Kuldeep Sharma¹, Arun Malik¹, Isha Batra¹, A. S. M. Sanwar Hosen², Md Abdul Latif Sarker³, Dong Seog Han^4,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5049-5072, 2023, DOI:10.32604/cmc.2023.035638

Abstract Electric smart grids enable a bidirectional flow of electricity and information among power system assets. For proper monitoring and controlling of power quality, reliability, scalability and flexibility, there is a need for an environmentally friendly system that is transparent, sustainable, cost-saving, energy-efficient, agile and secure. This paper provides an overview of the emerging technologies behind smart grids and the internet of things. The dependent variables are identified by analyzing the electricity consumption patterns for optimal utilization and planning preventive maintenance of their legacy assets like power distribution transformers with real-time parameters to ensure an uninterrupted and reliable power supply. In… More >

Vlastimil Slany¹, Petr Koudelka^1,*, Eva Krcalova¹, Jan Jobbagy², Lukas Danys³, Rene Jaros³, Zdenek Slanina³, Michal Prauzek³, Radek Martinek³

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5201-5217, 2022, DOI:10.32604/cmc.2022.021349

Abstract The massive development of internet of things (IoT) technologies is gaining momentum across all areas of their possible deployment—spanning from Industry 4.0 to eHealth, smart city, agriculture or waste management. This ongoing development is further pushed forward by the gradual deployment of 5G networks. With 5G capable smart devices, it will be possible to transfer more data with shorter latency thereby resulting in exciting new use cases such as Massive IoT. Massive-IoT (low-power wide area network-LPWAN) enables improved network coverage, long device operational lifetime and a high density of connections. Despite all the advantages of massive-IoT technology, there are certain… More >

Taher M. Ghazal^1,2, Sajida Noreen³, Raed A. Said⁴, Muhammad Adnan Khan^5,*, Shahan Yamin Siddiqui^3,6, Sagheer Abbas³, Shabib Aftab³, Munir Ahmad³

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 539-553, 2022, DOI:10.32604/iasc.2022.019658

Abstract The usage of IoT-based smart meter in electric power consumption shows a significant role in helping the users to manage and control their electric power consumption. It produces smooth communication to build equitable electric power distribution for users and improved management of the entire electric system for providers. Machine learning predicting algorithms have been worked to apply the electric efficiency and response of progressive energy creation, transmission, and consumption. In the proposed model, an IoT-based smart meter uses a support vector machine and deep extreme machine learning techniques for professional energy management. A deep extreme machine learning approach applied to… More >

Isma Farah Siddiqui¹, Scott Uk-Jin Lee^2,*, Asad Abbas³

Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 107-119, 2020, DOI:10.31209/2019.100000132

Abstract The issue of battery drainage in the gigantic smart meters network such as semantic-aware IoT-enabled smart meter has become a serious concern in the smart grid framework. The grid core migrates existing tabular datasets i.e., Relational data to semantic-aware tuples in its Resource Description Framework (RDF) format, for effective integration among multiple components to work aligned with IoT. For this purpose, WWW Consortium (W3C) recommends two specifications as mapping languages. However, both specifications use entire RDB schema to generate data transformation mapping patterns and results large quantity of unnecessary transformation. As a result, smart meters use huge computing resources, maximum… More >

Weilie Liu, Jialing He, Meng Li, Rui Jin, Jingjing Hu, Zijian Zhang

Intelligent Automation & Soft Computing, Vol.25, No.3, pp. 585-593, 2019, DOI:10.31209/2019.100000113

Abstract Smart energy disaggregation is receiving increasing attention because it can be used to save energy and mine consumer's electricity privacy by decomposing aggregated meter readings. Many smart energy disaggregation schemes have been proposed; however, the accuracy and efficiency of these methods need to be improved. In this work, we consider a supervised energy disaggregation method which initially learns the power consumption of each appliance and then disaggregates meter readings using the previous learning result. In this study, we improved the fast search and find of density peaks clustering algorithm to cluster appliance power signals twice to learn appliance feature matrices.… More >