Household occupancy and energy consumption prediction for energy data big data mining

Hangdong An

doi:10.1117/12.2667622

22 February 2023 Household occupancy and energy consumption prediction for energy data big data mining

Hangdong An

Proceedings Volume 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022); 1258704 (2023) https://doi.org/10.1117/12.2667622
Event: Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 2022, Shanghai, China

Abstract

Globally, solar power technology has become one of the most important sources of electricity for cities or households. And more and more households are choosing to use small, intelligent solar power systems from utility companies as a supplementary energy source for their homes. The energy consumption data stored by the smart system can reflect the user's household activities. The aim of this paper is to re-analyse the energy consumption data provided by Red-back for households in 2011, using big data techniques, to determine which energy information needs to be protected in the smart system by predicting household daily energy consumption using deep learning and machine learning methods, combined with weather data to predict home occupancy.

Citation Download Citation

Hangdong An "Household occupancy and energy consumption prediction for energy data big data mining", Proc. SPIE 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 1258704 (22 February 2023); https://doi.org/10.1117/12.2667622

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