A Generic Pipeline for Machine Learning Users in Energy and Buildings Domain

Abstract

One of the biggest problems in applying machine learning (ML) in the energy and buildings field is the lack of experience of ML users in implementing each ML algorithm in real-life applications the right way, because each algorithm has prerequisites to be used and specific problems or applications to be implemented. Hence, this paper introduces a generic pipeline to the ML users in the specified field to guide them to select the best-fitting algorithm based on their particular applications and to help them to implement the selected algorithm correctly to achieve the best performance. The introduced pipeline is built on (1) reviewing the most popular trails to put ML pipelines for the energy and building, with a declaration for each trial drawbacks to avoid it in the proposed pipeline; (2) reviewing the most popular ML algorithms in the energy and buildings field and linking them with possible applications in the energy and buildings field in one layout; (3) a full description of the proposed pipeline by explaining the way of implementing it and its environmental impacts in improving energy management systems for different countries; and (4) implementing the pipeline on real data (CBECS) to prove its applicability.

Keywords

• machine learning
• benchmarking
• prediction
• pipeline
• features
• training