Development of a multi-granularity energy forecasting toolkit for demand response baseline calculation

The peak load caused by heating, ventilation, and air-conditioning (HVAC) systems is one of the main control targets of a demand response (DR) program. One key issue related to DR is the baseline energy consumption forecasting based on which the DR strategies and performance can be evaluated. Data-driven models, as a promising method for HVAC energy prediction, have been widely studied. But most existing researches have focused on developing complicated algorithms rather than exploring informative features. In this study, a comprehensive review of feature engineering for HVAC energy prediction model development is presented. A novel feature engineering method is roposed. Besides, an easy-to-use, high-accuracy HVAC energy forecasting toolkit that is applicable to datasets of various granularities is developed. This toolkit uses easily available meteorological parameters and raw historical energy data as inputs, on which it performs data preprocessing, feature extension, and integrated optimization, thereby producing the predicted data. By employing a novel feature extension strategy and integrated optimization of feature selection and hyperparameter tuning, this toolkit performs capably in terms of prediction accuracy and stability. The results of a comparative experiment conducted on large-scale data verify that the average forecasting error (measured in terms of the coefficient of variation of the root mean square error) is <8%.

Automated summary

This study conducted a comprehensive review of feature engineering for HVAC energy prediction model development, proposed a novel feature engineering method, and developed an easy-to-use, high-accuracy HVAC energy forecasting toolkit. The toolkit was validated on large-scale data, showing an average forecasting error of <8%.