Junde Chen¹, Jiajia Yuan², Weirong Chen³, Adnan Zeb⁴, Md Suzauddola⁵, Yaser A. Nanehkaran^2,*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2575-2591, 2024, DOI:10.32604/cmc.2024.048522

Abstract Missing value is one of the main factors that cause dirty data. Without high-quality data, there will be no reliable analysis results and precise decision-making. Therefore, the data warehouse needs to integrate high-quality data consistently. In the power system, the electricity consumption data of some large users cannot be normally collected resulting in missing data, which affects the calculation of power supply and eventually leads to a large error in the daily power line loss rate. For the problem of missing electricity consumption data, this study proposes a group method of data handling (GMDH) based data interpolation method in distribution… More >

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 >

Yiqun Zhu¹, Shuxian Sun¹, Chunyu Liu¹, Xinyi Tian¹, Jingyi He², Shuai Xiao^2,*

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 >

Qiuying Shen¹, Wentao Zhang¹, Mofei Song^2,*

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 >

Jiaming Dou¹, Hongyan Ma^1,2,3,*, 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 >

Majida Kazmi^1,*, Hashim Raza Khan^1,2, Lubaba², Mohammad Hashir Bin Khalid², Saad Ahmed Qazi^1,2

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 >

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 >

Pingping Xia^{1, *}, 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 >

Khelifi Naim^{1, *}, 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 >