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4th International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Tan, Lit Yang Chen Kwan, Charis Shwu Ajibade, Samuel-Soma M. Ramly, Athirah Mohd School of Engineering and Technology Sunway University Department of Computing and Information Systems Selangor Malaysia School of Architecture Computing and Engineering University of East London Department of Computing London United Kingdom

ISBN: (纸本)9798350353266

The incorporation of artificial intelligence (AI) into power-related applications signifies a new and unexplored domain in machine learning for predicting power generation. This novel method utilizes prediction models, often used in different fields, to predict energy-related patterns, providing a unique and specialized viewpoint. The synergy of academicians, AI experts, and industry professionals in the energy sector has resulted in the creation of customized AI models to optimize operational efficiency. By customizing various AI models to suit the distinct attributes of energy scenarios and datasets, these models are positioned to transform energy management methods. This study examines the utilization of AI models to enhance energy efficiency in power generation in Malaysia. The project seeks to predict future power consumption in various sectors, analyze growth rates, and identify sectors with investment potential by developing a Linear Regression model. In addition, a thorough power plan is developed using the estimated energy usage. A comparative analysis is performed to determine the most appropriate model for this particular scenario, which will improve decision-making in the energy sector. The results of this study present promising opportunities for further investigation. By broadening the study's focus to encompass a broader array of AI models and their assessment of performance, it is possible to gain useful insights for predicting power generation. Furthermore, the integration of real-time data streams and the inclusion of feedback loops in the AI models could improve their ability to adapt and increase their accuracy as time progresses. © 2024 IEEE.

关键词： Prediction models

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Algorithms for online CO2 baseline correction in intermittently occupied rooms 18

Algorithms for online CO2 baseline correction in intermitten...

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18th Healthy Buildings Europe Conference 2023: Beyond Disciplinary Boundaries

作者： Vihman, Lauri Parts, Tuule Mall Aljas, Hans Kristjan Thalfeldt, Martin Raik, Jaan Tallinn University of Technology Tallinn Estonia Centre for Dependable Computing Systems Department of Computing Systems Tallinn University of Technology Estonia Department of Civil Engineering and Architecture Tallinn University of Technology Tallinn Estonia

ISBN: (纸本)9781713877158

CO2 sensor data is often applied for Demand Controlled Ventilation (DCV), Indoor Air Quality (IAQ) assessment, and occupancy detection. In room controllers, the auto-calibration function shifts the zero level so that the measurements would not drift off. However, this creates jumps in data and sometimes values below outdoor CO2 level. If the data is further used, occupancy is detected differently, the ventilation would not function as designed, and the assessment would result in different IAQ class certificates. Therefore, in this work, a statistical method to correct the CO2 baseline automatically in real time was developed based on measurements from a school building in Estonia in 56 different rooms. The school had balanced heat recovery ventilation that assured adequate ventilation. During the process, the performance of different algorithms and parameters for the correction were compared. The CO2 concentration baseline correction algorithm was realised using the 1% percentile and 10-hour sliding time window as an optimal compromise to correct the base level to 400 ppm and the algorithm performed well based on qualitative assessment. The impact of the algorithm was significant when comparing the initially logged and corrected values against CO2 concentration thresholds 550, 800, and 1000 ppm. © Healthy Buildings Europe *** right reserved.

关键词： Carbon dioxide

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Analysis of Machine Learning (ML) and Internet of Things (IoT): A Scientometric Review 20

Analysis of Machine Learning (ML) and Internet of Things (Io...

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20th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2024

作者： Ajibade, Samuel-Soma M. Jasser, Muhammed Basheer Legaspino, Rosie Fe B. Gimeno, Anabella R. Ypanto, Leomar A. Baird, Jeem Clyde C. Adediran, Anthonia Oluwatosin Sunway University School Of Engineering And Technology Department Of Computing And Information Systems Selangor Malaysia Cebu Technological University Cebu Moalboal Philippines Federal University Of Uberlandia Faculty Of Architecture And Urban Design Uberlândia Brazil

ISBN: (纸本)9798350382310

The objective of this paper is to critically examine the publication's trends and research landscape on the Internet of Things and Machine Learning (Io T-ML) research through bibliometric analysis (BA). The Elsevier Scopus database was selected to identify, screen, and analyze the published documents on IoT-ML. The publication trends and major stakeholders on the topic were subsequently. BA was performed to examine the co-authorships, keyword occurrences, and citations using VOSviewer software. Lastly, a systematic literature review was performed to summarize the scientific and technological advancements. Results revealed that the publications on IoT-ML largely comprise articles, conference proceedings, and reviews, whereas the publication trends showed an increase from 2 to 105 between 2015 and 2021. The top researchers on IoT-ML are S. Baskar, M. Guizani, and A. Souri, whereas the top institution is the Vellore Institute of technology (India). However, the top country and research funder are;India, and National Natural Science Foundation (China), respectively. BA showed that the research landscape on IoT-ML comprises numerous networks of co-authors and countries working on related themes. The most impactful publication is Mahdavinejad et al. (2018) which has 479 citations, whereas the most prestigious journal is the IEEE Io T journal with 9 published documents in Scopus. Keyword analysis revealed IoT-ML has potential applications in health care, medicine, computer security and data analytics etc. Overall, IoT-ML has significant socio-economic and research impacts, and as such, the field will witness geometric techno-scientific growth and development in the future, particularly in the areas of network security and big data analytics. © 2024 IEEE.

关键词： Machine learning

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Distributed training and inference of deep learning solar energy forecasting models 31

Distributed training and inference of deep learning solar en...

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31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2023

作者： Campoy, Javier Prado-Rujas, Ignacio-Iker Risco-Martin, Jose L. Olcoz, Katzalin Perez, Maria S. Universidad Politecnica de Madrid Ontology Eneineering Group Etsi Informaticos. Madrid28660 Spain Universidad Complutense de Madrid Architecture and Technology of Computing Systems Madrid28040 Spain

ISBN: (纸本)9798350337631

Different accurate predictive models have been developed to forecast the amount of solar energy produced in a given area. These models are usually run in a centralized manner, considering irradiance inputs taken from a set of sensors that are deployed in that area. CAIDE is a framework that supports the deployment and analysis of solar plants following Model Based System Engineering (MBSE) and Internet of Things (IoT) methodologies. However, the current solution performs the training and inference phases of the solar energy forecasting models in a central way, not taking advantage of the distributed environment modeled by means of CAIDE. This work presents an extension of CAIDE that allows us to distribute the training and inference phases, obtaining performance improvements, and achieving a greater adaptation to the inherently distributed topology of the deployment of the sensors. © 2023 IEEE.

关键词： Solar energy

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Artificial Intelligence Models in Power Generation for Energy Consumption Prediction

Artificial Intelligence Models in Power Generation for Energ...

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International Conference on Emerging Trends in Networks and Computer Communications (ETNCC)

作者： Lit Yang Tan Charis Shwu Chen Kwan Samuel-Soma M. Ajibade Athirah Mohd Ramly Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Malaysia Department of Computing School of Architecture Computing and Engineering University of East London London UK

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

关键词： Adaptation models Energy consumption Analytical models Linear regression Artificial neural networks Predictive models Data models Artificial intelligence Power generation Streams

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Full life-cycle cost model for practical application of solar energy system

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Energy Informatics 2025年第1期8卷

作者： Sha, Ang Xiong, Zhen Zang, Xiaolin Zhao, Wei Ge, Ruibang Yao, Wanxiang Aiello, Marco Innovation Institute for Sustainable Maritime Architecture Research and Technology Qingdao University of Technology Qingdao266033 China School of Architecture and Urban Planning Qingdao University of Technology Qingdao266033 China School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China School of Continuing Education Qingdao University of Technology Qingdao266033 China Service Computing Department Institute of Architecture of Application Systems University of Stuttgart Universitätsstraße 38 Stuttgart70569 Germany

In pursuit of carbon neutrality, a swift transformation is underway in the global energy structure, marked by a consistent rise in the installed capacity of solar energy systems. Meanwhile, the substantial reduction of government subsidies in the solar industry intensifies focus on the economic viability of solar energy installations. In this study, we propose a full life-cycle cost model, named the F-LCC model, for calculating the cost of the solar energy system on the long term, e.g., 20–30 years. This model integrates replacement costs, residual value calculation, interest rate, and inflation impacts while supporting market price estimation for individual components, thereby aiding feasibility analysis in the early project phase. We design an investment cost recovery algorithm based on the F-LCC model to calculate the break-even electricity price for solar energy system. Moreover, we analyze component cost distributions, Net Present Value (NPV), and Discounted Payback Period (DPP) for grid-connected and off-grid solar energy systems with capacities of 10 kWp and 100 kWp in the Chinese market. The results show that the proposed model, compared to other models, captures the fact that payback times are longer. In a solar energy system without storage, solar panels have the highest component cost share at 28.8%. With battery storage, batteries dominate the total cost, reaching up to 74.6%. And the the grid-connected systems DPP ranging from a minimum of 5.5 to a maximum of 7.0 years by grid-connected electricity price, while off-grid systems require at least 19.9 years. The 10 kWp off-grid fixed mounting system’s break-even price being 137.1% higher than its grid-connected counterpart. In addition, tracking-mount systems offer greater cost-reduction potential than fixed installations, with the payback period reduced by 20% for 100 kWp grid-connected systems and 15% for off-grid systems. Finally, we develop a plugin based on the F-LCC model. These findings deepen unde

关键词： Commerce

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Cross-technology Communication between WiFi and ZigBee 9

Cross-Technology Communication between WiFi and ZigBee

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9th IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications, MAPE 2022

作者： Fan, Wendong Chen, Tian Gu, Yu Li, Yang Hefei University of Technology S2AC Lab School of Computer and Information Hefei China Hefei University of Technology Institute of Affective Computing and Systems Architecture Hefei China Anhui Agricultural University School of Information & Computer Hefei China

ISBN: (数字)9781665427753

ISBN: (纸本)9781665427753

The coexistence of heterogeneous devices in the Internet of Things wireless network brings a new topic of cross-technology communication in order to improve the communication efficiency between heterogeneous devices and promote the cooperation between heterogeneous devices. WiFi and ZigBee are the two most commonly used IoT devices in IoT environments. They have the advantages of low communication delay, high speed, low cost, and easy deployment. Since the spectrum of WiFi and ZigBee overlap, ZigBee signal will affect the transmission of CSI signal. We propose a CTC technique based on machine learning and deep learning, which uses only WiFi channel state information (CSI), and can distinguish whether there is ZigBee signal transmission in WiFi signals by classifying WiFi channel state information. In this paper, machine learning classification methods and deep learning methods are used to classify CSI sequences respectively. We extract the features of 8 CSI sequences to improve the accuracy of machine learning classifier and LSTM classifier, and adopt transformer network classifier to further improve the classification accuracy. In our experimental dataset, the highest accuracy rate can reach 94.9%. The evaluation results show that the accuracy of this method is significantly higher than the existing classification methods. © 2022 IEEE.

关键词： Channel state information

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Statistical Analysis of Digital Financial technology Adoption Research

Statistical Analysis of Digital Financial Technology Adoptio...

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2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Ajibade, Temiloluwa Iyanuoluwa Ayodele, Olamide Emmanuel Ajibade, Samuel-Soma M. Goles, Nanet A. Dayupay, Johnry P. Adediran, Anthonia Oluwatosin Scholar School System Department of Business Management Birmingham United Kingdom Ekiti State University Department of Banking and Finance Ekiti State Ado Ekiti Nigeria Sunway University School of Engineering and Technology Department of Computing and Information Systems Selangor Malaysia Cebu Technological University Moalboal Cebu Philippines Federal University of Uberlandia Faculty of Architecture and Urban Design Uberlandia Brazil

ISBN: (纸本)9798350358155

Digital financial technology adoption (DFTA) studies published in Scopus between 2019 and 2022 were examined in this study. After that, we assessed relevant literature, did bibliometric studies, and examined publication and citation patterns. The statistics showed 2643 citations and 176 publications. DFTA research has gained popularity when time-varying TP and TC values increased gradually. This topic's most popular sources are "articles"and "Sustainability (Switzerland)". A search of DFTA research stakeholders revealed several authors, organizations, and nations. Most famously associated with the University of Johannesburg in South Africa, David Mhlanga has produced more publications than anyone else. The National Natural Science Foundation and other Chinese donors who dominate DFTA research funding contributed to researchers' and affiliations' output. Cluster and hotspot analysis revealed two themes, "Digital financial technology adoption"and "Sustainable Banking Inclusion,"and three keywords, Digital financial technology adoption, monetary inclusion, and finance. According to the examined literature, DFTA's effects on international financial stability and GDP growth are understudied. Future research should examine DFTA's environmental impact, long-term profitability, and commercial strategies for low-income and rural communities. © 2024 IEEE.

关键词： Decentralized finance

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DU-DARTS: Decreasing the Uncertainty of Differentiable architecture Search 32

DU-DARTS: Decreasing the Uncertainty of Differentiable Archi...

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32nd British Machine Vision Conference, BMVC 2021

作者： Lu, Shun Hu, Yu Yang, Longxing Sun, Zihao Mei, Jilin Zeng, Yiming Li, Xiaowei Research Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy of Sciences China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China Tecent ADlab China

Differentiable Neural architecture Search (DARTS) recently attracts a lot of research attention because of its high efficiency. However, the competition of candidate operations in DARTS introduces high uncertainty for selecting the truly important operation, thus leading to serious performance collapse. In this work, we decrease the uncertainty of differentiable architecture search (DU-DARTS) by enforcing the distribution of architecture parameters to approach the one-hot categorical distribution and by replacing the zero operation with a gate switch. Without any extra search cost, our method achieves state-of-the-art performance with 2.32%, 16.74%, and 24.1% test error on CIFAR-10, CIFAR-100, and ImageNet datasets, respectively. Moreover, DU-DARTS can robustly find an excellent architecture on NAS-Bench-1Shot1 and NAS-Bench-201, which further demonstrates the effectiveness of our method. The source code is available at https://***/ShunLu91/DU-DARTS. © 2021. The copyright of this document resides with its authors.

关键词： Computer vision

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Distributed training and inference of deep learning solar energy forecasting models

Distributed training and inference of deep learning solar en...

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Euromicro Conference on Parallel, Distributed and Network-Based Processing

作者： Javier Campoy Ignacio-Iker Prado-Rujas Jose L. Risco-Martin Katzalin Olcoz Maria S. Perez Ontology Eneineering Group. ETSI Informaticos. Universidad Politecnica de Madrid Madrid Architecture and Technology of Computing Systems Universidad Complutense de Madrid Madrid

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