Optimized small-scaled hybrid energy management of a smart house based on genetic algorithm

Viktor Ten, Zhandos Yessenbayev, Akmaral Shamshimova, Albina Khakimova

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Renewable energy sources provide both electricity and heating for experimental smart house located at renewable energy test site at Nazarbayev University. Smart house's electric and heating subsystems are supplied by PV cells and solar thermal heater respectively. Partially the system consumes the electricity from utility grid. The dynamics of the system consists of electrical and thermal parts based on the state of charge of accumulator battery stack and temperatures of the heating system and smart house itself. The goal of designed control is to maintain the states of the system inside of required ranges and simultaneously to minimize the expenses for power consumption from utility grid. The task of minimization is solved using genetic algorithm. The simulation results are obtained in MATLAB and confirm the efficiency of designed control.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1153-1158
Number of pages6
ISBN (Electronic)9781509002870
DOIs
Publication statusPublished - Mar 2 2016
EventIEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015 - Miami, United States
Duration: Dec 9 2015Dec 11 2015

Other

OtherIEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
CountryUnited States
CityMiami
Period12/9/1512/11/15

Fingerprint

Intelligent buildings
Energy management
Genetic algorithms
Heating
Electricity
MATLAB
Electric power utilization
Temperature
Hot Temperature

Keywords

  • Energy management
  • Genetic algorithm
  • Renewable energy

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications

Cite this

Ten, V., Yessenbayev, Z., Shamshimova, A., & Khakimova, A. (2016). Optimized small-scaled hybrid energy management of a smart house based on genetic algorithm. In Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015 (pp. 1153-1158). [7424475] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA.2015.64

Optimized small-scaled hybrid energy management of a smart house based on genetic algorithm. / Ten, Viktor; Yessenbayev, Zhandos; Shamshimova, Akmaral; Khakimova, Albina.

Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1153-1158 7424475.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ten, V, Yessenbayev, Z, Shamshimova, A & Khakimova, A 2016, Optimized small-scaled hybrid energy management of a smart house based on genetic algorithm. in Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015., 7424475, Institute of Electrical and Electronics Engineers Inc., pp. 1153-1158, IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015, Miami, United States, 12/9/15. https://doi.org/10.1109/ICMLA.2015.64
Ten V, Yessenbayev Z, Shamshimova A, Khakimova A. Optimized small-scaled hybrid energy management of a smart house based on genetic algorithm. In Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1153-1158. 7424475 https://doi.org/10.1109/ICMLA.2015.64
Ten, Viktor ; Yessenbayev, Zhandos ; Shamshimova, Akmaral ; Khakimova, Albina. / Optimized small-scaled hybrid energy management of a smart house based on genetic algorithm. Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1153-1158
@inproceedings{dc417f17b02e46a2a08e4cfedfa9e608,
title = "Optimized small-scaled hybrid energy management of a smart house based on genetic algorithm",
abstract = "Renewable energy sources provide both electricity and heating for experimental smart house located at renewable energy test site at Nazarbayev University. Smart house's electric and heating subsystems are supplied by PV cells and solar thermal heater respectively. Partially the system consumes the electricity from utility grid. The dynamics of the system consists of electrical and thermal parts based on the state of charge of accumulator battery stack and temperatures of the heating system and smart house itself. The goal of designed control is to maintain the states of the system inside of required ranges and simultaneously to minimize the expenses for power consumption from utility grid. The task of minimization is solved using genetic algorithm. The simulation results are obtained in MATLAB and confirm the efficiency of designed control.",
keywords = "Energy management, Genetic algorithm, Renewable energy",
author = "Viktor Ten and Zhandos Yessenbayev and Akmaral Shamshimova and Albina Khakimova",
year = "2016",
month = "3",
day = "2",
doi = "10.1109/ICMLA.2015.64",
language = "English",
pages = "1153--1158",
booktitle = "Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

TY - GEN

T1 - Optimized small-scaled hybrid energy management of a smart house based on genetic algorithm

AU - Ten, Viktor

AU - Yessenbayev, Zhandos

AU - Shamshimova, Akmaral

AU - Khakimova, Albina

PY - 2016/3/2

Y1 - 2016/3/2

N2 - Renewable energy sources provide both electricity and heating for experimental smart house located at renewable energy test site at Nazarbayev University. Smart house's electric and heating subsystems are supplied by PV cells and solar thermal heater respectively. Partially the system consumes the electricity from utility grid. The dynamics of the system consists of electrical and thermal parts based on the state of charge of accumulator battery stack and temperatures of the heating system and smart house itself. The goal of designed control is to maintain the states of the system inside of required ranges and simultaneously to minimize the expenses for power consumption from utility grid. The task of minimization is solved using genetic algorithm. The simulation results are obtained in MATLAB and confirm the efficiency of designed control.

AB - Renewable energy sources provide both electricity and heating for experimental smart house located at renewable energy test site at Nazarbayev University. Smart house's electric and heating subsystems are supplied by PV cells and solar thermal heater respectively. Partially the system consumes the electricity from utility grid. The dynamics of the system consists of electrical and thermal parts based on the state of charge of accumulator battery stack and temperatures of the heating system and smart house itself. The goal of designed control is to maintain the states of the system inside of required ranges and simultaneously to minimize the expenses for power consumption from utility grid. The task of minimization is solved using genetic algorithm. The simulation results are obtained in MATLAB and confirm the efficiency of designed control.

KW - Energy management

KW - Genetic algorithm

KW - Renewable energy

UR - http://www.scopus.com/inward/record.url?scp=84969715265&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84969715265&partnerID=8YFLogxK

U2 - 10.1109/ICMLA.2015.64

DO - 10.1109/ICMLA.2015.64

M3 - Conference contribution

SP - 1153

EP - 1158

BT - Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015

PB - Institute of Electrical and Electronics Engineers Inc.

ER -