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data-driven analysis and machine learning for energy prediction in distributed photovoltaic generation plants: A case study in Queensland, Australia

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ENERGY REPORTS 2022年 8卷 745-751页

作者： Ramos, Lucas Colnago, Marilaine Casaca, Wallace Sao Paulo State Univ UNESP Dept Energy Engn BR-19274000 Rosana Brazil

Understanding patterns and energy-related data in photovoltaic systems is one of the key tasks in energy generation and distribution. In fact, the use of data-driven tools and predictive learning models can support the government, power regulatory agencies, and the energy industry in improving their decision-making and operational activities. Bering this in mind, this paper presents a case study of data-driven analysis and machine learning to forecast the energy charge in the distributed photovoltaic power grid of Queensland, in Australia. Our analysis relies on a freely, open energy tracking platform and the design of three machine learning approaches built on the basis of Random Forest, Support Vector machines, and Gradient Boosting methods. Experimental results with real data showed that the trained models allow for very consistent predictions while reaching a high forecasting accuracy (around 95%-93% in Generated-Exported prediction, respectively). Moreover, it was found that the Gradient Boosting-based model ensures robust behavior and low prediction errors, as endorsed by quality validation metrics. Another technical aspect observed is that the variables artificially created to boost the models substantially improve the post-analysis and overall accuracy of the results. (C) 2021 The Author(s). Published by Elsevier Ltd.

关键词： Photovoltaics Distributed energy machine learning data-driven models

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Assessment of the Relationship Between Attribute Coding and the Interpretability of machine learning Models: An Analysis in the Context of Children and Adolescents with Depression

Assessment of the Relationship Between Attribute Coding and ...

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17th international Joint conference on Biomedical engineering Systems and Technologies, BIOSTEC 2024

作者： Nascimento, Ludmila B. S. Balbino, Marcelo de S. Teodoro, Maycoln L. M. Nobre, Cristiane N. Institute of Exact Sciences and Informatics Pontifical Catholic University of Minas Gerais Dom José Gaspar Belo Horizonte Brazil Department of Computing and Civil Construction Federal Center for Technological Education of Minas Gerais Belo Horizonte Brazil Department of Psychology Federal University of Minas Gerais Belo Horizonte Brazil

Depression is a global public health challenge that affects approximately 300 million people. Artificial Intelligence and machine learning have revolutionized the healthcare sector, allowing the development of models to diagnose depression. Tabular data, shared in healthcare, requires pre processing, including encoding categorical attributes into numeric values, as many machine learning algorithms only support numeric data. This study aims to investigate different coding methods for non-ordinal nominal categorical attributes in a data set related to depression in children and adolescents suffering from Major Depressive Disorder (MDD).The comparison results revealed that the XGBoost algorithm with the Hash Encoding, Customized One Hot, Frequency, and Dummy coding techniques were more effective for the analyzed data set. However, not all of these encodings are interpretable. These results provide significant insights, highlighting the importance of choosing appropriate coding methods to improve the accuracy of machine learning models and the inter pretability of these models in healthcare. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Adversarial machine learning

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Stock Price Prediction using machine learning

Stock Price Prediction using Machine Learning

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2022 international conference on Electronics and Renewable Systems, ICEARS 2022

作者： Varaprasad, B.N. Kundan Kanth, Ch. Jeevan, G. Chakravarti, Y. Kalyan Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India Velagapudi Ramakrishna Siddhartha Engineering College Department of Information Technology Vijayawada India

ISBN: (纸本)9781665484251

In the current era stock price prediction plays a key role for prediction of future data with respect to training the past data by using machine learning or deep learning technologies. Building a model and then passing the past data as input that is as training data to the model based on the results acquired need to consider an algorithm which gives better accuracy and response time and segmentation. In this paper for estimating the stock values we are considering LSTM and Regression models of machine learning. Factors considered are opening values of stock;closing values of stock, lower and higher values of stock and volume. © 2022 IEEE.

关键词： Forecasting

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Crop Yield Predictions Based on machine learning 23rd

Crop Yield Predictions Based on Machine Learning

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2022 international Association for Hydraulic Environmental engineering and Research Asia Pacific Division

作者： Arunya, K. G. Krishnaveni, M. Anna Univ Ctr Water Resources Chennai Tamil Nadu India Anna Univ Inst Ocean Management Chennai Tamil Nadu India

ISBN: (纸本)9789819760084;9789819760091

machine learning (ML) is an important tool in decision-making and many ML algorithms are used in crop yield prediction. ML algorithm predictions are based on various dependent factors. In this study, climate variabilities such as precipitation and crop yield are considered as the dependent factors. This paper shows a micro-level study which concentrates on the Paravanar River Basin which is situated on Cuddalore District, Tamil Nadu, India. There are more than 40 crops grown in this region and the main source of irrigation is the north-east monsoon rainfall. Since, the hypothesis is tested between the annual rainfall and the crop yield, the crops which are cultivated throughout the year are chosen for analysis. They are cashew nut, coriander, sugarcane, sweet potato and turmeric. The PERSIANN-CDR data from National Centres for Environmental Information-National Oceanic and Atmospheric Administration was used as the precipitation data and the agriculture crop production data was downloaded from Ministry of Agriculture and Farmers Welfare of India. The datasets were tested using the following ML algorithms logistic regression, decision tree classifier, random forest classifier and XGBoost classifier. Implementation of ML-derived results can only be done through participatory approaches. Therefore, participatory rural appraisal was done with the farmers and the villagers to assess the willingness of implementation and it shows positive results. Hence the main aim of the study tests the hypotheses on dependency of crop yield on precipitation on a regional scale.

关键词： machine learning Precipitation Crop yield Participatory rural appraisal

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Improving Fair Training under Correlation Shifts 40

Improving Fair Training under Correlation Shifts

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40th international conference on machine learning

作者： Roh, Yuji Lee, Kangwook Whang, Steven Euijong Suh, Changho Korea Adv Inst Sci & Technol Dept Elect Engn Daejeon South Korea Univ Wisconsin Madison Dept Elect & Comp Engn Madison WI USA

Model fairness is an essential element for Trustworthy AI. While many techniques for model fairness have been proposed, most of them assume that the training and deployment data distributions are identical, which is often not true in practice. In particular, when the bias between labels and sensitive groups changes, the fairness of the trained model is directly influenced and can worsen. We make two contributions for solving this problem. First, we analytically show that existing in-processing fair algorithms have fundamental limits in accuracy and group fairness. We utilize the notion of correlation shifts between labels and groups, which can explicitly capture the change of the above bias. Second, we propose a novel pre-processing step that samples the input data to reduce correlation shifts and thus enables the in-processing approaches to overcome their limitations. We formulate an optimization problem for adjusting the data ratio among labels and sensitive groups to reflect the shifted correlation. A key benefit of our approach lies in decoupling the roles of pre- and in-processing approaches: correlation adjustment via pre-processing and unfairness mitigation on the processed data via inprocessing. Experiments show that our framework effectively improves existing in-processing fair algorithms w.r.t. accuracy and fairness, both on synthetic and real datasets.

关键词： machine learning

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Join Path-Based data Augmentation for Decision Trees 38

Join Path-Based Data Augmentation for Decision Trees

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38th IEEE international conference on data engineering (ICDE)

作者： Ionescu, Andra Hai, Rihan Fragkoulis, Marios Katsifodimos, Asterios Delft Univ Technol Delft Netherlands Delivery Hero SE Berlin Germany Delft Univ Technol Delft Netherlands

ISBN: (纸本)9781665481045

machine learning (ML) applications require high-quality datasets. Automated data augmentation techniques can help increase the richness of training data, thus increasing the ML model accuracy. Existing solutions focus on efficiency and ML model accuracy but do not exploit the richness of dataset relationships. With relational data, the challenge lies in identifying join paths that best augment a feature table to increase the performance of a model. In this paper we propose a two-step, automated data augmentation approach for relational data that involves: (i) enumerating join paths of various lengths given a base table and (ii) ranking the join paths using filter methods for feature selection. We show that our approach can improve prediction accuracy and reduce runtime compared to the baseline approach.

关键词： Runtime conferences Training data machine learning Feature extraction data engineering data models

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Introduction to the DDDAS2022 conference Infosymbiotics/Dynamic data Driven Applications Systems 4th

Introduction to the DDDAS2022 Conference Infosymbiotics/Dyna...

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4th international conference on Dynamic data Driven Applications Systems, DDDAS 2022

作者： Blasch, Erik Darema, Frederica MOVEJ Analytics FairbornOH United States InfoSymbiotic Systems Society BethesdaMD United States

ISBN: (纸本)9783031526695

The 4th international DDDAS 2022 conference, convened on October 6–10, featured presentations on Dynamic data Driven Applications Systems (DDDAS)-based approaches and capabilities, in a wide set of areas, with an overarching theme of "InfoSymbiotics/DDDAS for human, environmental and engineering sustainment". The topics included aerospace mechanics and space systems, networked communications and autonomy, biomedical and environmental systems, and featured recent techniques in generative Artificial Intelligence, theoretical machine learning, and dynamic Digital Twins. Capturing the tenets of the DDDAS paradigm across these areas, solutions were presented to address challenges in systems-of-systems’ approaches providing analysis, assessments and enhanced capabilities in the presence of complex and big data. The conference comprised of the main track that featured 31 plenary presentations of peer reviewed papers, five keynotes, an invited talk, and a panel on wildfires monitoring. In conjunction with the main track of the DDDAS conference, a Workshop on Climate and Life, Earth, Planets (CLEPs) was conducted, which featured 20 presentations on environmental challenges and a panel on Seismic and Nuclear Explosion monitoring. In addition to the papers of the plenary presentations in the main track of the conference, the DDDAS2022 Proceedings feature an overview of the conference, a synopsis of the main-track papers, and summaries of the keynotes and the wildfires panels followed by corresponding contributed papers by the the speakers in these sessions. Additional information and archival materials, including the presentations’ slides and recordings, are available in the DDDAS website: ***. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Fires

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Analysis of ultimate bearing capacity of foundations based on machine learning

Analysis of ultimate bearing capacity of foundations based o...

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2022 international conference on Intelligent Transportation, Big data and Smart City, ICITBS 2022

作者： Wu, Yicheng Peng, Tianbo Tongji University College of Civil Engineering Shanghai200092 China Tongji University State Key Laboratory of Disaster Reduction in Civil Engineering Shanghai200092 China

ISBN: (纸本)9781665497213

This paper presents an analysis method of ultimate bearing capacity of foundations based on machine learning. Firstly, based on the clustering algorithm of machine learning, the numerical results are processed to obtain the failure mode of the foundation under the limit state, and the equilibrium solution of the ultimate bearing capacity of shallow foundation and caisson foundation is deduced. Then, the numerical solution and equilibrium solution of the ultimate bearing capacity of shallow foundation and caisson foundation are compared with Prandtl's theoretical solution and centrifugal model test results respectively;Finally, the correctness of foundation numerical simulation method and the effectiveness of machine learning clustering algorithm in finding foundation failure mode under vertical load are verified. © 2022 IEEE.

关键词： Foundations

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Automating Accessibility Compliance: Leveraging machine learning to Analyze Developer Challenges with WCAG Guidelines 8

Automating Accessibility Compliance: Leveraging Machine Lear...

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8th international conference on data Science and machine learning Applications, CDMA 2025

作者： Alghamdi, Asmaa Mansour Aljedaani, Wajdi Ludi, Stephanie Javed, Yasir University of North Texas United States Jazan University Saudi Arabia Saudi Authority for Data and Artificial Intelligence Saudi Arabia Prince Sultan University Saudi Arabia

ISBN: (纸本)9798331539696

The prevailing strategy for ensuring web application accessibility relies heavily on adherence to established guidelines, particularly the Web Content Accessibility Guidelines (WCAG). However, the process of interpreting and implementing these guidelines presents significant challenges for developers, which are often compounded by the time-consuming nature of manual analysis required to ensure compliance. Despite the critical importance of accessibility, there remains limited exploration of these challenges within the development process. In this paper, we aim to address these challenges by leveraging machine learning techniques to automatically classify and analyze accessibility-related issues discussed by developers on Stack Overflow, with a focus on understanding their connection to WCAG 2.2 guidelines. To achieve this, we analyzed 5,644 developers' posts related to web accessibility collected from Stack Overflow between 2008 and 2022. These posts were categorized according to the principles of WCAG 2.2 using advanced machine learning models, including Random Forest, Support Vector machine (SVM), Gradient Boosting, AdaBoost, and Multi-Layer Perceptron (MLP). The results highlight a strong emphasis among developers on addressing complex, non-linear accessibility issues. Our analysis demonstrated that Random Forest and Gradient Boosting models were most effective, achieving accuracy rates of 0.987 and 0.986, respectively. These findings underscore the challenges developers face in aligning with accessibility standards, particularly in areas such as time-based media customization and screen reader configuration, and the potential for machine learning to support more efficient and accurate compliance with accessibility guidelines. © 2025 IEEE.

关键词： Adversarial machine learning

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On the Artificial Intelligence for the Sea State Forecast Along the Northern Sea Route

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international JOURNAL OF OFFSHORE AND POLAR engineering 2022年第4期32卷 411-417页

作者： Buinyi, Aleksei Zhdanova, Ekaterina Evdokimov, Aleksandr Sukhikh, Natalia Marine Informat Technol Moscow Russia

Nowadays, the problem of short- and long-term forecasts in the Arctic region becomes more important for global logistics and engineering. Here, we present our first findings on the application of machine learning (ML) methods to oceanographic data. The Barents Sea is the region of our interest, and we focus on the analysis of wind waves and surface currents. We applied several ML models to simulate observed time series and obtained short- and long-term forecasts. The Long Short Time Memory model and XGBoost algorithms showed better results in fitting the observed curve.

关键词： machine learning wind waves currents prediction SARIMA LSTM XGBoost

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