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Load Forecasting For Air Conditioning systems Using Linear Regression and Artificial Neural Networks

Load Forecasting For Air Conditioning Systems Using Linear R...

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IEEE international conference on Smart Instrumentation, measurement and Applications (ICSIMA)

作者： Zakaria Zikri Bin Zainudin Siti Hajar Binti Yusoff Teddy Surya Gunawan Sarah Yasmin Mohamad Israth Jahan Chowdhury Siti Nadiah Mohd Sapihie Electrical and Computer Engineering Dept. International Islamic University Malaysia Kuala Lumpur Malaysia Petronas Research Sdn Bhd Malaysia

ISBN: (数字)9798350365283

ISBN: (纸本)9798350365290

The increasing demand for energy efficiency in industrial sectors necessitates innovative approaches to optimize energy consumption. This research addresses the challenge of accurately forecasting energy loads in air conditioning systems within the metal printing industry. Traditional forecasting methods often fail to capture industrial settings' complex, dynamic energy demands. This study aims to develop a precise load forecasting model by integrating Linear Regression (LR) and Artificial Neural Networks (ANN). Using real-world data from Kian Joo Can Factory Berhad, the ANN model demonstrated superior performance with a Mean Absolute Percentage Error (MAPE) of 11.44% and a Coefficient of Variation of the Root Mean Square Error (CVRMSE) of 4.214%. These findings suggest significant potential for reducing energy consumption, lowering operational costs, and improving equipment maintenance. Implementing machine learning algorithms in this context underscores their value in enhancing the efficiency, reliability, and cost-effectiveness of Air Handling Units (AHU) in industrial air conditioning systems.

关键词： Air conditioning Energy consumption Costs Load forecasting Atmospheric modeling Linear regression Artificial neural networks

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Prediction of the Magnitude of Lightning Impulse Interference in an Electronic Device Using an Artificial Neural Network

Prediction of the Magnitude of Lightning Impulse Interferenc...

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international conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： Zinnur Gizatullin Ramis Mubarakov Department for Automated Systems for Information Processing and Control Department for Computer-Aided Design Systems Kazan National Research Technical University named after A.N. Tupolev-KAI Kazan Russian Federation Department for Computer-Aided Design Systems Kazan National Research Technical University named after A.N. Tupolev-KAI Kazan Russian Federation

ISBN: (数字)9798350395013

ISBN: (纸本)9798350395020

Reliable operation of modern electronic devices is the basis for their effective use. Electromagnetic interference, such as lightning, creates the risk of temporary or permanent disruption of the functioning of device elements. Therefore, it is necessary to take into account the possible consequences of such interference on the basis of prediction even at the design stage of electronic devices. The paper a technique for predicting the magnitude of lightning impulse interference in electronic devices using an artificial neural network is proposed. The advantage of this approach over simulation modeling is that rather complex three-dimensional models and long computation times are not needed. A stand was developed and obtained by measuring the amount of lightning interference in an electronic device. The features of the task of training an artificial neural network based on experimental data are considered. The architecture and parameters of the artificial neural network have been selected. An artificial neural network was trained based on experimental data. The root mean square error between the predicted data and the average impulse interference voltage values on the training data does not exceed acceptable values. The results of predicting the magnitude of lightning interference in an electronic device are presented. If we consider further prospects for using an artificial neural network to predict electromagnetic interference, we can note the relevance of the problem of the impact of electrostatic discharge, intentional short pulse and etc.

关键词： Training Solid modeling Magnetic field measurement Computational modeling Electromagnetic interference Lightning Training data

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Clustering Approach for modeling Course Difficulty Level in Adaptive Learning

Clustering Approach for Modeling Course Difficulty Level in ...

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IEEE international conference on Computational Intelligence and Virtual Environments for measurement systems and Applications

作者： Imamah Umi Laili Yuhana Arif Djunaidy Eric Pardede Mauridhi Hery Purnomo Departement of Electrical Engineering Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Departement of Informatics Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Departement of Information Systems Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Departement of Computer Science and Information Technology La Trobe University Bundoora VIC Australia

ISBN: (数字)9798350322996

ISBN: (纸本)9798350323009

The variations in students’ abilities present challenges in achieving desired learning outcomes. Students exhibit different levels of comprehension regarding the complexity of learning materials. In this study, we aim to model the difficulty levels of learning materials using a clustering approach to offer personalized learning material recommendations within an adaptive learning framework. Data were collected from student scores of Data Structure course obtained between 2020 and 2022 at the University of Trunojoyo Madura. The dataset modeling process adheres to the CRISP-DM Methodology, employing the K-Means clustering method and Euclidean Distance. We employed the Within-Cluster Sum of Squares (WCSS) metric and silhouette score to identify the optimal K-value, which resulted in a value of 5, accompanied by a silhouette score of 0.629. Our analysis pinpointed Stack and Deque as the easiest, while Linked List emerged as the most challenging course material. Accordingly, we will assign a label of 1 to the easiest materials, 2 to those of medium difficulty, and 3 to the most challenging ones. These labels will serve as criteria for recommending suitable learning materials to students based on their knowledge level. We will assess the ratio between the overall knowledge level score and the difficulty level of the material. The ratio value will serve as a reference for determining material recommendations for students. Materials categorized with the highest difficulty levels are exclusively suggested to students demonstrating faster learning abilities with a high value of ratio, and vice versa. This approach is feasible for implementation in adaptive learning.

关键词： measurement Adaptive learning Adaptation models Computational modeling Clustering methods Virtual environments Euclidean distance

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Study on Intelligent Decision-Making of Cooperative Search Underwater Target with A Certain Type of Aircraft

Study on Intelligent Decision-Making of Cooperative Search U...

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international conference on computers, Communications, and systems (ICCCS)

作者： Shanhui Zhang Yongqin Sun Xiao Yang Haicui Meng The 91206 Unit of PLA Navy Aviation University Qingdao China

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

In order to deal with the problem of intelligent decision-making with a large amount of uncertain information in the ocean battlefield, an intelligent decision-making method of cooperative search underwater target is put forward based on fuzzy measure. Firstly, the search scheme is determined, and the decision- making index model is constructed to establish invisibility model, search capability model, operability model and economy model. According to the Shapley value, the Marichal entropy algorithm is used to solve the gλ fuzzy measure, the importance of each decision-making index is determined. Then, the decision-making index is integrated by Choquet Fuzzy integral, the intelligent decision-making is realized. In the last, the simulations combined with the problem analysis process show that the method is rational and effectual.

关键词： Weight measurement Oceans Computational modeling Simulation Decision making Sea measurements Search problems

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Comparative Analysis of Neural Network and Radial Basis Functions Approaches for Electricity Consumption Forecasting in Five Different Regions in Albania

Comparative Analysis of Neural Network and Radial Basis Func...

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IEEE international Symposium on Innovations in Intelligent systems and Applications (INISTA)

作者： Agresa Qosja Eralda Gjika Dhamo Didier Georges Ligor Nikolla Arben Cela Department of Computer Science University Metropolitan Tirana GIPSA-lab University Grenoble Alpes CNRS Tirana Albania Department of Applied Mathematics University of Tirana Tirana Albania GIPSA-lab Grenoble INP-UGA University Grenoble Alpes CNRS Grenoble France Department of Computer Science University Metropolitan Tirana Tirana Albania Department of Informatics and Telecommunication University Gustave Eiffel ESIEE Paris Paris France

ISBN: (数字)9798350368130

ISBN: (纸本)9798350368147

Forecasting electricity consumption remains essential, necessitating the application of recent, hybrid, and classical models. Traditional approaches often fall short in capturing nonlinear and irregular data patterns. This paper compares three distinct approaches for surrogate modeling and forecasting daily electricity consumption across five regions in Albania: a Radial Basis Function approach, a feedforward Neural Network approach, and a Recurrent Neural Network approach. Our aim is to develop a generalized model for these regions and provide highly accurate one-day-ahead predictions, considering daily average temperature as an exogenous variable. We conduct a comparative analysis based on model fit, computational efficiency, and error measurement metrics. Our findings underscore the exceptional performance of the RBF ARX approach, showcasing its efficacy in accurately forecasting electricity consumption based on daily data. Daily mean temperature is identified as a key factor influencing model performance.

关键词： Temperature measurement Analytical models Technological innovation Temperature Accuracy Electricity Biological system modeling

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Empowering Non-Experts: A Web-Based Solution for Collaborative Image Annotation in Machine Learning Models for computer Vision

Empowering Non-Experts: A Web-Based Solution for Collaborati...

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international Multidisciplinary conference on computer and Energy Science (SpliTech)

作者： Pedro Couto Rolando Miragaia João Ramos António Pereira Computer Science and Communications Research Centre Polytechnic University of Leiria Leiria Portugal Computer Science and Communications Research Centre Polytechnic University of Leiria INOV INESC INNOVATION Leiria Portugal

ISBN: (数字)9789532901351

ISBN: (纸本)9798350390797

As the global population is expected to exceed 9.7 billion by 2050, improving the efficiency of food production systems is crucial to achieving food security. This article discusses the issues of growing populations, rising food consumption, and the need for technological revolution in the agricultural sector. Leveraging robotic technology, particularly in environments like orchards and greenhouses, holds promise in enhancing production efficiency. This work proposes a solution harnessing artificial intelligence and computer vision to mitigate labeling errors in image datasets often used for supervised learning models, offering a user-friendly interface for modifying class labels. The proposed system simplifies the annotation process through a simple architecture comprising front-end, back-end, and file storage modules, ensuring accuracy and adaptability, specifically tailored for individuals with agricultural expertise, yet lacking experience in AI or computer Science.

关键词： measurement Training computer vision Adaptation models Accuracy Annotations Computational modeling

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Architecture Design for Rise/Fall Asymmetry Glitch Minimization in Current-Steering DACs

Architecture Design for Rise/Fall Asymmetry Glitch Minimizat...

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IEEE international conference on Electronics, Circuits and systems (ICECS)

作者： Ramin Babaee Shahab Oveis Gharan Martin Bouchard School of EECS University of Ottawa Ottawa ON Canada Ciena Corp. Ottawa ON Canada

ISBN: (数字)9798350377200

ISBN: (纸本)9798350377217

Current-steering digital-to-analog converter (DAC) is a prominent architecture that is commonly used in high-speed applications such as optical communications. One of the shortcomings of this architecture is the output glitches that are input dependent and degrade the dynamic performance of the DAC. We investigate DAC glitches that arise from asymmetry in the fall/rise response of DAC switches. We formulate a glitch metric that defines the overall DAC performance, which is then used to find a novel DAC weighting scheme. Numerical simulations show that the proposed architecture can potentially provide a significant performance advantage compared to the segmented structure.

关键词： measurement Greedy algorithms Optical switches Nonlinear distortion computer architecture Numerical simulation Minimization Optical fiber communication Telecommunications Integrated circuit modeling

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Mitigating Cyberbullying in Social Media: A Deep Contextual Learning Approach for Severity Level Classification in Textual Data

Mitigating Cyberbullying in Social Media: A Deep Contextual ...

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Electronics and Sustainable Communication systems (ICESC), 2020 international conference on

作者： Prashant Agrawal Awanit Kumar Arun Kr. Tripathi Computer Science & Engineering Sangam University Rajasthan India Computer Science & Engineering Sangam University Bhilwara Rajasthan India Department of Computer Applications KIET Group of Institution Delhi-NCR Ghaziabad India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

This research introduces a novel approach that integrates Deep Contextual Learning (DCL), specifically the DCL-256-32 model with an embedding model to accurately classify offense levels within the textual data. The DCL-256-32 model employs a SoftMax function to assign probabilities to distinct severity classes, ranging from critical to negligible. The proposed model incorporates two endpoints: an embedding model for generating semantic representations of input text and a set of pre-trained DCL-256-32 models for predicting offense levels. By averaging these predictions and associating them with humanreadable labels, this study proposes a robust and scalable framework for real-time text analysis. The proposed model demonstrates high performance compared to existing methods, contributing to the advancement of Natural Language Processing (NLP) and classification. This research study offers a practical solution for enhancing digital safety and combating online harassment.

关键词： Text analysis Semantics Cyberbullying Predictive models Natural language processing Data models Real-time systems Distance measurement Safety Context modeling

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Comparison of Full Modulation and Formant-Modulation Methods of Speech Intelligibility measurement

Comparison of Full Modulation and Formant-Modulation Methods...

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IEEE international conference on Methods and systems of Navigation and Motion Control (MSNMC)

作者： Arkadiy Prodeus Oleksandr Dvornyk Anton Naida Maryna Didkovska Acoustic and Multimedia Electronic Systems Department NTUU “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv Ukraine

In this paper, comparing of the full modulation and full formant-modulation methods of speech intelligibility measurement according to the measurements accuracy under the conditions of noise interference has been made. By means of computer modeling of the measuring the speech transmission index (STI), the dependences of the STI estimates errors on the signal-to-noise ratio and on the duration of the test signals were obtained. It is shown that the accuracy of STI estimates is practically the same for both methods. In particular, it is shown that the use of test signals with a duration of 4 s when measuring STI is minimally acceptable and provides an estimation error of 0.03. The use of test signals with a duration of 8 s and 16 s allows reducing the STI estimation error to 0.02 and 0.01, respectively.

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joint Optimization of Wind Farm Layout Considering Yaw Control 8th

Joint Optimization of Wind Farm Layout Considering Yaw Cont...

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8th international conference on Life System modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Wang, Yiqun Jiang, Ruolin Chen, Yuanye Fang, Fang School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789819602247

As wind farms continue to grow in size, minimizing wake losses between turbines in layout design becomes increasingly important. Current wind farm layout strategies often focus on the performance of individual turbines without considering wake interactions. This study introduces a coordinated control approach that considers wake effects and demonstrates an improved yaw control results to reduce the impact of wake interactions. By implementing cooperative yaw control among turbines and using the NSGA-II algorithm, the study establishes a dual optimization problem of minimizing turbine spacing and maximizing power output, ultimately yielding the optimal layout for wind turbines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wind turbines

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