Building Energy Information: Demand and Consumption Prediction with Machine Learning Models for Sustainable and Smart Cities

Sina Ardabili, Amir Mosavi, Annamária R. Várkonyi-Kóczy

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Building energy consumption plays an essential role in urban sustainability. The prediction of the energy demand is also of particular importance for developing smart cities and urban planning. Machine learning has recently contributed to the advancement of methods and technologies to predict demand and consumption for building energy systems. This paper presents a state of the art of machine learning models and evaluates the performance of these models. Through a systematic review and a comprehensive taxonomy, the advances of machine learning are carefully investigated and promising models are introduced.

Original languageEnglish
Title of host publicationLecture Notes in Networks and Systems
PublisherSpringer
Pages191-201
Number of pages11
DOIs
Publication statusPublished - Jan 1 2020

Publication series

NameLecture Notes in Networks and Systems
Volume101
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

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Keywords

  • And consumption
  • Big data
  • Building energy
  • Deep learning
  • Energy demand
  • IoT
  • Machine learning
  • Smart cities
  • Soft computing
  • Sustainable cities
  • Sustainable urban development

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications

Cite this

Ardabili, S., Mosavi, A., & Várkonyi-Kóczy, A. R. (2020). Building Energy Information: Demand and Consumption Prediction with Machine Learning Models for Sustainable and Smart Cities. In Lecture Notes in Networks and Systems (pp. 191-201). (Lecture Notes in Networks and Systems; Vol. 101). Springer. https://doi.org/10.1007/978-3-030-36841-8_19