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AI4EF: Artificial Intelligence for Energy Efficiency in the building sector

SoftwareX 2025年 30卷

作者： Tzortzis, Alexandros Menelaos Kormpakis, Georgios Pelekis, Sotiris Michalitsi-Psarrou, Ariadni Karakolis, Evangelos Ntanos, Christos Askounis, Dimitris Decision Support Systems Laboratory School of Electrical and Computer Engineering Institute of Communications and Computer Systems National Technical University of Athens Greece

AI4EF (Artificial Intelligence for Energy Efficiency) is an advanced, user-centric tool designed to support decision-making in building energy retrofitting and efficiency optimization. Leveraging machine learning (ML) and data-driven insights, AI4EF (Artificial Intelligence for Energy Efficiency) enables stakeholders such as public sector representatives, energy consultants, and building owners—to model, analyze, and predict energy consumption, retrofit costs, and environmental impacts of building upgrades. Featuring a modular framework, AI4EF includes customizable building retrofitting, photovoltaic installation assessment, and predictive modeling tools that allow users to input building parameters and receive tailored recommendations for achieving energy savings and carbon reduction goals. Additionally, the service incorporates a Training Playground for data scientists to refine ML models used by said framework. Finally, AI4EF provides access to the Enershare Data Space to facilitate seamless data sharing and access within the ecosystem. AI4EF's compatibility with open-source identity management (Keycloak) enhances security and accessibility, making it adaptable for various regulatory and organizational contexts. This paper presents an overview of AI4EF's architecture, its application in energy efficiency scenarios, and its potential for advancing sustainable energy practices through artificial intelligence (AI). © 2025 The Authors

关键词： Energy efficiency

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A hypothetical comparative evaluation system for arctic indoors

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Frontiers of Architectural research 2025年第1期14卷 210-223页

作者： Seyed-Amin Tabatabaeifard Jean-Francois Lalonde Marc Hebert Andre Potvin Claude MH.Demers GRAP(Groupe de Recherche en Ambiances Physiques) Ecole d’architectureUniversite LavalQuebecCanada Computer Vision and Systems Lab Department of Electrical and Computer EngineeringUniversite LavalQuebecCanada CERVO Brain Research Centre Faculty of MedicineUniversite LavalQuebecCanada

This research presents an innovative approach to evaluating indoor spaces,combining qualitative attributes with numerical architectural metrics. A hypothetical comparative visualization system is introduced, utilizing HDR visual imaging and thermal imaging in 360° field of view across multiple indoor environments. The study aims to provide architectsand occupants with a user-friendly tool informing them about the primary considerations oftheir built spaces, with a specific focus on indoor environmental qualities in remote Arctic regions. Key inquiries delve into the efficacy of the spherical approach and the capacity ofcomparative visualization to offer insights into space quality. Preliminary experiments contrastindoor environments in terms of circadian lighting, thermal uniformity, and view access tooutside in the 360° field of view (VAR360). The resulting visualizations hold significance inintroducing an immersive approach for depicting specific non-visible environmental qualities,particularly in relation to the window characteristics of spaces. It demonstrates the integration of multiple environmental variables, both steady-state and temporal, from central pointswithin spaces, providing a comprehensive view over their non-visible qualities. These resultsshould be useful for researchers and practitioners within building sciences, computer vision,and photobiology, showcasing an out-of-the-box approach for categorizing indoor spaces basedon standards and human-environmental qualifications.

关键词： Environmental assessment 360°imaging Comparative systems Low-cost tools Built environments

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The Utilization of a Parallel Computing Algorithm for Accelerating Switching-Level Modeling of Power Electronics Simulations in a T-Type PV Inverter 14

The Utilization of a Parallel Computing Algorithm for Accele...

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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Brown, Buck F. Wang, Liwei Zhang, Zheyu Enslin, Johan Li, Yi Clemson University Department of Electrical and Computer Engineering North CharlestonSC United States Clemson University School of Computing North CharlestonSC United States Rensselaer Polytechnic Institute Department of Electrical Computer and Systems Engineering TroyNY United States

ISBN: (纸本)9798331516116

High-fidelity, switching-level modeling of power electronics simulations can be computationally intensive and time-consuming. This computational burden has recently escalated further due to the increased number of converters implemented in a system, e.g., renewable energy systems. Unlike switching modeling, average modeling can alleviate the computational burden and adequately represent a converter's behavior for controller design. However, average modeling does not encapsulate switching harmonics, which are crucial for power electronics design, operation, and reliability studies. This paper proposes a novel way to accelerate the switching-level modeling of simulations by leveraging parallel computing techniques and showcases this technique in a specific case study. This new technique is called the average-to-switching (A2S) model. First, the basic concept of the A2S model is outlined. Second, a case study is designed for a two-stage, T-type PV inverter. Third, electrical and thermal waveforms produced by the case study are given and discussed. The results of testing the A2S model showcase a speedup of greater than 22 times the simulation benchmark while maintaining an R2 value of greater than 0.99. © 2025 IEEE.

关键词： Power electronics

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A Miniature Hexa-Band Antenna for Internet of Things Applications Using Six Quarter-wavelength Resonators

Advanced Electromagnetics

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Advanced Electromagnetics 2025年第1期14卷 52-58页

作者： Hasan, Yousif Mohsin Rahman, Zain-Aldeen S. A. Al-Yasir, Yasir I. A. Department of Electronic and Communication Engineering College of Engineering University of Al-Qadisiyah Iraq Department of Electrical Techniques Technical Institute/Qurna Southern Technical University Basra61016 Iraq Communication and Space Systems Engineering Research Team Manchester Metropolitan University Manchester United Kingdom Communications Engineering Department Iraq University College Basra Iraq

This work presents a compact, low-cost hexa-band microstrip antenna for Internet of Things (IoT) applications. The proposed hexa-band antenna is composed of simple six-λ/4 resonators to generate six resonant frequencies: GSM 1.84 GHz (1.8-1.89 GHz), Bluetooth 2.31 GHz (2.25 –2.375 GHz), WiMAX 3.3 GHz (3.16-3.47 GHz), WiFi 4.63 GHz (4.34-5 GHz), upper WLAN 6.1 GHz (5.8-6.91 GHz) and X-band 9.26 GHz (8.87-9.83 GHz). The proposed hexa-band antenna is fabricated using FR4 substrate with (23 × 20 × 1.6) mm3 dimensions, and its measured results are presented to validate the simulated results. The results of simulations and measurements are used to examine the radiation properties, including radiation patterns, gain, efficiency, VSWR, and reflection coefficient. The proposed hexa-band antenna has a miniaturized size and good radiation performance. © 2025, Advanced Electromagnetics. All rights reserved.

关键词： Wireless local area networks (WLAN)

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All-silicon integrated platform for light generation, manipulation, and detection—and its application to refractive index sensors

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Optica 2025年第5期12卷 577-584页

作者： de Cea, Marc Kramnik, Danielius Stojanovic, Vladimir Ram, Rajeev J. Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Sciences UC Berkeley BerkeleyCA94720 United States

Traditional silicon photonic platforms offer powerful capabilities for light manipulation and detection but cannot realize an integrated light source. This results in systems with increased cost and complexity due to the need for external light generation and coupling. In this work we overcome this limitation and experimentally demonstrate the generation, manipulation, and detection of waveguide-coupled light in a commercial silicon photonics foundry with monolithically integrated electronics—without any modification to the fabrication process and no additional post-processing steps. We propose the use of our platform to realize fully integrated, monolithic optical refractometric sensors and theoretically show that performance comparable to commercial, state of the art bench-top systems can be achieved. Our work lays the foundation for the realization of truly monolithic, full-functionality silicon photonic electro-optical systems with increasing levels of miniaturization and cost reduction. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Silicon photonics

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Enhancing Smart Microgrid Resilience and Virtual Power Plant Profitability Through Hybrid IGWO-PSO Optimization With a Three-Phase Bidding Strategy

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IEEE Access 2025年 13卷 80796-80820页

作者： Yuvaraj, T. Sengolrajan, T. Prabaharan, Natarajan Devabalaji, K.R. Uehara, Akie Senjyu, Tomonobu Chennai Institute of Technology Centre for Smart Energy Systems Chennai600069 India Kongunadu College of Engineering and Technology Department of Electrical and Electronics Engineering Tiruchirappalli621215 India SASTRA Deemed University School of Electrical and Electronics Engineering Thanjavur613401 India Aarupadai Veedu Institute of Technology Vinayaka Missions Research Foundation Department of Electrical and Electronics Engineering Chennai603104 India University of the Ryukyus Faculty of Engineering Nishihara903-0213 Japan

The increasing energy demand and rising fossil fuel prices are accelerating the transition to renewable energy, supported by government initiatives due to their environmental and economic advantages. However, challenges such as limited capacity and stability constraints hinder the widespread adoption of distributed energy resources (DERs). Virtual Power Plants (VPPs) enhance market participation by aggregating DERs, while electric vehicles (EVs) contribute to environmental sustainability by reducing emissions. Additionally, integrating distribution static compensators (DSTATCOMs) within VPPs improves microgrid stability and reactive power support. This study proposes a two-stage optimization approach to enhance network resilience and VPP profitability in a radial distribution network (RDN). The first stage focuses on minimizing resilience-related costs and energy not supplied (ENS) during natural disasters, while the second stage optimizes VPP profit using a three-phase bidding strategy, which includes the day-ahead market, real-time market, and overall market. A hybrid improved grey wolf optimization-particle swarm optimization (IGWO-PSO) algorithm is developed to solve this complex optimization problem. To demonstrate the effectiveness of the proposed approach, IGWO-PSO is compared with other hybrid optimization algorithms. Validation on a modified IEEE 33-bus RDN confirms that the proposed model enhances VPP placement and sizing, leading to improved economic, operational, and resilience metrics. Furthermore, the model accounts for uncertainties in load demand, renewable generation, energy prices, and equipment availability, ensuring a robust and adaptable energy management strategy. © 2013 IEEE.

关键词： Particle swarm optimization (PSO)

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Enhancing Data Security and Privacy in Cloud Computing: A Survey of Modern Techniques 3

Enhancing Data Security and Privacy in Cloud Computing: A Su...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Suganya, N. Gouthami, P. Sathiya, R. Sivaranjani, S. Murugesan, M. Department of Computer Science and Business Systems Dr. N.G.P. Institute of Technology Coimbatore India Department of Information Technology Thangavelu Engineering College Chennai India Departement of Electrical and Electronics Engineering Sri Krishna College of Engineering and Technology Coimbatore India Departement of Electrical and Electronics Engineering Karpagam Institute of Technology Coimbatore India

ISBN: (纸本)9798331527549

A computing system that is based on the internet and offers users all resources as on-demand services. Servers, storage, databases, software, and networking are among these on-demand services. Usually, the user must pay for the use of the resources;they are not their property. The number of people using this technology is increasing dramatically as a result of these features. Although it has benefits, cloud computing poses certain security and privacy risks. The authorized user’s data can be stolen easily. This current research thoroughly investigates modern strategies to these problems, highlighting identity management protocols, effective encryption and blockchain-based solutions. The article identifies research gaps, focuses attention to the inadequacies of current approaches, and recommends the possible ways to improve the efficiency, scalability and trustworthiness. © 2025 IEEE.

关键词： Cloud security

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An Underdetermined Single-Channel Blind-Source-Separation for Multisource Acoustic-Emission-Based Partial Discharge Signals in Power Transformers

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IEEE Transactions on Plasma Science 2025年第4期53卷 788-797页

作者： Mandal, Raj Kumar Devarajan, Harimurugan Nandi, Sounak Shanker, T. Bhavani Ghassemi, Mona Dr. B. R. Ambedkar National Institute of Technology Jalandhar144011 India Central Power Research Institute Power Capacitors Division Bengaluru560080 India University of Texas at Dallas Department of Electrical and Computer Engineering RichardsonTX75080 United States

Condition monitoring of the power transformer is important in extending the lifetime of the transformer through timely maintenance. Acoustic-emission (AE)-based partial discharge (PD) monitoring in a transformer is a commonly used technique for assessing the PD levels inside the transformer. The classification of the type of PD or localization of PD generally involves using multiple acoustic sensors, and recently, deep learning techniques have been widely used with the acquired PD data for classification. In the present work, a single-channel synchrosqueezing transform (SST)-based FastIVA blind-source-separation (BSS) technique in the time-frequency (TF) domain is proposed for the separation of multiple PD signals. SST, along with singular value decomposition (SVD), is used to identify the number of sources. The SST-FastIVA-based method is used for PD signal separation for the first time and it performs better than other separation techniques in noisy conditions. An SVM-based machine learning model is developed with original unmixed source signals for a classification task. The separated signals are tested using the developed model, and it attains an F1 score of 78.70% on the separated signals. Furthermore, the proposed methodology is used with the experimental data, and the F1 score obtained with the separated signal is 84.44%. The proposed single-channel (sensor) technique with BSS is a cost-effective method that can be used for the classification of multiple PD sources. © 1973-2012 IEEE.

关键词： Blind source separation

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Bidirectional LSTM-Based Energy Consumption Forecasting:Advancing AI-Driven Cloud Integration for Cognitive City Energy Management

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computers, Materials & Continua 2025年第5期83卷 2907-2926页

作者： Sheik Mohideen Shah Meganathan Selvamani Mahesh Thyluru Ramakrishna Surbhi Bhatia Khan Shakila Basheer Wajdan Al Malwi Mohammad Tabrez Quasim Department of Computer Science and Engineering Srinivasa Ramanujan CentreSASTRA Deemed UniversityKumbakonam612001India Department of Computer Science and Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bangalore562112India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and TechnologyChitkara UniversityRajpura140401PunjabIndia Department of Information Systems College ofComputer and Information SciencePrincessNourah bintAbdulrahmanUniversityP.O.Box 84428Riyadh11671Saudi Arabia College of Computer Science Informatics and Computer Systems Department King Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and *** study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)*** a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear *** bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal *** design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and *** to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these *** results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive *** integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.

关键词： Deep learning bidirectional LSTM energy consumption forecasting time-series analysis predictive modeling machine learning in energy management

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XMeta-OS: A Conceptual Framework for Unified Resource Management in Distributed Edge-Cloud Environments 3rd

XMeta-OS: A Conceptual Framework for Unified Resource Manag...

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3rd International Conference on Advances in Computing research, ACR 2025

作者： Zacharioudakis, Eleftherios Kakoulli, Elena Evripidou, Salomi Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos Pafos Cyprus National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (纸本)9783031876462

This paper introduces XMeta-OS, a meta-operating system specially designed with Linux as its foundation to unify and optimize resource management for the distributed edge-cloud when dynamic use of GPU resources is possible. This approach offer a new GPU abstraction layer and dynamic edge-cloud allocation model that offer enhanced flexibility and resource utilization over existing systems like Kubernetes and OpenStack. By using likely sensitive data, the model applied cloud-pushed deployment model. XMeta-OS manages computer resources in real-time. Therefore, fulfilling high performance various needs. It allocates CPU and GPU in real-time to workloads requiring massive parallel processing, such as machine learning, scientific simulations, real-time data analytics, and transportation tasks. Fabrication in the laboratory of an edge-cloud environment can help to assess the scalability, fault tolerance and efficiency of the system. It showcases the overall architectural design of XMeta-OS. Changes to the kernel and security solutions for safe and effective distribution are suggested. This article presents the design of the XMeta-OS. The future of XMeta-OS entails usage and testing with which we see latency, energy, computation and other such measurements in labs. XMeta-OS is a solution to cost performance, emerging application, large-scale edge-cloud deployments, and distributed GPU-enabled infrastructures. Meta-Operating System in Edge-Cloud Continuum for dynamic resource management of GPU. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cluster computing

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