Automated Feature Engineering, AutoML, and Decision-Focused Learning for Improved Energy Consumption Forecasting

The thesis presents a domain‑specific automated feature engineering pipeline, AutoEnergy, for energy consumption forecasting. AutoEnergy generates interpretable features from timestamps and lagged consumption, integrates with AutoML, and achieves 19.52%–84.72% error reductions across eighteen real‑world datasets while running 1.31–4.41 times faster than baseline methods. When combined with Decision‑Focused Learning for battery storage, it cuts operating costs by 22.9%–56.5% on a UK property dataset.

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