Temperature regulation of a commercial space in building system optimized by genetic algorithm

ABSTRACT

Heating systems are becoming an increasingly important research subject in the building field because, they are the main element used to improve occupants comfort. This work is being done to improve building comfort while also reducing energy consumption by employing a new proposed optimized neural network control approach for a commercial space's temperature regulation in building system optimized by genetic algorithm. The key control goal is to decrease electricity consumption while maintaining the occupants’ optimum thermal comfort. This approach’s originality may allow it to work in the face of disruptions such as occupancy profiles, electrical devices, inside temperature, outside temperature, and weather information. A combination of genetic algorithms and artificial neural networks is presented to benefit from the advantages of each approach to create a quick and strong controller, especially when the system lacks a mathematical and physical model. The suggested technique is effectively shown using an example of temperature regulation in a commercial space, under Matlab/Simulink environment using the SIMBAD toolbox (SIMulator of Buildings and Devices). In many cases, the effectiveness of the suggested control has been demonstrated using good performance indices. The simulation results demonstrated that a high level of temperature regulation is generated in the commercial space with an electrical radiator of 1000 W, , and a high degree of comfort. As a result, the validity and efficacy of this control technique have been demonstrated.

KEYWORDS:

• Artificial neural network control
• building HVAC control
• building thermal comfort
• energy consumption
• genetic algorithms
• temperature regulation

Disclosure statement

No potential conflict of interest was reported by the author(s).