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

EasyChair Preprint 4292

Abstract

The building energy consumption plays an important role in the 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 in 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.

Keyphrases: Big Data., Deep Learning., Machine Learning., Soft Computing.

@booklet{EasyChair:4292,
  author    = {Sina Ardabili and Amir Mosavi and A.R Várkonyi Kóczy},
  title     = {Building Energy Information: Demand and Consumption Prediction with Machine Learning Models for Sustainable and Smart Cities},
  howpublished = {EasyChair Preprint 4292},
  year      = {EasyChair, 2020}}