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Multi-agent system for optimized energy management in multi-smart buildings via deregulated market system

Energy Reports 2025年 14卷 455-472页

作者： Abdul Haseeb Sajid Muhammad Zubair Iftikhar Syed Ali Abbas Kazmi Zafar A. Khan Sultan Alghamdi Muhammad Waseem US-Pakistan Centre for Advanced Studies in Energy (USPCAS-E) National University of Sciences & Technology (NUST) H-12 Sector Islamabad 44000 Pakistan Department of Electrical and Electronic Engineering The University of Melbourne VIC 3010 Australia Department of Electrical Engineering Mirpur University of Science and Technology Mirpur AJK Pakistan Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah 21589 Saudi Arabia Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah 21589 Saudi Arabia International Renewable and Energy Systems Integration Research Group (IRESI) School of Business Maynooth University Maynooth Co. Kildare W23 HW31 Ireland

A key focus in smart grid research, especially within emerging distribution frameworks, is the effective management of energy resources. The proposed approach employs a multi-agent system to manage energy resources in interconnected smart buildings. The goal is to enhance resilience by determining the energy available for consumption or storage from the source by generating agent, adjusting energy usage in response to grid cost by load agent, and optimizing the supply-and-demand dynamic by storing surplus power during peak hours and discharging it during off-peak hours by storage agent. A MATLAB simulation-based model was developed to analyze five different cases, resulting in the proposed scheme. During off-peak hours, grid dependence decreases by 65.5 %, by 73.49 % during mid-peak hours, and by 85.9 % during peak hours, compared to the base case. When comparing case 3 to the base scenario, the share of energy imported by buildings from the main grid during off-peak, mid-peak, and peak hours is reduced to 69.8 %, 76.51 %, and 89.39 %, respectively, following the implementation of the deregulated market system. This paper presented a genetic algorithm in case 4 to optimize consumption patterns to minimize costs and achieve a 30 % reduction in consumption during peak hours. The resulting consumption decreases peak-hour grid imports by 85.4 % and 96.1 % for building 1 and building 3, respectively, compared to the base case. The findings of this study are useful in understanding the prospects of transforming the energy landscape through proper energy management and enabling a competitive energy market structure.

关键词： GA Generating Agent FIT Feed-In Tariff DER Distributed Energy Resources EMMC Energy Market Management Centre MCA Microgrid Control Agent DRP Demand Response Program SA Storage Agent TOU Time of Use RTP Real-Time Pricing PV Photovoltaic GP Generation Pattern DSM Demand Side Management GHG Greenhouse Gases PAR Peak-to-Average Ratio ESS Energy Storage System SOC State of Charge MCP Market Clearing Price MAS Multi-Agent System RER Renewable Energy Resources Demand side management Deregulated market system Energy scheduling Multi-agent system

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The 14th International Symposium on Visual Information Communication and Interaction

ACM International Conference Proceeding Series

引用

ACM International Conference Proceeding Series 2021年

作者： Klein, Karsten Burch, Michael Limberger, Daniel Trapp, Matthias Computer Graphics Systems Group The Hasso Plattner Institute Digital Engineering Faculty University of Potsdam Potsdam Germany

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Learning For Predictive Control: A Dual Gaussian Process Approach

arXiv

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arXiv 2022年

作者： Liu, Yuhan Wang, Pengyu Tóth, Roland Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Flight Dynamics and Control Laboratory Department of Aerospace Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

An important issue in model-based control design is that an accurate dynamic model of the system is generally nonlinear, complex, and costly to obtain. This limits achievable control performance in practice. Gaussian process (GP) based estimation of system models is an effective tool to learn unknown dynamics directly from input/output data. However, conventional GP-based control methods often ignore the computational cost associated with accumulating data during the operation of the system and how to handle forgetting in continuous adaption. In this paper, we present a novel Dual Gaussian Process (DGP) based model predictive control (MPC) strategy that enables efficient use of online learning based predictive control without the danger of catastrophic forgetting. The bio-inspired DGP structure is a combination of a long-term GP and a short-term GP, where the long-term GP is used to keep the learned knowledge in memory and the short-term GP is employed to rapidly compensate unknown dynamics during online operation. Furthermore, a novel recursive online update strategy for the short-term GP is proposed to successively improve the learnt model during online operation. Effectiveness of the proposed strategy is demonstrated via numerical simulations. © 2022, CC BY-NC-SA.

关键词： Model predictive control

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Agricultural data Privacy: Emerging platforms & strategies

Food and Humanity

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Food and Humanity 2025年 4卷

作者： Gavai, Anand K. Bouzembrak, Yamine Xhani, Donika Sedrakyan, Gayane Meuwissen, Miranda P.M. Souza, Renata Guizzardi-Silva Marvin, Hans J.P. van Hillegersberg, Jos Industrial Engineering & Business Information Systems University of Twente Enschede Netherlands Information Technology Group Wageningen University and Research Wageningen Netherlands Business Economics Group Wageningen University and Research Wageningen Netherlands Research department of HAYAN Group B.V Rhenen Netherlands Jheronimus Academy of Data Science (JADS) s-Hertogenbosch Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven 5612 AP Netherlands

Background: In today's world, grappling with the dual challenges of energy scarcity and climate change, the agricultural and food supply chains are at a crucial juncture for transformation. Data privacy within these sectors is increasingly significant, as the integration of advanced digital platforms becomes essential for improving efficiency and sustainability. Scope and Approach: This systematic literature review (SLR) explores the development and application of privacy-preserving data platforms specifically tailored to the agricultural and food supply chains. The review focuses on the evolving landscape of data architectures, data sovereignty, and advanced privacy-preserving techniques. Techniques such as anonymization, encryption, differential privacy, and federated learning are examined, along with the legal and ethical considerations surrounding data sharing in the context of global energy and climate-related challenges. Key Findings and Conclusions: The review synthesizes findings from a broad spectrum of studies published over the last decade, uncovering significant advancements in privacy-preserving technologies which may show their benefit for the agricultural and food sectors. It identifies dominant research trends and promising future directions for enhancing data security and sustainability. The key findings underscore the vital importance of safeguarding data privacy in these sectors, highlighting the potential of advanced privacy techniques to protect sensitive agricultural data while balancing the need for transparency and operational effectiveness. The review concludes that adopting these innovative privacy methods is crucial for fostering a more sustainable and secure future for agriculture and food systems, contributing to the global Sustainable Development Goals such as SDG 2 (Zero Hunger) and SDG 12 (Responsible Consumption and Production) by improving precision agriculture, optimizing yields (crop and animal) and reducing food waste. © 2025 The

关键词： Data Infrastructure Data platforms Data privacy Food systems Sustainability

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Oral cavity carcinoma detection using BAT algorithm-optimized machine learning models with transfer learning and random sampling

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computers in Biology and Medicine 2025年第PT B期192卷 110250-110250页

作者： Folorunso, Sakinat O. Abdulwarith, Akinshipo Adeniyi, Abidemi Emmanuel Aworinde, Halleluyah Oluwatobi Awotunde, Joseph Bamidele Artificial Intelligence Systems Research Group Department of Computer Science Olabisi Onabanjo University Ago-Iwoye Nigeria Department of Oral and Maxillofacial Pathology and Biology Faculty of Dentistry University of Lagos Lagos Nigeria Department of Computer Science College of Computing and Communication Studies Bowen University Iwo Nigeria Chitkara University Institute of Engineering and Technology Chitkara University Punjab India MIVA Open University Abuja Nigeria Department of Information Technology Durban University of Technology KZ Durban South Africa Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Nigeria

Background: Oral cavity carcinoma remains a major public health concern, where early and accurate detection is vital for improving patient outcomes and survival rates. Current diagnostic systems often face challenges such as limited feature selection capabilities, imbalanced datasets, and computational inefficiencies. Methods: This study proposes a novel diagnostic framework TR-ROS-BAT-ML that integrates transfer learning, random sampling, and a BAT algorithm-based optimization strategy with ensemble machine learning classifiers. A dataset comprising 1224 hematoxylin and eosin (H&E)-stained histological images (at 100x and 400x magnifications) of normal oral epithelium and Oral Squamous Cell Carcinoma (OSCC) was collected from 230 patients using a Leica ICC50 HD microscopy camera. Pre-trained deep learning models (NANSNetLarge, EfficientNetB7, EfficientNetV2L, EfficientNetV2S, EfficientNetV2M) were employed for feature extraction. To address class imbalance, random oversampling techniques were applied. The BAT algorithm, inspired by bat echolocation behavior, was used for feature selection and hyperparameter tuning. Optimized features were classified using ensemble methods, including XGBoost, AdaBoost, Extra Trees (ET), Histogram-Based Gradient Boosting (HBGC), and MultiLayer Perceptron (MLP). Results: The proposed approach achieved high diagnostic performance across multiple model combinations. The best performance was recorded with the optimized ET model using random oversampling, achieving a recall of 0.992, demonstrating its efficacy in detecting oral lesions. In contrast, the combination of EfficientNetV2S + ROS + MLP yielded the lowest accuracy at 50.8 %. These results confirm the robustness of the TR-ROS-BAT-ML framework in handling imbalanced datasets and optimizing classification performance. Conclusions: This study demonstrates the effectiveness of combining nature-inspired optimization, transfer learning, and ensemble machine learning for enhanced detecti

关键词： Adaptive boosting

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High-Gain, Low-Profile, Integrable Planar Lens Antenna at 275 GHz

High-Gain, Low-Profile, Integrable Planar Lens Antenna at 27...

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Mingxiang Stephen Li Rajour Tanyi Ako Sharath Sriram Christophe Fumeaux Withawat Withayachumnankul Terahertz Engineering Laboratory The University of Adelaide Adelaide SA Australia Functional Materials and Microsystems Research Group and the Micro Nano Research Facility ARC Centre of Excellence for Transformative Meta-Optical Systems RMIT University Melbourne VIC Australia School of Electrical Engineering and Computer Science The University of Queensland Brisbane QLD Australia

Terahertz communications require compact, high-gain antennas that can be integrated with sources, as alternative to traditional lenses, which have bulky sizes and lack design flexibility. This paper introduces a high-gain, low-profile silicon cavity antenna integrated with a WR-3 waveguide feed. The antenna consists of a 2-mm thick silicon wafer with non-uniform hole arrays designed to locally control the effective permittivity. The holes are laser-etched on both sides of the silicon wafer, to overcome phase coverage limitations caused by the tapered side walls of the air hole during fabrication. Experimental results confirm a gain of 19 dBi at 275 GHz, with a 3-dB bandwidth of approximately 29%. We further demonstrate that the high-gain antenna can be integrated with a quarter-wave plate for circularly polarized (CP) radiation.

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A Low-Complexity Solution to Sum Rate Maximization for IRS-assisted SWIPT-MIMO Broadcasting

arXiv

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arXiv 2023年

作者： Kumar, Vaibhav Papazafeiropoulos, Anastasios Hanif, Muhammad Fainan Tran, Le-Nam Flanagan, Mark F. School of Electrical and Electronic Engineering University College Dublin Belfield Dublin 4 Ireland Communications and Intelligent Systems Research Group University of Hertfordshire HatfieldAL10 9AB United Kingdom University of Luxembourg Luxembourg Institute of Electrical Electronics and Computer Engineering University of the Punjab Lahore Pakistan

This paper focuses on the fundamental problem of maximizing the achievable weighted sum rate (WSR) at information receivers (IRs) in an intelligent reflecting surface (IRS) assisted simultaneous wireless information and power transfer system under a multiple-input multiple-output (SWIPT-MIMO) setting, subject to a quality-of-service (QoS) constraint at the energy receivers (ERs). Notably, due to the coupling between the transmit precoding matrix and the passive beamforming vector in the QoS constraint, the formulated non-convex optimization problem is challenging to solve. We first decouple the design variables in the constraints following a penalty dual decomposition method, and then apply an alternating gradient projection algorithm to achieve a stationary solution to the reformulated optimization problem. The proposed algorithm nearly doubles the WSR compared to that achieved by a block-coordinate descent (BCD) based benchmark scheme. At the same time, the complexity of the proposed scheme grows linearly with the number of IRS elements while that of the benchmark scheme is proportional to the cube of the number of IRS elements. © 2023, CC BY.

关键词： Energy harvesting

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Segmentation and Multi-facet Classification of Individual Logs in Wooden Piles 18th

Segmentation and Multi-facet Classification of Individual Lo...

引用

18th International Conference on computer Aided systems Theory, EUROCAST 2022

作者： Praschl, Christoph Auersperg-Castell, Philipp Forster-Heinlein, Brigitte Zwettler, Gerald Adam Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria Bluedynamics Auersperg-Castell KG Kritzing 31 Freinberg Austria Faculty of Computer Science and Mathematics University of Passau Passau Germany

ISBN: (纸本)9783031253119

The inspection of products and assessment of quality is connected with high costs and time effort in many industrial domains. This also applies to the forestry industry. Utilizing state-of-the-art deep learning models allows automizing the analysis of wooden piles in a vision-based manner. In this work, a parallel two-step approach is presented for the segmentation and multi-facet classification of individual logs, according to the wood type and quality. The present approach is based on a preliminary log localization step and like this allows determining the quality, volume and also the value of individual logs, respectively the whole wooden pile. Using a YOLOv4 model for wood species classification for douglas firs, pines and larches results in an accuracy of 74.53%, while a quality classification model for spruce logs reaches 86.58%. In addition to that, the trained U-NET segmentation model reaches an accuracy of 93%. In the future, the underlying data set and models will be further improved and integrated to a mobile application for the on site analysis of wooden piles by foresters. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Nonlinear reconfiguration of network edges, topology and information content during an artificial learning task

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Brain Informatics 2021年第1期8卷 1-15页

作者： Shine, James M. Li, Mike Koyejo, Oluwasanmi Fulcher, Ben Lizier, Joseph T. Centre for Complex Systems The University of Sydney CamperdownNSW2006 Australia Brain and Mind Centre The University of Sydney CamperdownNSW2050 Australia Beckman Institute for Advanced Science and Technology University of Illinois Champaign Champaign United States Department of Computer Science University of Illinois at Urbana-Champaign Champaign United States School of Physics The University of Sydney CamperdownNSW2006 Australia Complex Systems Research Group Faculty of Engineering The University of Sydney CamperdownNSW2006 Australia

Here, we combine network neuroscience and machine learning to reveal connections between the brain’s network structure and the emerging network structure of an artificial neural network. Specifically, we train a shallow, feedforward neural network to classify hand-written digits and then used a combination of systems neuroscience and information-theoretic tools to perform ‘virtual brain analytics’ on the resultant edge weights and activity patterns of each node. We identify three distinct phases of network reconfiguration across learning, each of which are characterized by unique topological and information-theoretic signatures. Each phase involves aligning the connections of the neural network with patterns of information contained in the input dataset or preceding layers (as relevant). We also observe a process of low-dimensional category separation in the network as a function of learning. Our results offer a systems-level perspective of how artificial neural networks function—in terms of multi-stage reorganization of edge weights and activity patterns to effectively exploit the information content of input data during edge-weight training—while simultaneously enriching our understanding of the methods used by systems neuroscience. © 2021, The Author(s).

关键词： Brain

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Cell-free Massive MIMO Channels in an Urban Environment - Measurements and Channel Statistics

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IEEE Transactions on Wireless Communications 2025年

作者： Zhang, Yuning Choi, Thomas Cheng, Zihang Gomez-Ponce, Jorge Kanno, Issei Ito, Masaaki Molisch, Andreas F. Wireless Devices and Systems Group in the Ming Hsieh Department of Electrical and Computer Engineering Los AngelesCA90089 United States ESPOL Polytechnic University Escuela Superior Politécnica Del Litoral ESPOL Facultad de Ingeniería en Electricidad y Computación Km 30.5 vía Perimetral GuayaquilEC090112 Ecuador KDDI Research Inc Saitama Japan

Cell-free massive MIMO (CF-mMIMO), where each user equipment (UE) is connected to multiple access points (APs), is emerging as an important component for fifth-generation (5G) and sixth-generation (6G) cellular systems. Accurate channel models based on measurements are required to optimize the design and deployment of such systems. This paper presents an extensive measurement campaign for CF-mMIMO in an urban environment. A new 'virtual AP' technique measures channels between 80 UE locations and more than 20, 000 possible microcellular AP locations. Measurements are done at 3.5 GHz carrier frequency with 350 MHz bandwidth (BW). The paper describes the measurement setup and data processing, shows sample results and their physical interpretation, and provides statistics for key quantities such as pathloss, shadowing, delay spread (DS), and delay window. We find pathloss coefficients of 2.9 and 10.4 for line-of-sight (LOS) and non line-of-sight (NLOS), respectively, where the high LOS coefficient is mainly because larger distance leads to more grazing angle of incidence and thus lower antenna gain in our setup. Shadowing standard deviations are 5.1/16.6 dB, and root mean squared (RMS) DSs of -80.6/-72.6 dBs1. The measurements can also be used for parameterizing a certain type of channel model, namely Cell-free massive MIMO for Urban Non-stationary Environment with Correlations (CUNEC), which will be reported in future work. © 2002-2012 IEEE.

关键词： 5G mobile communication systems

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