A generic pipeline for machine learning users in energy andbuildings domain

• Mahmoud Abdelkader Bashery Abbass ^[1] ; Mohamed Hamdy ^[2]
  1. [1] Helwan University

     Helwan University

     Egipto

     
  2. [2] Norwegian University of Science and Technology

     Norwegian University of Science and Technology

     Noruega

     
• Localización: Artificial intelligence in the energy industry / Ana Belén Gil González (ed. lit.), 2022, ISBN 978-3-0365-4605-6, págs. 35-65
• Idioma: inglés
• Enlaces
  • Texto Completo Libro (pdf)
• Resumen

  • One of the biggest problems in applying machine learning (ML) in the energy and buildingsfield is the lack of experience of ML users in implementing each ML algorithm in real-life applicationsthe 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 thespecified field to guide them to select the best-fitting algorithm based on their particular applicationsand 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 theenergy 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 themwith possible applications in the energy and buildings field in one layout; (3) a full description ofthe proposed pipeline by explaining the way of implementing it and its environmental impacts inimproving energy management systems for different countries; and (4) implementing the pipeline onreal data (CBECS) to prove its applicability.