2017
DOI: 10.1016/j.rser.2016.06.001
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Models for forecasting growth trends in renewable energy

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Cited by 182 publications
(101 citation statements)
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“…Artificial Neural Network (ANN) has been used to predict the fluctuation of renewable energy productions, such as solar (Almaktar et al, 2015) and the wind (Ramasamy et al, 2015). Other common learning algorithms are Extreme Learning Machine (Golestaneh et al, 2016) and gray prediction model (Tsai et al, 2017). For broader applications, forecasting analysis typically uses social and economic data when integrating into optimization and energy system analysis.…”
Section: Bottom-up Energy Modelingmentioning
confidence: 99%
“…Artificial Neural Network (ANN) has been used to predict the fluctuation of renewable energy productions, such as solar (Almaktar et al, 2015) and the wind (Ramasamy et al, 2015). Other common learning algorithms are Extreme Learning Machine (Golestaneh et al, 2016) and gray prediction model (Tsai et al, 2017). For broader applications, forecasting analysis typically uses social and economic data when integrating into optimization and energy system analysis.…”
Section: Bottom-up Energy Modelingmentioning
confidence: 99%
“…With the classification of different areas, managers can use limited resources in the most efficient manner and clarify priorities for improvement, in order to improve performance and satisfaction [14][15][16][17][18][19][20] .…”
Section: Methodsmentioning
confidence: 99%
“…The four quadrants of IPA have their respective definitions [14][15][16][17][18][19] : (1) Keep up the good work: It means that customers attach great importance to this area and feel satisfied with the performance of an enterprise.…”
Section: Economics and Management Innovations(icemi)mentioning
confidence: 99%