Reducing Household Electricity Consumption with Artificial Intelligence to Capture Carbon Dioxide

GIST Professor Jinho Kim's Energy Convergence College Team

[Asia Economy Reporter Kim Bong-su] Domestic researchers have developed a technology that can optimize household energy consumption using artificial intelligence (AI), contributing to electricity savings and carbon dioxide reduction.

The Gwangju Institute of Science and Technology (GIST) announced on the 29th that Professor Jin-ho Kim's research team at the School of Energy Convergence has developed a new AI-based analytical technology that detects and extracts consumption patterns of residential energy users living in houses or apartments.

The research team extracted appliance usage and human occupancy patterns through a new probabilistic approach using second-by-second power consumption measurement data of home appliances.

To estimate the actual participation potential of demand response resources, analysis of energy load characteristics including user behavior based on information data is necessary. In the simulation operation algorithm for demand response potential estimation, the user's inconvenience related to the dynamic characteristics of appliances was quantified and reflected.

For example, the comfort level perceived by humans due to air conditioner operation and indoor thermal inertia temperature changes was constrained according to the ISO scale, and changes in lighting brightness were measured and controlled based on the International Renewable Energy Agency (IRENA) standards to prevent eye fatigue. Accordingly, resource potential estimation was made possible within the range that satisfies the energy use satisfaction of the resident users.

Through this study, the research team confirmed that one household participating as a demand response resource for 250 days can contribute about 10 MWh of energy to the power grid, which corresponds to a reduction of approximately 7.7 tons of carbon dioxide. They suggested that if part of the output from fossil fuel power generators is replaced by demand response resources, a new market incentive could be created to return environmental benefits from carbon reduction to consumers.

Professor Kim said, "We confirmed that big data-based analysis capable of converting household energy demand into a large integrated resource is possible," adding, "It can be utilized to improve the efficiency of sector coupling in various fields such as water, heat, gas, and electric vehicles, and to prepare policies for this purpose."

The results of this study were published in the September issue of ‘IEEE Transactions on Smart Grid,’ a journal ranked in the top 10% in the field of electrical and electronic engineering.