Demand Response Potential Forecasting: A Systematic Review of Methods, Challenges, and Future Directions

Ali Muqtadir (), Bin Li, Bing Qi, Leyi Ge, Nianjiang Du and Chen Lin
Additional contact information
Ali Muqtadir: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Bin Li: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Bing Qi: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Leyi Ge: State Grid Zhilian E-Commerce Company Limited, Beijing 100053, China
Nianjiang Du: Yantai Power Supply Company, State Grid Shandong Electric Power Company, Yantai 264001, China
Chen Lin: Longyan Branch, State Grid Fujian Electric Power Company Limited, Longyan 364000, China

Energies, 2025, vol. 18, issue 19, 1-37

Abstract: Demand response (DR) is increasingly recognized as a critical flexibility resource for modernizing power systems, enabling the large-scale integration of renewable energy and enhancing grid stability. While the field of general electricity load forecasting is supported by numerous systematic reviews, the specific subfield of DR potential forecasting has received comparatively less synthesized attention. This gap leaves a fragmented understanding of modeling techniques, practical implementation challenges, and future research problems for a function that is essential for market participation. To address this, this paper presents a PRISMA-2020-compliant systematic review of 172 studies to comprehensively analyze the state-of-the-art in DR potential estimation. We categorize and evaluate the evolution of forecasting methodologies, from foundational statistical models to advanced AI architectures. Furthermore, the study identifies key technological enablers and systematically maps the persistent technical, regulatory, and behavioral barriers that impede widespread DR deployment. Our analysis demonstrates a clear trend towards hybrid and ensemble models, which outperform standalone approaches by integrating the strengths of diverse techniques to capture complex, nonlinear consumer dynamics. The findings underscore that while technologies like Advanced Metering Infrastructure (AMI) and the Internet of Things (IoT) are critical enablers, the gap between theoretical potential and realized flexibility is primarily dictated by non-technical factors, including inaccurate baseline methodologies, restrictive market designs, and low consumer engagement. This synthesis brings much-needed structure to a fragmented research area, evaluating the current state of forecasting methods and identifying the critical research directions required to improve the operational effectiveness of DR programs.

Keywords: deep learning; demand response (DR) potential; demand-side flexibility; energy markets; machine learning; smart grids (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/19/5217/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/19/5217/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:19:p:5217-:d:1762549

Access Statistics for this article

Energies is currently edited by Ms. Cassie Shen

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().