DEEP ENSEMBLES APPROACH FOR ENERGY FORECASTING

Date

2023-9-5

Author

Enginar, Onur

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In this thesis study, we develop a novel deep ensembles based architecture that en- ables transfer learning to reduce the time requirement of deep ensembles without compromising the model’s accuracy. We apply our model to open energy datasets. Moreover, this thesis compares SoTA tabular learning models with deep ensembles and traditional machine learning models and provides a benchmark for the literature. We further develop a feature selection algorithm based on boosted deep ensembles model and compare it with linear feature selection models and tree-based feature se- lection algorithms.

Subject Keywords

Deep Learning, Energy Forecasting, Tabular Learning, Feature Selection

URI

https://hdl.handle.net/11511/105623

Collections

Graduate School of Applied Mathematics, Thesis