy management
/ Energy resources
/ Energy storage
/ energy usage
/ Forecasting
/ Forecasting techniques
/ green AI services
/ Green technology
/ Humidity
/ load forecasting
/ load profiling
/ Machine learning
/ price forecasting
/ Renewable resources
/ smart-grid
/ Solar energy
/ Taxonomy
/ Wind power
2023
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A Review for Green Energy Machine Learning and AI Services
Journal Article
A Review for Green Energy Machine Learning and AI Services
2023
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Overview
There is a growing demand for Green AI (Artificial Intelligence) technologies in the market and society, as it emerges as a promising technology. Green AI technologies are used to create sustainable solutions and reduce the environmental impact of AI. This paper focuses on describing the services of Green AI and the challenges associated with it at the community level. This article also highlights the accuracy levels of machine learning algorithms for various time periods. The process of choosing the appropriate input parameters for weather, locations, and complexity is outlined in this paper to examine the ML algorithms. For correcting the algorithm performance parameters, metrics like RMSE (root mean square error), MSE (mean square error), MAE (mean absolute error), and MPE (mean percentage error) are considered. Considering the performance and results of this review, the LSTM (long short-term memory) performed well in most cases. This paper concludes that highly advanced techniques have dramatically improved forecasting accuracy. Finally, some guidelines are added for further studies, needs, and challenges. However, there is still a need for more solutions to the challenges, mainly in the area of electricity storage.
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