The prediction of residential building consumption using profiling and time encoding

The prediction of future consumption is vital for building management in general and green buildings in particular. A certified green building should meet an evaluation criterion of resource efficiency throughout its life cycle. This paper presents a hybrid model that combines a profile averaging model and a machine learning model, namely random forest regressor method, for improved far horizon forecasting (10% improvement when the prediction horizon is 1 week). The proposed hybrid model is modular, explainable, and does not entail a large dataset. The feature analysis highlighted the importance of explicit time encoding by feeding the time as an input feature to the forecasting model (importance factor of 35%).