Forecasting the Loads of Residential Buildings by Machine Learning Techniques

Because of its substantial technological and practical significance, energy consumption prediction in residential buildings is a critical component in electrical energy networks. It is a complicated process to forecast due to several variables, including resident behavior, household dimension, loads existing in residence, and numerous additional things. Machine Learning (ML) approaches can help to bridge this barrier by utilizing historical information that is already available to forecast new instances and produce wise conclusions. Thus, this work emphasizes investigating the applications of ML techniques to forecast the load of residential buildings precisely.