AI-Powered IoT Smart Meter Analytics for Precision Energy Consumption Forecasting
Keywords:
Energy efficiency, Carbon emissions, Smart meters, Energy consumption prediction, Machine learning algorithms, Internet of Things (IoT)Abstract
Countries are placing a greater emphasis on energy efficiency in an effort to reduce their carbon emissions and make the most of the resources they have available inside their borders. The tracking of energy use in the past consisted of either hand-reading meters or crude automated devices that provided only a limited amount of data. It is common for conventional energy consumption prediction
systems to rely on too simplistic models that do not make full use of the opportunities presented by data from smart meters.
References
U.S. Energy Consumption Fell by a Record 7. Available online: (accessed on 26 June 2023).
Stay-at-Home Orders Led to Less Commercial and Industrial Electricity Use in April. (accessed on 30 June 2020).
Chinthavali, S.; Tansakul, V.; Lee, S.; Whitehead, M.; Tabassum, A.; Bhandari, M.; Munk, J.; Zandi, H.; Buckberry, H.; Kuruganti, T.; et al. COVID-19 pandemic ramifications on residential Smart homes energy use load profiles. Energy Build. 2022, 259, 111847.
