Energy and Water Management in Smart Buildings Using Spiking Neural Networks: A Low-Power, Event-Driven Approach for Adaptive Control and Anomaly Detection

Malek Alrashidi, Sami Mnasri (), Maha Alqabli, Mansoor Alghamdi, Michael Short, Sean Williams, Nashwan Dawood, Ibrahim S. Alkhazi and Majed Abdullah Alrowaily
Additional contact information
Malek Alrashidi: Department of Computer Sciences, Applied College, University of Tabuk, Tabuk 47512, Saudi Arabia
Sami Mnasri: Department of Computer Sciences, Applied College, University of Tabuk, Tabuk 47512, Saudi Arabia
Maha Alqabli: Department of Computer Sciences, Applied College, University of Tabuk, Tabuk 47512, Saudi Arabia
Mansoor Alghamdi: Department of Computer Sciences, Applied College, University of Tabuk, Tabuk 47512, Saudi Arabia
Michael Short: Department of Engineering, School of Computing Engineering and Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK
Sean Williams: Department of Engineering, School of Computing Engineering and Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK
Nashwan Dawood: Department of Engineering, School of Computing Engineering and Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK
Ibrahim S. Alkhazi: Department of Computer Science, College of Computers & Information Technology, University of Tabuk, Tabuk 47512, Saudi Arabia
Majed Abdullah Alrowaily: Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia

Energies, 2025, vol. 18, issue 19, 1-23

Abstract: The growing demand for energy efficiency and sustainability in smart buildings necessitates advanced AI-driven methods for adaptive control and predictive maintenance. This study explores the application of Spiking Neural Networks (SNNs) to event-driven processing, real-time anomaly detection, and edge computing-based optimization in building automation. In contrast to conventional deep learning models, SNNs provide low-power, high-efficiency computation by mimicking biological neural processes, making them particularly suitable for real-time, edge-deployed decision-making. The proposed SNN based on Reward-Modulated Spike-Timing-Dependent Plasticity (STDP) and Bayesian Optimization (BO) integrates occupancy and ambient condition monitoring to dynamically manage assets such as appliances while simultaneously identifying anomalies for predictive maintenance. Experimental evaluations show that our BO-STDP-SNN framework achieves notable reductions in both energy consumption by 27.8% and power requirements by 70%, while delivering superior accuracy in anomaly detection compared with CNN, RNN, and LSTM based baselines. These results demonstrate the potential of SNNs to enhance the efficiency and resilience of smart building systems, reduce operational costs, and support long-term sustainability through low-latency, event-driven intelligence.

Keywords: spiking neural networks; smart building automation; event-driven processing; energy efficiency on edge computing; predictive maintenance; spike-timing-dependent plasticity (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/1996-1073/18/19/5089/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/19/5089/ (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:5089-:d:1757514

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 ().