Predicting solar generation from weather forecasts using machine learning
By Navin Sharma, Pranshu Sharma, David E. Irwin, Prashant J. Shenoy. IEEE International Conference on Smart Grid Communications, 2011
452 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/1b463b36d1bbccc419954cc28bd28b0108c2ca63
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yoshua Bengio
- Sergey Levine
- Demis Hassabis
- Alexandre Lacoste
- Li Deng
- Natasha Jaques
- Konrad Paul Kording
- Jianfeng Gao
In Electrical and Electronic Engineering
- Demis Hassabis
- Alexandra Sasha Luccioni
- Lynn H. Kaack
- Adam Wierman
- Luigi Glielmo
- Yoshua Bengio
- Alessandra Parisio
- Evangelos Rikos
Citations in the corpus
- Tackling Climate Change with Machine Learning (2019, methodology): “[40] Navin Sharma, Pranshu Sharma, David Irwin, and Prashant Shenoy.”
- A Model Predictive Control Approach to Microgrid Operation Optimization (2014, methodology): “The latter is a powerful statistical method aiming at capturing the underlying structure in a data set based on input training data [46], [47]; recently, the SVM technique is being successfully applied to demand forecasting and for predicting renewable generation [48], [49].”
- Renewable and cooling aware workload management for sustainable data centers (2012, methodology): “The work in [38] considered the impact of the weather conditions explicitly and used an SVM classifier in conjunction with a RBF kernel to predict solar irradiation.”