Probabilistic load forecasting for the low voltage network: forecast fusion and daily peaks

Gilbert, C., Browell, J. and Stephen, B. (2023) Probabilistic load forecasting for the low voltage network: forecast fusion and daily peaks. Sustainable Energy, Grids and Networks, 34, 100998. (doi: 10.1016/j.segan.2023.100998)

Abstract

Short-term forecasts of energy consumption are invaluable for the operation of energy systems, including low voltage electricity networks. However, network loads are challenging to predict when highly desegregated to small numbers of customers, which may be dominated by individual behaviours rather than the smooth profiles associated with aggregate consumption. Furthermore, distribution networks are challenged almost entirely by peak loads, and tasks such as scheduling storage and/or demand flexibility maybe be driven by predicted peak demand, a feature that is often poorly characterised by general-purpose forecasting methods. Here we propose an approach to predict the timing and level of daily peak demand, and a data fusion procedure for combining conventional and peak forecasts to produce a general-purpose probabilistic forecast with improved performance during peaks. The proposed approach is demonstrated using real smart meter data and a hypothetical low voltage network hierarchy comprising feeders, secondary and primary substations. Fusing state-of-the-art probabilistic load forecasts with peak forecasts is found to improve performance overall, particularly at smart-meter and feeder levels and during peak hours, where improvement in terms of CRPS exceeds 10%.

• Item Type: Articles
• Additional Information: The authors were funded by the EPSRC project Analytical Middleware for Informed Distribution Networks (AMIDiNe, EP/S030131/1) and the Innovation Fellowship held by JB (EP/R023484/1 and EP/R023484/2).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Browell, Dr Jethro

Creator Roles

Browell, J.: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Visualization, Writing – review and editing

• Authors: Gilbert, C., Browell, J., and Stephen, B.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Journal Name: Sustainable Energy, Grids and Networks
• Publisher: Elsevier
• ISSN: 2352-4677
• ISSN (Online): 2352-4677
• Published Online: 14 January 2023
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in Sustainable Energy, Grids and Networks 34: 100998
• Publisher Policy: Reproduced under a Creative Commons License
• Data DOI: 10.5281/zenodo.7064279

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
315958		System-wide Probabilistic Energy Forecasting	Jethro Browell	Engineering and Physical Sciences Research Council (EPSRC)	EP/R023484/2	M&S - Statistics