Ensemble Approach for Short-term Load Forecasting Using Regularized Greedy Forest

MPhil Thesis Defence


Title: "Ensemble Approach for Short-term Load Forecasting Using Regularized 
Greedy Forest"

By

Mr. Wai Keung Binnie YIU


Abstract

Smart grid is being developed to modernize the electricity grid in order to 
increase power quality. Accurate short-term load forecasting (STLF) is crucial 
for improving the reliability and energy efficiency of power utility networks. 
Operational planning decisions depend primarily on load forecasting; 
overestimation leads to wasted energy and costs, whereas underestimation leads 
to energy shortages or even blackouts. However, there is no universal model for 
solving all forecasting problems. This study focuses on the regularized greedy 
forest (RGF) algorithm, which learns a forest by considering the current tree 
structure with regularization. In this work, the RGF model is combined with the 
eXtreme gradient boosting and light gradient boosting machine models, which are 
gradient-boosting frameworks, to form a more robust ensemble model using the 
Bayesian optimization technique. The results show that the proposed ensemble 
model is suitable for STLF problems. It is practical and reliable and can 
provide accurate day-ahead short term hourly load forecasting for Hong Kong. It 
achieves the best performance among the tree-based models and the deep learning 
models in different scenarios.


Date:  			Wednesday, 27 July 2022

Time:			9:00am - 11:00am

Zoom Meeting:
https://hkust.zoom.us/j/97499307673?pwd=bjVFUGl2bUl6akhyOW5rbVdXSVdxZz09

Committee Members:	Prof. Tong Zhang (Supervisor)
 			Dr. Cheuk-Wing Lee (Supervisor, CLP)
 			Dr. Qifeng Chen (Chairperson)
 			Dr. Minhao Cheng


**** ALL are Welcome ****