基于优化复值时空图卷积神经网络的物联网系统中的太阳能预测

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Atul B. Kathole , Devyani Jadhav , Kapil Netaji Vhatkar , Swapnaja Amol , Nisarg Gandhewar
{"title":"基于优化复值时空图卷积神经网络的物联网系统中的太阳能预测","authors":"Atul B. Kathole ,&nbsp;Devyani Jadhav ,&nbsp;Kapil Netaji Vhatkar ,&nbsp;Swapnaja Amol ,&nbsp;Nisarg Gandhewar","doi":"10.1016/j.knosys.2024.112400","DOIUrl":null,"url":null,"abstract":"<div><p>The accurate prediction of solar energy generation is significant for efficient energy management in Internet of Things (IoT) devices. However, current forecasting models frequently fail to account for the dynamic nature of weather conditions. Also, traditional methods often have limited accuracy and scalability. This paper proposes Solar Energy Prediction in IoT system based optimized Complex-Valued Spatio-Temporal Graph Convolutional Neural Network (SEP-CVSGCNN-IoT) to overcome the limitations of existing models. Initially, the data are collected from solar panel and weather forecast. The collected data is given to the pre-processing using Data-Adaptive Gaussian Average Filtering (DAGAF) to remove the unwanted data and replace missing data. The pre-processed data is given into Nutcracker Optimization (NCO) algorithm for selecting optimal features. Then, the selected features are given to the Complex-Valued Spatio-Temporal Graph Convolutional Neural Network (CVSGCNN) for solar energy prediction. Finally, Dipper Throated Optimization Algorithm (DTOA) is proposed to enhance the weight parameter of CVSGCNN classifier, which precisely predicts solar energy in IoT. The proposed SEP-CVSGCNN-IoT method provides 18.46%, 26.34, 15.69 and 20.84% higher accuracy and 18.24%, 23.77, 24.34 and 16.29% lower mean absolute error when analyzed with existing techniques, such as deep learning enhanced solar energy prediction and AI-driven IoT (DL-ESEF-AI), towards efficient renewable energy prediction using deep learning (TEE-REP-DL), a new deep learning method for effectual forecasting of short-term PV energy production (DL-EF-SPEP) and metaheuristic-dependent hyper parameter tuning for recurrent deep learning: application to the solar energy generation prediction (HT-RDL-PSEG) respectively.</p></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2000,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Solar energy prediction in IoT system based optimized complex-valued spatio-temporal graph convolutional neural network\",\"authors\":\"Atul B. Kathole ,&nbsp;Devyani Jadhav ,&nbsp;Kapil Netaji Vhatkar ,&nbsp;Swapnaja Amol ,&nbsp;Nisarg Gandhewar\",\"doi\":\"10.1016/j.knosys.2024.112400\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The accurate prediction of solar energy generation is significant for efficient energy management in Internet of Things (IoT) devices. However, current forecasting models frequently fail to account for the dynamic nature of weather conditions. Also, traditional methods often have limited accuracy and scalability. This paper proposes Solar Energy Prediction in IoT system based optimized Complex-Valued Spatio-Temporal Graph Convolutional Neural Network (SEP-CVSGCNN-IoT) to overcome the limitations of existing models. Initially, the data are collected from solar panel and weather forecast. The collected data is given to the pre-processing using Data-Adaptive Gaussian Average Filtering (DAGAF) to remove the unwanted data and replace missing data. The pre-processed data is given into Nutcracker Optimization (NCO) algorithm for selecting optimal features. Then, the selected features are given to the Complex-Valued Spatio-Temporal Graph Convolutional Neural Network (CVSGCNN) for solar energy prediction. Finally, Dipper Throated Optimization Algorithm (DTOA) is proposed to enhance the weight parameter of CVSGCNN classifier, which precisely predicts solar energy in IoT. The proposed SEP-CVSGCNN-IoT method provides 18.46%, 26.34, 15.69 and 20.84% higher accuracy and 18.24%, 23.77, 24.34 and 16.29% lower mean absolute error when analyzed with existing techniques, such as deep learning enhanced solar energy prediction and AI-driven IoT (DL-ESEF-AI), towards efficient renewable energy prediction using deep learning (TEE-REP-DL), a new deep learning method for effectual forecasting of short-term PV energy production (DL-EF-SPEP) and metaheuristic-dependent hyper parameter tuning for recurrent deep learning: application to the solar energy generation prediction (HT-RDL-PSEG) respectively.</p></div>\",\"PeriodicalId\":49939,\"journal\":{\"name\":\"Knowledge-Based Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.2000,\"publicationDate\":\"2024-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Knowledge-Based Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0950705124010347\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knowledge-Based Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0950705124010347","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

准确预测太阳能发电量对物联网(IoT)设备的高效能源管理意义重大。然而,目前的预测模型往往无法考虑天气条件的动态性质。而且,传统方法的准确性和可扩展性往往有限。本文提出了基于优化的复值时空图卷积神经网络(SEP-CVSGCNN-IoT)的物联网系统太阳能预测,以克服现有模型的局限性。最初,我们从太阳能电池板和天气预报中收集数据。收集到的数据使用数据自适应高斯平均滤波(DAGAF)进行预处理,以去除不需要的数据并替换缺失数据。预处理后的数据进入胡桃钳优化(NCO)算法,以选择最佳特征。然后,将所选特征输入复值时空图卷积神经网络(CVSGCNN),用于太阳能预测。最后,提出了北斗七星优化算法(DTOA)来增强 CVSGCNN 分类器的权重参数,从而精确预测物联网中的太阳能。所提出的 SEP-CVSGCNN-IoT 方法的准确率分别提高了 18.46%、26.34%、15.69% 和 20.84%,平均绝对误差分别降低了 18.24%、23.77%、24.34% 和 16.29%。在与现有技术(如深度学习增强型太阳能预测和人工智能驱动的物联网(DL-ESEF-AI)、利用深度学习实现高效可再生能源预测(TEE-REP-DL)、用于有效预测短期光伏发电量的新型深度学习方法(DL-EF-SPEP)和用于递归深度学习的元启发式超参数调优:应用于太阳能发电预测(HT-RDL-PSEG))进行分析时,所提出的SEP-CVSGCNN-IoT方法的准确率分别提高了18.46%、26.34%、15.69%和20.84%,平均绝对误差分别降低了18.24%、23.77%、24.34%和16.29%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Solar energy prediction in IoT system based optimized complex-valued spatio-temporal graph convolutional neural network

The accurate prediction of solar energy generation is significant for efficient energy management in Internet of Things (IoT) devices. However, current forecasting models frequently fail to account for the dynamic nature of weather conditions. Also, traditional methods often have limited accuracy and scalability. This paper proposes Solar Energy Prediction in IoT system based optimized Complex-Valued Spatio-Temporal Graph Convolutional Neural Network (SEP-CVSGCNN-IoT) to overcome the limitations of existing models. Initially, the data are collected from solar panel and weather forecast. The collected data is given to the pre-processing using Data-Adaptive Gaussian Average Filtering (DAGAF) to remove the unwanted data and replace missing data. The pre-processed data is given into Nutcracker Optimization (NCO) algorithm for selecting optimal features. Then, the selected features are given to the Complex-Valued Spatio-Temporal Graph Convolutional Neural Network (CVSGCNN) for solar energy prediction. Finally, Dipper Throated Optimization Algorithm (DTOA) is proposed to enhance the weight parameter of CVSGCNN classifier, which precisely predicts solar energy in IoT. The proposed SEP-CVSGCNN-IoT method provides 18.46%, 26.34, 15.69 and 20.84% higher accuracy and 18.24%, 23.77, 24.34 and 16.29% lower mean absolute error when analyzed with existing techniques, such as deep learning enhanced solar energy prediction and AI-driven IoT (DL-ESEF-AI), towards efficient renewable energy prediction using deep learning (TEE-REP-DL), a new deep learning method for effectual forecasting of short-term PV energy production (DL-EF-SPEP) and metaheuristic-dependent hyper parameter tuning for recurrent deep learning: application to the solar energy generation prediction (HT-RDL-PSEG) respectively.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信