Big data analytic techniques: predicting renewable energy capacity to facilitate the optimization of power plant energy trading and control algorithms




Tannahill, Barnabas K.

Journal Title

Journal ISSN

Volume Title



Large data has been accumulating in all aspects of our lives for quite some time. Advances in sensor technology, the Internet, wireless communication, and inexpensive memory have all contributed to an explosion of "Big Data". System of Systems (SoS) integrate independently operating, non-homogeneous systems to achieve a higher goal than the sum of the parts. Today's SoS are also contributing to the existence of unmanageable "Big Data". Recent efforts have developed a promising approach, called "Data Analytics", which uses statistical and computational intelligence (CI) tools such as principal component analysis (PCA), clustering, fuzzy logic, neuro-computing, evolutionary computation (such as genetic algorithms), Bayesian networks, etc. to reduce the size of "Big Data" to a manageable size and apply these tools to a) extract information, b) build a knowledge base using the derived data, and c) eventually develop a non-parametric model for the "Big Data". This thesis demonstrates how to construct a bridge between SoS and Data Analytics to develop reliable models for such systems. This thesis uses data analytics to generate models to forecast produced photovoltaic and wind energy to assist in the optimization of a micro grid SoS. Tools like fuzzy interference, neural networks, PCA, and genetic algorithms are used.


This item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.


Analytics, Big Data, Fuzzy Inference System, Microgrid, Neural Network, Renewable Energy



Electrical and Computer Engineering