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

Viktor Ten; Zhandos Yessenbayev; Akmaral Shamshimova; Albina Khakimova

doi:10.1109/ICMLA.2015.64

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 proceeding › Conference contribution

4 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 language	English
Title of host publication	Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1153-1158
Number of pages	6
ISBN (Electronic)	9781509002870
DOIs	https://doi.org/10.1109/ICMLA.2015.64
Publication status	Published - Mar 2 2016
Event	IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015 - Miami, United States Duration: Dec 9 2015 → Dec 11 2015

Other

Other	IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
Country	United States
City	Miami
Period	12/9/15 → 12/11/15

Keywords

Energy management
Genetic algorithm
Renewable energy

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications

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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 proceeding › Conference 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

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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.",

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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.

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

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Keywords

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