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Articles 1 - 6 of 6
Full-Text Articles in Physical Sciences and Mathematics
Machine Learning Predictions Of Electricity Capacity, Marcus Harris, Elizabeth Kirby, Ameeta Agrawal, Rhitabrat Pokharel, Francis Puyleart, Martin Zwick
Machine Learning Predictions Of Electricity Capacity, Marcus Harris, Elizabeth Kirby, Ameeta Agrawal, Rhitabrat Pokharel, Francis Puyleart, Martin Zwick
Systems Science Faculty Publications and Presentations
This research applies machine learning methods to build predictive models of Net Load Imbalance for the Resource Sufficiency Flexible Ramping Requirement in the Western Energy Imbalance Market. Several methods are used in this research, including Reconstructability Analysis, developed in the systems community, and more well-known methods such as Bayesian Networks, Support Vector Regression, and Neural Networks. The aims of the research are to identify predictive variables and obtain a new stand-alone model that improves prediction accuracy and reduces the INC (ability to increase generation) and DEC (ability to decrease generation) Resource Sufficiency Requirements for Western Energy Imbalance Market participants. This …
Data From: Machine Learning Predictions Of Electricity Capacity, Marcus Harris, Elizabeth Kirby, Ameeta Agrawal, Rhitabrat Pokharel, Francis Puyleart, Martin Zwick
Data From: Machine Learning Predictions Of Electricity Capacity, Marcus Harris, Elizabeth Kirby, Ameeta Agrawal, Rhitabrat Pokharel, Francis Puyleart, Martin Zwick
Systems Science Faculty Datasets
This research applies machine learning methods to build predictive models of Net Load Imbalance for the Resource Sufficiency Flexible Ramping Requirement in the Western Energy Imbalance Market. Several methods are used in this research, including Reconstructability Analysis, developed in the systems community, and more well-known methods such as Bayesian Networks, Support Vector Regression, and Neural Networks. The aims of the research are to identify predictive variables and obtain a new stand-alone model that improves prediction accuracy and reduces the INC (ability to increase generation) and DEC (ability to decrease generation) Resource Sufficiency Requirements for Western Energy Imbalance Market participants. This …
Reconstructability Analysis: Discrete Multivariate Modeling, Martin Zwick
Reconstructability Analysis: Discrete Multivariate Modeling, Martin Zwick
Systems Science Faculty Publications and Presentations
An introduction to Reconstructability Analysis for the Discrete Multivariate Modeling course and for other purposes.
Using Information Theory To Extract Patterns From Categorical Raster Data, David Percy
Using Information Theory To Extract Patterns From Categorical Raster Data, David Percy
Systems Science Faculty Publications and Presentations
Information theory -- Reconstructability Analysis (RA) implemented in the Occam software -- was used to extract patterns from National Land Cover Data. The aim was to predict temporal change in evergreen forests from time-lagged and spatially adjacent states. The NLCD satellite data were preprocessed with Python and submitted to Occam for analysis, and Occam output was also explored with R-studio. The effectiveness of RA methodology for the analysis of this type of categorical space-time grid data was demonstrated.
Keyword-Based Patent Citation Prediction Via Information Theory, Farshad Madani, Martin Zwick, Tugrul U. Daim
Keyword-Based Patent Citation Prediction Via Information Theory, Farshad Madani, Martin Zwick, Tugrul U. Daim
Engineering and Technology Management Faculty Publications and Presentations
Patent citation shows how a technology impacts other inventions, so the number of patent citations (backward citations) is used in many technology prediction studies. Current prediction methods use patent citations, but since it may take a long time till a patent is cited by other inventors, identifying impactful patents based on their citations is not an effective way. The prediction method offered in this article predicts patent citations based on the content of patents. In this research, Reconstructability Analysis (RA), which is based on information theory and graph theory, is applied to predict patent citations based on keywords extracted from …
Binary Decision Diagrams And Crisp Possibilistic Reconstructability Analysis, Martin Zwick, Alan Mishchenko
Binary Decision Diagrams And Crisp Possibilistic Reconstructability Analysis, Martin Zwick, Alan Mishchenko
Systems Science Faculty Publications and Presentations
The paper discusses the application of Binary Decision Diagrams (BDDs) in the reconstructability analysis of crisp possibilistic systems. In particular, we show how BDDs can be used to represent set-theoretic relations and implement the three basic operations of reconstructability analysis.