Abstract

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.

Notes

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Graduation Date

2021

Semester

Fall

Advisor

Rabelo, Luis

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Industrial Engineering and Management Systems

Degree Program

Industrial Engineering

Format

application/pdf

Identifier

CFE0008854; DP0026133

URL

https://purls.library.ucf.edu/go/DP0026133

Language

English

Release Date

12-15-2022

Length of Campus-only Access

1 year

Access Status

Doctoral Dissertation (Open Access)

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