Jie Han

ORCID Identifier(s)


Graduation Semester and Year




Document Type


Degree Name

Doctor of Philosophy in Industrial Engineering


Industrial and Manufacturing Systems Engineering

First Advisor

Shouyi Wang


Electricity price forecasting (EPF) is a crucial task for market participants seeking informed decisions in day-ahead electricity markets. The increasing penetration of stochastic renewable energy and the deregulation of electricity markets pose challenges to electricity price forecasting. Given the dependence of electricity prices on stochastic factors such as weather conditions, market dynamics, and customer behaviors, deterministic forecasting methods offer limited insight into the potential future states of energy prices in highly stochastic markets. In this study, a transformer-based electricity price forecasting (TDEPF) model was developed, utilizing a two-step training process and demonstrating superior performance compared to typical RNN models. Subsequently, three probabilistic forecasting models were introduced: quantile regression (QR-Transformer), transformer-based composite quantile regression (TCQR), and Gaussian Process combined with transformer (GP-Transformer). These models can produce multi-step ahead forecasts using multivariate time series as inputs. The application of these models was demonstrated in the context of day-ahead electricity price forecasting, utilizing five years of data from the Electric Reliability Council of Texas (ERCOT). Among the probabilistic models, TCQR emerged as the most effective, as evidenced by metrics such as CRPS, pinball loss, and Winkler score. To evaluate the accuracy of peak time prediction, a novel metric called Winkler-peak score was introduced. Additionally, to regulate the convergence of quantiles near the 0% or 100% level, MSE-regularized pinball loss was proposed. Simultaneously, to prioritize peak time prediction, Winkler-peak score regularized pinball loss was proposed. Both regularization methods on pinball loss were demonstrated to effectively achieve their respective goals when applied in TCQR. Given that deep learning methods operate as black boxes and lack a guarantee of coverage rate, a deeper investigation into uncertainty quantification was undertaken on probabilistic forecasting results. Conformal prediction has been established as capable of constructing prediction intervals with statistically guaranteed coverage rates. However, existing research has not explored applications in either multivariate time series forecasting or multi-step ahead forecasting. Hence, in this study, adaptive quantile random forest (AQRF) and adaptive conformal residual fitting (ACRF) were introduced. Both methods can attain the target coverage rate, and AQRF, in particular, can achieve a narrower bandwidth compared to benchmark models such as conformal prediction and quantile random forest methods. In conclusion, this research can furnish reliable day-ahead electricity price forecasts, aiding decision-makers in making informed decisions within the electricity market.


Probabilistic forecasting, Multivariate time series, Conformal prediction, Deep learning


Engineering | Operations Research, Systems Engineering and Industrial Engineering


Degree granted by The University of Texas at Arlington

Available for download on Saturday, February 01, 2025