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Threefold Optimized Forecasting of Electricity Consumption in Higher Education Institutions

Computers, Materials & Continua 2022年第11期73卷 2351-2370页

作者： Majida Kazmi Hashim Raza Khan Lubaba Mohammad Hashir Bin Khalid Saad Ahmed Qazi Faculty of Electrical and Computer Engineering NED University of Engineering and TechnologyKarachi75270Pakistan neurocomputation lab National Center of Artificial IntelligenceKarachi75270Pakistan

Energy management benefits both consumers and utility companiesalike. Utility companies remain interested in identifying and reducing energywaste and theft, whereas consumers’ interest remain in lowering their energyexpenses. A large supply-demand gap of over 6 GW exists in Pakistan asreported in 2018. Reducing this gap from the supply side is an expensiveand complex task. However, efficient energy management and distributionon demand side has potential to reduce this gap economically. Electricityload forecasting models are increasingly used by energy managers in takingreal-time tactical decisions to ensure efficient use of resources. Advancementin Machine-learning (ML) technology has enabled accurate forecasting ofelectricity consumption. However, the impact of computation cost affordedby these ML models is often ignored in favour of accuracy. This studyconsiders both accuracy and computation cost as concurrently significantfactors because together they shape the technology environment as well ascreate economic impact. Thus, a three-fold optimized load forecasting modelis proposed which includes (1) application specific parameters selection, (2)impact of different dataset granularities and (3) implementation of specificdata preparation. It deploys and compares the widely used back-propagationArtificial Neural Network (ANN) and Random Forest (RF) models for theprediction of electricity consumption of buildings within a university. In addition to the temporal and historical power consumption date as input parameters, the study also embeds weather data as well as university operationalcalendars resulting in improved performance. The outcomes are indicativethat the granularity i.e. the scale of details in data, and set of reduced and fullinput parameters impact performance accuracies differently for ANN and RFmodels. Experimental results show that overall RF model performed betterboth in terms of accuracy as well as computational time for a 1-min, 15-minand 1-h dataset

关键词： Electricity forecasting short term higher educational institution artificial neural network random forest accuracy computational time

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Improving Routine Immunization Coverage Through Optimally Designed Predictive Models

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Computers, Materials & Continua 2022年第1期70卷 375-395页

作者： Fareeha Sameen Abdul Momin Kazi Majida Kazmi Munir A Abbasi Saad Ahmed Qazi Lampros K Stergioulas Faculty of Electrical and Computer Engineering NED University of Engineering and TechnologyKarachi75270Pakistan Department of Pediatrics and Child Health Aga Khan UniversityKarachi74800Pakistan Surrey Business School University of SurreyGuildfordGU27XHUnited Kingdom neurocomputation lab National Centre of Artificial IntelligenceNED University of Engineering&TechnologyKarachi75270Pakistan Faculty of IT and Design The Hague University of Applied Sciences2521 ENThe HagueThe Netherlands

Routine immunization(RI)of children is the most effective and timely public health intervention for decreasing child mortality rates around the *** being a low-and-middle-income-country(LMIC)has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases(VPDs).For improving RI coverage,a critical need is to establish potential RI defaulters at an early stage,so that appropriate interventions can be targeted towards such populationwho are identified to be at risk of missing on their scheduled vaccine *** this paper,a machine learning(ML)based predictivemodel has been proposed to predict defaulting and non-defaulting children on upcoming immunization visits and examine the effect of its underlying contributing *** predictivemodel uses data obtained from Paigham-e-Sehat study having immunization records of 3,113 *** design of predictive model is based on obtaining optimal results across accuracy,specificity,and sensitivity,to ensure model outcomes remain practically relevant to the problem *** optimization of predictive model is obtained through selection of significant features and removing data *** machine learning algorithms were applied for prediction of defaulting children for the next immunization *** results showed that the random forest model achieves the optimal accuracy of 81.9%with 83.6%sensitivity and 80.3%*** main determinants of vaccination coverage were found to be vaccine coverage at birth,parental education,and socioeconomic conditions of the defaulting *** information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.

关键词： Machine learning predictive models routine immunization vaccine coverage pakistan optimization SMOTE

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An Efficient IIoT-Based Smart Sensor Node for Predictive Maintenance of Induction Motors

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Computer Systems Science & Engineering 2023年第10期47卷 255-272页

作者： Majida Kazmi Maria Tabasum Shoaib Arshad Aziz Hashim Raza Khan Saad Ahmed Qazi Faculty of Electrical and Computer Engineering NED University of Engineering and TechnologyKarachi75270Pakistan neurocomputation lab National Center of Artificial IntelligenceKarachi75270Pakistan Department of Electrical Engineering PNECNational University of Sciences and Technology(NUST)Karachi75350Pakistan

Predictive maintenance is a vital aspect of the industrial sector,and the use of Industrial Internet of Things(IIoT)sensor nodes is becoming increasingly popular for detecting motor faults and monitoring motor *** integrated approach for acquiring,processing,and wirelessly transmitting a large amount of data in predictive maintenance applications remains a significant *** study presents an IIoT-based sensor node for industrial *** sensor node is designed to acquire vibration data on the radial and axial axes of the motor and utilizes a hybrid approach for efficient data processing via edge and cloud *** initial step of signal processing is performed on the node at the edge,reducing the burden on a centralized cloud for processing data from multiple *** proposed architecture utilizes the lightweight Message Queue Telemetry Transport(MQTT)communication protocol for seamless data transmission from the node to the local and main *** broker’s bridging allows for data backup in case of connection *** proposed sensor node is rigorously tested on a motor testbed in a laboratory setup and an industrial setting in a rice industry for validation,ensuring its performance and accuracy in real-world industrial *** data analysis and results from both testbed and industrial motors were discussed using vibration analysis for identifying *** proposed sensor node is a significant step towards improving the efficiency and reliability of industrial motors through realtime monitoring and early fault detection,ultimately leading to minimized unscheduled downtime and cost savings.

关键词： IIoT sensor node condition monitoring fault classification predictive maintenance MQTT

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