Journal of Electrical Systems and Information Technology最新文献_第6页

Forecasting household energy consumption based on lifestyle data using hybrid machine learning 使用混合机器学习预测基于生活方式数据的家庭能源消耗

Journal of Electrical Systems and Information Technology Pub Date : 2023-09-19 DOI: 10.1186/s43067-023-00104-2

seidu agbor abdul rauf, Adebayo F. Adekoya

{"title":"Forecasting household energy consumption based on lifestyle data using hybrid machine learning","authors":"seidu agbor abdul rauf, Adebayo F. Adekoya","doi":"10.1186/s43067-023-00104-2","DOIUrl":"https://doi.org/10.1186/s43067-023-00104-2","url":null,"abstract":"Abstract Household lifestyle play a significant role in appliance consumption. The overall effects are that, it can be a determining factor in the healthy functioning of the household appliance or its abnormal functioning. The rapid growth in residential consumption has raised serious concerns toward limited energy resource and high electricity pricing. The propose 134% electricity tariffs adjustment by Electricity Company of Ghana (ECG) at the heat of economic hardships caused by Covid-19 has raised serious public agitation in Ghana (west Africa) . The unpredictable lifestyle of residential consumers in an attempt to attain a comfortable lifestyle and the rippling effects of population growth burdens energy demand at the residential sector. This study attempts to identify the lifestyle factors that have great influence on household energy consumption and predict future consumption of the household with mitigating factors to cushion the effects on high consumption. The study is based on lifestyle data using hybrid machine learning. The hybrid model achieved high accuracy (96%) as compared to previous models. The hybrid model performance was evaluated using mean absolute percentage error (MAPE), root mean square error (RMSE) and correlation coefficient (R) metrics.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135061022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classifying blinking and winking EOG signals using statistical analysis and LSTM algorithm 利用统计分析和LSTM算法对眨眼眼电信号进行分类

Journal of Electrical Systems and Information Technology Pub Date : 2023-09-19 DOI: 10.1186/s43067-023-00112-2

Ahmed M. D. E. Hassanein, Ahmed G. M. A. Mohamed, Mohamed A. H. M. Abdullah

{"title":"Classifying blinking and winking EOG signals using statistical analysis and LSTM algorithm","authors":"Ahmed M. D. E. Hassanein, Ahmed G. M. A. Mohamed, Mohamed A. H. M. Abdullah","doi":"10.1186/s43067-023-00112-2","DOIUrl":"https://doi.org/10.1186/s43067-023-00112-2","url":null,"abstract":"Abstract Detection of eye movement types whether the movement of the eye itself or blinking has attracted a lot of recent research. In this paper, one method to detect the type of wink or blink produced by the eye is scrutinized and another method is proposed. We discuss what statistical analysis can teach us about detection of eye movement and propose a method based on long short-term memory (LSTM) networks to detect those types. The statistical analysis is composed of two main steps, namely calculation of the first derivative followed by a digitization step. According to the values of the digitized curve and the duration of the signal, the type of the signal is detected. The success rate reached 86.6% in detection of the movement of the eye when those volunteers are not trained on using our system. However, when they are trained, the detection success rate reached 93.3%. The statistical analysis succeeds in achieving detection of all types of eye movement except one type which is the non-intentional blinking. Although rate of success achieved is high, but as the number of people using this system increases, the error in detection increases that is because it is fixed and not adaptive to changes. However; we learnt from statistical analysis that the first derivative is a very important feature to classify the type of an EOG signal. Next, we propose using the LSTM network to classify EOG signals. The effect of using the first derivative as a feature for identifying the type of EOG signals is discussed. The LSTM algorithm succeeds in detecting the type of EOG signals with a percentage equal to 92% for all types of eye movement.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135059362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Management of Var sources for the reactive power planning problem by oppositional Harris Hawk optimizer 基于Harris Hawk优化器的无功规划问题无功源管理

Journal of Electrical Systems and Information Technology Pub Date : 2023-09-19 DOI: 10.1186/s43067-023-00111-3

Swetha Shekarappa G, Sheila Mahapatra, Saurav Raj

引用次数: 0

Allocation of synchronized phasor measurement units for power grid observability using advanced binary accelerated particle swarm optimization approach 基于先进二进制加速粒子群优化方法的同步相量测量单元分配

Journal of Electrical Systems and Information Technology Pub Date : 2023-09-04 DOI: 10.1186/s43067-023-00110-4

R. Babu, S. Raj, Sheila Mahapatra

引用次数: 0

GMA: Gap Imputing Algorithm for time series missing values GMA:时间序列缺失值的间隙估计算法

Journal of Electrical Systems and Information Technology Pub Date : 2023-08-30 DOI: 10.1186/s43067-023-00094-1

Abd Alhamid Rabia Khattab, Nada M. Elshennawy, Mahmoud Fahmy

引用次数: 0

Healthcare predictive analytics using machine learning and deep learning techniques: a survey 使用机器学习和深度学习技术的医疗保健预测分析:调查

Journal of Electrical Systems and Information Technology Pub Date : 2023-08-29 DOI: 10.1186/s43067-023-00108-y

Mohammed Badawy, Nagy Ramadan, H. Hefny

引用次数: 2

The robust H∞documentclass[12pt]{minimal} usepackage{amsmath} usepackage{wasysym} usepackage{amsfonts} usepackage{amssymb} usepackage{amsbsy} usepackage{mathrsfs} usepackage{upgreek} setlength{oddsidemargin}{-69pt} begin{document}$$H_{infty }$$end{document} control of stochastic neutral The robust H∞documentclass[12pt]{minimal} usepackage{amsmath} usepackage{wasysym} usepackage{amsfonts} usepackage{amssymb} usepackage{amsbsy} usepackage{mathrsfs} usepackage{upgreek} setlength{oddsidemargin}{-69pt} begin{document}$$H_{infty }$$end{document} control of stochastic neutral

Journal of Electrical Systems and Information Technology Pub Date : 2023-08-25 DOI: 10.1186/s43067-023-00106-0

Rajagounder Ravi Kumar, R. Naveen, V. Anandhi, A. Sudha

引用次数: 0

Voltage-controlled oscillator based analog-to-digital converter in 130-nm CMOS for biomedical applications 基于压控振荡器的130纳米CMOS模数转换器，用于生物医学应用

Journal of Electrical Systems and Information Technology Pub Date : 2023-08-21 DOI: 10.1186/s43067-023-00109-x

Dina M. Ellaithy

引用次数: 0

Real-time fault diagnosis system for electrical panel using embedded systems 基于嵌入式系统的电气面板实时故障诊断系统

Journal of Electrical Systems and Information Technology Pub Date : 2023-07-28 DOI: 10.1186/s43067-023-00107-z

Parsa Parsafar, Payam Qaderi Baban

引用次数: 0

Design of PFC converter with stand-alone inverter for microgrid applications 微电网中PFC变换器与独立逆变器的设计

Journal of Electrical Systems and Information Technology Pub Date : 2023-07-21 DOI: 10.1186/s43067-023-00105-1

Sujith Boddu, Arnab Ghosh

引用次数: 0