Computationally intelligent heating controller optimization
Abstract
Computationally intelligent optimization of the controller in a building radiator heating system performed by several metaheuristic methods has been considered in this paper. Presented results compare conventional controller tuning using integral of time absolute error criterion and six metaheuristic methods-Simulated Annealing (SA), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), reported in our previous research, and by Ant Colony Optimization (ACO), Grey Wolf Optimizer (GWO) and Bat Algorithm (BA) presented in this paper. Performance of the tuned heating controller has been tested for the typical winter day in the building geographical region. General conclusions are presented confirming that metaheuristic computationally intelligent optimization of thermal controllers is a feasible concept with strong potentials in providing improved performance, comfort and energy efficiency.
Keywords:
metaheuristic optimization / heating / control / computational intelligence / building heating systemSource:
Journal of Mechatronics, Automation and Identification Technology, 2018, 3, 3, 16-20Publisher:
- Univerzitet u Novom Sadu, Fakultet tehničkih nauka i CAM Engineering
Funding / projects:
- Intelligent Control Systems of the Air-conditioning for the Purpose of Achieving Energy Efficient Exploitation Regimes in the Complex Operating Conditions (RS-MESTD-Technological Development (TD or TR)-33047)
- Research of MHD flows around the bodies, in the tip clearances and the channels and application in the MHD pumps development (RS-MESTD-Technological Development (TD or TR)-35016)
- Research and development of new generation wind turbines of high-energy efficiency (RS-MESTD-Technological Development (TD or TR)-35005)
Collections
Institution/Community
Mašinski fakultetTY - JOUR AU - Ćojbašić, Žarko AU - Ristanović, Milan AU - Dučić, Nedeljko AU - Savić, Stefan AU - Marković, Nemanja PY - 2018 UR - https://machinery.mas.bg.ac.rs/handle/123456789/2778 AB - Computationally intelligent optimization of the controller in a building radiator heating system performed by several metaheuristic methods has been considered in this paper. Presented results compare conventional controller tuning using integral of time absolute error criterion and six metaheuristic methods-Simulated Annealing (SA), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), reported in our previous research, and by Ant Colony Optimization (ACO), Grey Wolf Optimizer (GWO) and Bat Algorithm (BA) presented in this paper. Performance of the tuned heating controller has been tested for the typical winter day in the building geographical region. General conclusions are presented confirming that metaheuristic computationally intelligent optimization of thermal controllers is a feasible concept with strong potentials in providing improved performance, comfort and energy efficiency. PB - Univerzitet u Novom Sadu, Fakultet tehničkih nauka i CAM Engineering T2 - Journal of Mechatronics, Automation and Identification Technology T1 - Computationally intelligent heating controller optimization EP - 20 IS - 3 SP - 16 VL - 3 UR - https://hdl.handle.net/21.15107/rcub_machinery_2778 ER -
@article{ author = "Ćojbašić, Žarko and Ristanović, Milan and Dučić, Nedeljko and Savić, Stefan and Marković, Nemanja", year = "2018", abstract = "Computationally intelligent optimization of the controller in a building radiator heating system performed by several metaheuristic methods has been considered in this paper. Presented results compare conventional controller tuning using integral of time absolute error criterion and six metaheuristic methods-Simulated Annealing (SA), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), reported in our previous research, and by Ant Colony Optimization (ACO), Grey Wolf Optimizer (GWO) and Bat Algorithm (BA) presented in this paper. Performance of the tuned heating controller has been tested for the typical winter day in the building geographical region. General conclusions are presented confirming that metaheuristic computationally intelligent optimization of thermal controllers is a feasible concept with strong potentials in providing improved performance, comfort and energy efficiency.", publisher = "Univerzitet u Novom Sadu, Fakultet tehničkih nauka i CAM Engineering", journal = "Journal of Mechatronics, Automation and Identification Technology", title = "Computationally intelligent heating controller optimization", pages = "20-16", number = "3", volume = "3", url = "https://hdl.handle.net/21.15107/rcub_machinery_2778" }
Ćojbašić, Ž., Ristanović, M., Dučić, N., Savić, S.,& Marković, N.. (2018). Computationally intelligent heating controller optimization. in Journal of Mechatronics, Automation and Identification Technology Univerzitet u Novom Sadu, Fakultet tehničkih nauka i CAM Engineering., 3(3), 16-20. https://hdl.handle.net/21.15107/rcub_machinery_2778
Ćojbašić Ž, Ristanović M, Dučić N, Savić S, Marković N. Computationally intelligent heating controller optimization. in Journal of Mechatronics, Automation and Identification Technology. 2018;3(3):16-20. https://hdl.handle.net/21.15107/rcub_machinery_2778 .
Ćojbašić, Žarko, Ristanović, Milan, Dučić, Nedeljko, Savić, Stefan, Marković, Nemanja, "Computationally intelligent heating controller optimization" in Journal of Mechatronics, Automation and Identification Technology, 3, no. 3 (2018):16-20, https://hdl.handle.net/21.15107/rcub_machinery_2778 .