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  • Open Access


    Technologies Behind the Smart Grid and Internet of Things: A System Survey

    Kuldeep Sharma1, Arun Malik1, Isha Batra1, A. S. M. Sanwar Hosen2, Md Abdul Latif Sarker3, Dong Seog Han4,*

    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 >

  • Open Access


    PoQ-Consensus Based Private Electricity Consumption Forecasting via Federated Learning

    Yiqun Zhu1, Shuxian Sun1, Chunyu Liu1, Xinyi Tian1, Jingyi He2, Shuai Xiao2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 3285-3297, 2023, DOI:10.32604/cmes.2023.026691

    Abstract With the rapid development of artificial intelligence and computer technology, grid corporations have also begun to move towards comprehensive intelligence and informatization. However, data-based informatization can bring about the risk of privacy exposure of fine-grained information such as electricity consumption data. The modeling of electricity consumption data can help grid corporations to have a more thorough understanding of users’ needs and their habits, providing better services for users. Nevertheless, users’ electricity consumption data is sensitive and private. In order to achieve highly efficient analysis of massive private electricity consumption data without direct access, a blockchain-based federated learning method is proposed… More >

  • Open Access


    RankXGB-Based Enterprise Credit Scoring by Electricity Consumption in Edge Computing Environment

    Qiuying Shen1, Wentao Zhang1, Mofei Song2,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 197-217, 2023, DOI:10.32604/cmc.2023.036365

    Abstract With the rapid development of the internet of things (IoT), electricity consumption data can be captured and recorded in the IoT cloud center. This provides a credible data source for enterprise credit scoring, which is one of the most vital elements during the financial decision-making process. Accordingly, this paper proposes to use deep learning to train an enterprise credit scoring model by inputting the electricity consumption data. Instead of predicting the credit rating, our method can generate an absolute credit score by a novel deep ranking model–ranking extreme gradient boosting net (rankXGB). To boost the performance, the rankXGB model combines… More >

  • Open Access


    Research on Electricity Consumption Model of Library Building Based on Data Mining

    Jiaming Dou1, Hongyan Ma1,2,3,*, Rong Guo1

    Energy Engineering, Vol.119, No.6, pp. 2407-2429, 2022, DOI:10.32604/ee.2022.019654

    Abstract With the exponential development of Chinese population, the massive energy consumption of buildings has recently become an interest subject. Although much research has been conducted on residential buildings, heating ventilation and air conditioning (HVAC), little research has been conducted on the relationship between student’s behavior, campus buildings, and their subsystems. Using classical seasonal decomposition, hierarchical clustering, and apriori algorithm, this paper aims to provide an empirical model for consumption data in campus library. Smart meter data from a library in Beijing, China, is adopted in this paper. Building electricity consumption patterns are investigated on an hourly/daily/monthly basis. According to the… More >

  • Open Access


    Threefold Optimized Forecasting of Electricity Consumption in Higher Education Institutions

    Majida Kazmi1,*, Hashim Raza Khan1,2, Lubaba2, Mohammad Hashir Bin Khalid2, Saad Ahmed Qazi1,2

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2351-2370, 2022, DOI:10.32604/cmc.2022.026265

    Abstract Energy management benefits both consumers and utility companies alike. Utility companies remain interested in identifying and reducing energy waste and theft, whereas consumers’ interest remain in lowering their energy expenses. A large supply-demand gap of over 6 GW exists in Pakistan as reported in 2018. Reducing this gap from the supply side is an expensive and complex task. However, efficient energy management and distribution on demand side has potential to reduce this gap economically. Electricity load forecasting models are increasingly used by energy managers in taking real-time tactical decisions to ensure efficient use of resources. Advancement in Machine-learning (ML) technology… More >

  • Open Access


    Machine Learning Empowered Electricity Consumption Prediction

    Maissa A. Al Metrik*, Dhiaa A. Musleh

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1427-1444, 2022, DOI:10.32604/cmc.2022.025722

    Abstract Electricity, being the most efficient secondary energy, contributes for a larger proportion of overall energy usage. Due to a lack of storage for energy resources, over supply will result in energy dissipation and substantial investment waste. Accurate electricity consumption prediction is vital because it allows for the preparation of potential power generation systems to satisfy the growing demands for electrical energy as well as: smart distributed grids, assessing the degree of socioeconomic growth, distributed system design, tariff plans, demand-side management, power generation planning, and providing electricity supply stability by balancing the amount of electricity produced and consumed. This paper proposes… More >

  • Open Access


    Analysis and Prediction of Regional Electricity Consumption Based on BP Neural Network

    Pingping Xia1, *, Aihua Xu2, Tong Lian1

    Journal of Quantum Computing, Vol.2, No.1, pp. 25-32, 2020, DOI:10.32604/jqc.2019.09232

    Abstract Electricity consumption forecasting is one of the most important tasks for power system workers, and plays an important role in regional power systems. Due to the difference in the trend of power load and the past in the new normal, the influencing factors are more diversified, which makes it more difficult to predict the current electricity consumption. In this paper, the grey system theory and BP neural network are combined to predict the annual electricity consumption in Jiangsu. According to the historical data of annual electricity consumption and the six factors affecting electricity consumption, the gray correlation analysis method is… More >

  • Open Access


    A Cryptographic-Based Approach for Electricity Theft Detection in Smart Grid

    Khelifi Naim1, *, Benahmed Khelifa2, Bounaama Fateh3

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 97-117, 2020, DOI:10.32604/cmc.2020.09391

    Abstract In order to strengthen their security issues, electrical companies devote particular efforts to developing and enhancing their fraud detection techniques that cope with the information and communication technologies integration in smart grid fields. Having been treated earlier by several researchers, various detection schemes adapted from attack models that benefit from the smart grid topologies weaknesses, aiming primarily to the identification of suspicious incoming hazards. Wireless meshes have been extensively used in smart grid communication architectures due to their facility, lightness of conception and low cost installation; however, the communicated packets are still exposed to be intercepted maliciously in order either… More >

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