Bampoulas, Adamantios and Pallonetto, Fabiano and Mangina, Eleni and Finn, Donal P. (2023) A Bayesian deep-learning framework for assessing the energy flexibility of residential buildings with multicomponent energy systems. Applied Energy, 348 (121576). pp. 1-25. ISSN 0306-2619

Preview

Download (6MB) | Preview

Abstract

This paper addresses the challenge of assessing uncertainty in energy flexibility predictions, which is a significant open question in the energy flexibility assessment field. To address this challenge, a methodology that quantifies the flexibility of multiple thermal and electrical systems is developed using appropriate indicators and considers the different types of uncertainty associated with building energy use. A Bayesian convolutional neural network is developed to capture aleatoric and epistemic uncertainty related to energy conversion device operation and temperature deviations resulting from exploiting building flexibility. The developed prediction models utilise residential occupancy patterns and a sliding window technique and are periodically updated. The energy systems evaluated include a heat pump, a photovoltaic system, and a stationary battery, and use synthetic datasets obtained from a calibrated physics-based model of an all-electric residential building for two occupancy profiles. Simulation results indicate that building flexibility potential predictability is influenced by weather conditions and/or occupant behaviour. Furthermore, the day-ahead and hour-ahead prediction models show excellent performance for both occupancy profiles, achieving coefficients of determination between 0.93 and 0.99. This methodology can enable electricity aggregators to evaluate building portfolios, considering uncertainty and multi-step predictions, to shift electricity demand to off-peak periods or periods of excess onsite renewable electricity generation in an end-user-customised manner.

Item Type:	Article
Keywords:	Energy flexibility; Flexibility indicators; Residential sector; Bayesian deep-learning; Probabilistic forecasting;
Academic Unit:	Faculty of Science and Engineering > Research Institutes > Hamilton Institute Faculty of Social Sciences > Research Institutes > Innovation Value Institute, IVI Faculty of Social Sciences > School of Business
Item ID:	18918
Identification Number:	https://doi.org/10.1016/j.apenergy.2023.121576
Depositing User:	Fabiano Pallonetto
Date Deposited:	24 Sep 2024 10:50
Journal or Publication Title:	Applied Energy
Publisher:	Elsevier
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

Repository Staff Only(login required)

Item control page

Downloads

Origin of downloads