Enhancing energy control stability under extreme conditions by integrating weather forecasts into ARLEM
Nik, Vahid M. LU
(2025)
In Energy and AI
22.
- Abstract
Increasing the stability of energy management systems is crucial, especially when facing extreme weather events. A common approach to prevent sudden oscillations is the implementation of predictive models; however, this often requires more complex models and greater computational power. In this work, weather forecasts are integrated into an approach based on Adaptive Reinforcement Learning for Energy Management (ARLEM). Since ARLEM is an online model-free reward-based RL method, it does not need any form of (predictive) modelling and weather forecasts serve as an additional source of information about the agent's environment. The impacts of incorporating weather forecasts into ARLEM are investigated for a typical urban neighborhood in... (More)
Increasing the stability of energy management systems is crucial, especially when facing extreme weather events. A common approach to prevent sudden oscillations is the implementation of predictive models; however, this often requires more complex models and greater computational power. In this work, weather forecasts are integrated into an approach based on Adaptive Reinforcement Learning for Energy Management (ARLEM). Since ARLEM is an online model-free reward-based RL method, it does not need any form of (predictive) modelling and weather forecasts serve as an additional source of information about the agent's environment. The impacts of incorporating weather forecasts into ARLEM are investigated for a typical urban neighborhood in Stockholm during a winter with two cold waves and considering 17 future climate scenarios for the period of 2040–2069. Multiple adaptive policy schemes are assessed considering four forecast horizons of 3, 6, 12, and 24 h. Results show that integrating weather forecasts into decision-making can significantly reduce fluctuations in the control system, leading to more stable energy management. Moreover, it enhances energy efficiency by reducing both average and peak demands, particularly during extreme weather events. Overall, this contributes to improving the climate resilience of energy systems.
(Less)
- author
- Nik, Vahid M.
LU
- organization
- publishing date
- 2025-12
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Adaptive reinforcement learning, Climate Resileince, Control, Energy management, Stability, Weather forecast
- in
- Energy and AI
- volume
- 22
- article number
- 100617
- publisher
- Elsevier
- external identifiers
-
- scopus:105016464540
- ISSN
- 2666-5468
- DOI
- 10.1016/j.egyai.2025.100617
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2025 The Author(s)
- id
- f28c985c-6ebd-4fe7-b1ad-42adf83270fc
- date added to LUP
- 2025-10-10 11:11:02
- date last changed
- 2025-10-13 09:57:29
@article{f28c985c-6ebd-4fe7-b1ad-42adf83270fc,
abstract = {{<p>Increasing the stability of energy management systems is crucial, especially when facing extreme weather events. A common approach to prevent sudden oscillations is the implementation of predictive models; however, this often requires more complex models and greater computational power. In this work, weather forecasts are integrated into an approach based on Adaptive Reinforcement Learning for Energy Management (ARLEM). Since ARLEM is an online model-free reward-based RL method, it does not need any form of (predictive) modelling and weather forecasts serve as an additional source of information about the agent's environment. The impacts of incorporating weather forecasts into ARLEM are investigated for a typical urban neighborhood in Stockholm during a winter with two cold waves and considering 17 future climate scenarios for the period of 2040–2069. Multiple adaptive policy schemes are assessed considering four forecast horizons of 3, 6, 12, and 24 h. Results show that integrating weather forecasts into decision-making can significantly reduce fluctuations in the control system, leading to more stable energy management. Moreover, it enhances energy efficiency by reducing both average and peak demands, particularly during extreme weather events. Overall, this contributes to improving the climate resilience of energy systems.</p>}},
author = {{Nik, Vahid M.}},
issn = {{2666-5468}},
keywords = {{Adaptive reinforcement learning; Climate Resileince; Control; Energy management; Stability; Weather forecast}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Energy and AI}},
title = {{Enhancing energy control stability under extreme conditions by integrating weather forecasts into ARLEM}},
url = {{http://dx.doi.org/10.1016/j.egyai.2025.100617}},
doi = {{10.1016/j.egyai.2025.100617}},
volume = {{22}},
year = {{2025}},
}