Graduation Semester and Year
Fall 2025
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Electrical Engineering
Department
Electrical Engineering
First Advisor
Ramtin Madani
Abstract
This dissertation develops interpretable, data-driven frameworks for short-term power demand forecasting using convex optimization and advanced feature engineering. The models combine historical load data, calendar structures, and meteorological variables to deliver accurate point, quantile, and probabilistic forecasts. By leveraging multi-periodic Fourier features, temperature-based regressors, and autoregressive memory, the proposed approach balances predictive performance with interpretability and scalability. Evaluations on multi-year datasets across US regions show consistent accuracy gains over benchmarks, while preserving transparency critical for real-world deployment. Beyond power systems, the framework generalizes to other time series applications in data science and AI, offering a robust, explainable alternative to black-box machine learning models for forecasting and optimization.
Keywords
Data-driven forecasting, Convex optimization, Interpretable machine learning, Probabilistic forecasting, Power demand prediction, Time series modeling, Feature engineering, Renewable energy integration, Artificial intelligence in energy systems, Scalable optimization
Disciplines
Electrical and Computer Engineering
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Recommended Citation
Ashraphijuo, Mersedeh, "Data-Driven Forecasting of Power Demand via Convex Optimization" (2025). Electrical Engineering Dissertations. 412.
https://mavmatrix.uta.edu/electricaleng_dissertations/412