Improving Building Heat Load Forecasting Models with Automated Identification and Attribution of Day Types

This paper introduces a comprehensive methodology for predicting hourly heat loads in buildings. The approach employs unsupervised learning to identify distinct day types based on daily load profiles. A classification process then assigns each day to one of these day types, followed by the application of various supervised learning techniques to forecast heat loads. The methodology is both simple and robust, facilitating its use in load prediction across a wide range of buildings. The process is validated using data from three distinct building types (Residential, Educational, and Commercial) located in Tartu, Estonia. The results indicate that the day type identification and attribution process significantly reduce model complexity and computational time while achieving high prediction accuracy (MAPE ~<2%) with minimal computational requirements.