Abstract
For improving control methods in the thermal environment, various algorithms have been studied to satisfy the specific conditions required by the characteristics of building spaces and to reduce the energy consumed in operation. In this research, a network-based learning control equipped with an adaptive controller is proposed to investigate the control performance for supply air conditions with maintaining the levels of indoor thermal comfort. In order to examine its performance, the proposed model is compared to two different models in terms of the patterns of heating and cooling energy use and the characteristics of operational signals and overshoots. As a result, the energy efficiency of the proposed control has been slightly decreased due to the energy consumption increased by precise controls, but the thermal comfort has improved by about 10.7% more than a conventional thermostat and by about 19.8% more than a deterministic control, respectively. This result can contribute to the reduction of actual installation and maintenance costs by reducing the operating time of dampers and the energy use of heating coils without compromising indoor thermal comfort.
Highlights
In the field of building thermal controls, several analyses based on mathematical and statistical approaches have been conducted to investigate the thermal control strategies for cooling and heating as spatial characteristics
The results show somewhat of a deviation from the generally considered thermal comfort level, due to a bit low Predicted Mean Vote (PMV) values derived from low indoor temperatures during the nighttime and due to somewhat high PMV values derived from slightly high setting values of daytime users’ metabolic rates and clothing insulation
There is a difference between the types of buildings being a warehouse, the model without an adaptive model improved by just 0.7% (173.1 → 171.9) in thermal comfort (the sum neural network model without an adaptive model improved by just 0.7% (173.1 → 171.9) in thermal of the absolute values of the error to the PMV setting value), and by 1.3% (115.4 → 113.9 kWh/m2 ·year) comfort, and by 1.3%
Summary
In the field of building thermal controls, several analyses based on mathematical and statistical approaches have been conducted to investigate the thermal control strategies for cooling and heating as spatial characteristics. Among the several control rules, the integral and derivative methods of the signals have been commonly used with manual and computing tools to improve the performance of generators, exchangers, distributers, and economizers in the Heating, Ventilation, and Air Conditioning (HVAC) systems. In order to retrofit old systems using limited resources and data, the current control algorithms have examined a higher level of optimized signals by utilizing several parameters in variables. By use of advanced tuning rules, the precise controls of fuel, damper, valve, fan motor speed, and resistance coil were examined, and the variations of heating and cooling load patterns were investigated in building components [1,2,3]. The FIS algorithm utilizes linguistic approaches to complement fixed number or linear models in parametric controls that revealed some limitations of the conventional reasoning. By using the differences and the derivatives of serial control signals in Energies 2020, 13, 6023; doi:10.3390/en13226023 www.mdpi.com/journal/energies
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