The growing digital economy has imposed greater demand on the electricity supply's reliability in the past decades, with more consumers and electric vehicles (EV) becoming connected to the electric grid. While the uncertainty of the outcomes is unavoidable, there is a need for more accurate forecasts, modeling tools, and detailed roadmaps that can support the reliable transitioning of power systems. Predicting the electricity demand growth will allow energy managers to understand consumer demand in the near future better. However, there are challenges for forecasting the peak demand growth since it is very difficult to model the various complex features that affect it (i.e., weather patterns, economic growth, etc.). This dissertation contributes to the body of knowledge by proposing an integrated forecasting framework that can help decision-makers deal with the day-to-day outcomes while closely monitoring the monthly peak demand growth. This data-driven framework brings together recent trends in the machine learning and system dynamics field to support forecasting. This hybrid modeling could help deliver more accurate forecasts that will help decision-makers test and adjust their strategies according to critical changes in supply and demand. An actual case study on the Republic of Panama was used to understand the challenges for managing large-scale power systems and the recent impacts that the COVID-19 pandemic had on the energy sector. The case study shows the potential to leverage technological trends (big data, internet of things) and state of the methods such as Convolutional Neural Networks for predicting electricity demand.
If this is your thesis or dissertation, and want to learn how to access it or for more information about readership statistics, contact us at STARS@ucf.edu.
Doctor of Philosophy (Ph.D.)
College of Engineering and Computer Science
Industrial Engineering and Management Systems
Length of Campus-only Access
Doctoral Dissertation (Open Access)
Ibrahim, Bibi, "A Holistic Approach for Power Systems Using Machine Learning and System Dynamics" (2021). Electronic Theses and Dissertations, 2020-. 883.