Weimin He, Lei Zhao^*

Journal of New Media, Vol.4, No.4, pp. 191-203, 2022, DOI:10.32604/jnm.2022.032994

Abstract Accurate electricity forecasting is the key basis for guiding the power sector to arrange operation plans and guaranteeing the profitability of electric power companies. However, with the increasing demand of enterprises and departments for data security, the phenomenon of “Isolated Data Island” becomes more and more serious, resulting in the accuracy loss of the traditional electricity prediction model. Federated learning, as an emerging artificial intelligence technology, is designed to ensure data privacy while carrying out efficient machine learning, which provides a new way to solve the problem of “Isolated Data Island” in terms of electricity forecasting. Nonetheless, due 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 >