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

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Engineering for Sustainable Future (INTER-ACADEMIA 2019)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 101))

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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.

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Abbreviations

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

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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.

Author information

Authors and Affiliations

  1. Institute of Advanced Studies Koszeg, Koszeg, Hungary

    Sina Ardabili

  2. Kalman Kando Faculty of Electrical Engineering, Obuda University, Budapest, Hungary

    Amir Mosavi

  3. School of the Built Environment, Oxford Brookes University, Oxford, OX3 0BP, UK

    Amir Mosavi

  4. Department of Mathematics and Informatics, J. Selye University, Komarno, Slovakia

    Annamária R. Várkonyi-Kóczy

Authors
  1. Sina Ardabili
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  2. Amir Mosavi
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  3. Annamária R. Várkonyi-Kóczy
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Corresponding author

Correspondence to Amir Mosavi .

Editor information

Editors and Affiliations

  1. Obuda University, Budapest, Hungary

    Annamária R. Várkonyi-Kóczy

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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: Várkonyi-Kóczy, A. (eds) Engineering for Sustainable Future. INTER-ACADEMIA 2019. Lecture Notes in Networks and Systems, vol 101. Springer, Cham. https://doi.org/10.1007/978-3-030-36841-8_19

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  • DOI: https://doi.org/10.1007/978-3-030-36841-8_19

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