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

  • Sina Ardabili
  • Amir MosaviEmail author
  • Annamária R. Várkonyi-Kóczy
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 101)

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.

Keywords

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

Nomenclatures

Generalized boosted regression

GBR

Deep learning

DL

Artificial neural network

ANN

Extreme learning machine

ELM

Machine learning

ML

Support vector machine

SVM

Wavelet neural networks

WNN

Support vector regression

SVR

Genetic algorithm

GA

Multi layered perceptron

MLP

Long short-term memory

LSTM

Decision tree

DT

Response surface methodology

RSM

Back propagation neural network

BPNN

Centroid mean

CM

Adaptive neuro fuzzy inference system

ANFIS

Analytic network process

ANP

Radial basis function

RBF

Feed-forward neural networks

FFNN

Particle swarm optimization

PSO

Random forest

RF

Non-random two-liquid

NRTL

Recurrent neural network

RNN

Partial least squares

PLS

Discriminant analysis

DA

Principal component analysis

PCA

Linear discriminant analysis

LDA

Autoregressive integrated moving average

ARIMA

Least-squares

LS

Sparse Bayesian

SB

Multi criteria decision making

MCDM

Genetic programming

GP

Multi linear regression

MLR

Step-wise Weight Assessment Ratio Analysis

SWARA

Multi Objective Optimization by Ratio Analysis

MOORA

Nonlinear autoregressive exogenous

NARX

Notes

Acknowledgments

This publication has been supported by the Project: “Support of research and development activities of the J. Selye University in the field of Digital Slovakia and creative industry” of the Research & Innovation Operational Programme (ITMS code: NFP313010T504) co-funded by the European Regional Development Fund.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Advanced Studies KoszegKoszegHungary
  2. 2.Kalman Kando Faculty of Electrical EngineeringObuda UniversityBudapestHungary
  3. 3.School of the Built EnvironmentOxford Brookes UniversityOxfordUK
  4. 4.Department of Mathematics and InformaticsJ. Selye UniversityKomarnoSlovakia

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