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 | |
| 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 |
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Keywords
- Energy management
- Genetic algorithm
- Renewable energy
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
Cite this
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
}
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 -