TA-23-A014 – Cold Start Methods for Building’s Energy Consumption Forecasting

Smart Energy Management Systems (SEMS) are crucial for minimizing CO2 emissions and optimizing energy consumption in buildings. Accurate energy forecasting models are necessary for SEMS to estimate available energy resources and create management strategies. This paper proposes a Cold Start approach that utilizes Few-Shot Learning to enhance the training of forecasting models for new building scenarios with limited or low-quality data. By using data from past buildings with similar characteristics, SEMS can achieve their energy management goals and reduce energy waste in the early deployment phase, resulting in environmental benefits, economic savings, and increased market value.