Quantile regression based probabilistic forecasting of renewable energy generation and building electrical load : A state of the art review

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

6 Scopus Citations
View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number107772
Journal / PublicationJournal of Building Engineering
Volume79
Online published11 Sept 2023
Publication statusPublished - 15 Nov 2023

Abstract

With the increasing penetration of renewable energy in smart grids and the increasing building electrical load, their accurate forecasting is essential for system design, control and associated optimizations. To date, probabilistic forecasting methods have attracted increasing attentions as they can assess various uncertainty impacts. Among them, quantile regression based probabilistic forecasting methods are more popular and experience fast developments. However, there is little review that systematically covers their similarities and differences in the aspects of mechanism, feature and effectiveness in applications. This paper, therefore, provides a comprehensive review of quantile regression-related methods for renewable energy generation and building electrical load. Firstly, according to their principles/mechanisms, existing quantile regression based probabilistic forecasting methods are classified into two major categories, namely statistic-based methods and machine learning-based methods. Meanwhile, their respective strengths and limitations are comparatively analyzed and summarized. Next, their practical applications and effectiveness are systematically reviewed. On the basis of the above review part, a discussion focusing on the current research gaps and potential research opportunities is presented regarding quantile regression future developments. The timely review can help improve researchers’ understanding and facilitate further improvements of the quantile regression based probabilistic forecasting methods. © 2023 Elsevier Ltd

Research Area(s)

  • Building energy demand, Machine learning, Probabilistic forecast, Quantile regression, Renewable energy

Citation Format(s)