Optimal Planning Strategy for Large PV/Battery System Based on Long-Term Insolation Forecasting

2011 ◽  
Vol 131 (10) ◽  
pp. 1665-1671
Author(s):  
Atsushi Yona ◽  
Kosuke Uchida ◽  
Tomonobu Senjyu ◽  
Toshihisa Funabashi
2017 ◽  
Author(s):  
Falk Lieder ◽  
Paul M. Krueger ◽  
Frederick Callaway ◽  
Tom Griffiths

We present an intelligent tutoring system for teaching people effective planning strategies. Our training program combines a novel process-tracing paradigm that makes people’s latent planning strategies observable with an AI systems that gives people immediate feedback on how close their planning strategy is to optimal planning. Three experiments demonstrate that our method can automatically discover which planning strategies is optimal for a given class of problems and teach it to people. We find that the metacognitive process feedback provided by our method accelerates learning compared to no-feedback and conventional feedback on the quality of the selected actions, and the training effects are retained after a break even when the feedback is removed.


Author(s):  
Yangyang Liu ◽  
Jiangxin Zhou ◽  
Guangli Wang ◽  
Shihua Zhao ◽  
Feng Yu

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