Hybrid approaches based on deep whole-sky-image learning to photovoltaic generation forecasting

2020 ◽  
Vol 280 ◽  
pp. 115875
Author(s):  
Weicong Kong ◽  
Youwei Jia ◽  
Zhao Yang Dong ◽  
Ke Meng ◽  
Songjian Chai
2016 ◽  
Vol 136 (3) ◽  
pp. 268-274 ◽  
Author(s):  
Akiko Takahashi ◽  
Tomohisa Makino ◽  
Jun Imai ◽  
Shigeyuki Funabiki

2016 ◽  
Vol 136 (7) ◽  
pp. 621-627
Author(s):  
Akiko Takahashi ◽  
Akihiro Yamagata ◽  
Jun Imai ◽  
Shigeyuki Funabiki

2020 ◽  
Author(s):  
James McDonagh ◽  
William Swope ◽  
Richard L. Anderson ◽  
Michael Johnston ◽  
David J. Bray

Digitization offers significant opportunities for the formulated product industry to transform the way it works and develop new methods of business. R&D is one area of operation that is challenging to take advantage of these technologies due to its high level of domain specialisation and creativity but the benefits could be significant. Recent developments of base level technologies such as artificial intelligence (AI)/machine learning (ML), robotics and high performance computing (HPC), to name a few, present disruptive and transformative technologies which could offer new insights, discovery methods and enhanced chemical control when combined in a digital ecosystem of connectivity, distributive services and decentralisation. At the fundamental level, research in these technologies has shown that new physical and chemical insights can be gained, which in turn can augment experimental R&D approaches through physics-based chemical simulation, data driven models and hybrid approaches. In all of these cases, high quality data is required to build and validate models in addition to the skills and expertise to exploit such methods. In this article we give an overview of some of the digital technology demonstrators we have developed for formulated product R&D. We discuss the challenges in building and deploying these demonstrators.<br>


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