Improving Effectiveness of Energy Baseline Using Deep Learning

Energy conservation and carbon reduction are critical in energy policies. Therefore, numerous energy-saving methods, such as the introduction of new technologies and the replacement of outdated equipment, have been proposed. To determine whether these methods are effective in energy conservation and carbon reduction, scientific validation is required. The most common validation method is energy baseline. An energy baseline refers to the use of data measured before energy-saving improvements. It is used to construct a mathematical model that describes energy consumption. Using the baseline, the energy consumption during the baseline period after improvements is calculated. By subtracting the measured consumption from the value, the amount of energy saved is estimated. Traditionally, linear regression is used to establish energy baseline prediction. However, linear regression has limitations with complex energy data. Therefore, we used deep learning models to handle nonlinear data in the air compression system for comparative analysis. The developed long-short-term memory (LSTM) model showed superior capabilities for processing nonlinear data, aligning with the actual data distribution, and reducing errors. Compared with linear regression models, the LSTM model reduced uncertainty, risk, and cost by 40.3%.