The prediction of residential building consumption using profiling and time encoding
Procedia Computer Science(2022)
Abstract
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%).
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Key words
Time encoding,forecasting,machine learning,green buildings
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