An Optimal Air-Conditioner On-Off Control Scheme under Extremely Hot Weather Conditions

Abstract

Being reliant on Air Conditioning (AC) throughout the majority of the year, desert countries with extremely hot weather conditions such as Qatar are facing challenges in lowering weariness cost due to AC On-Off switching while maintaining an adequate level of comfort under a wide-range of ambient temperature variations. To address these challenges, this paper investigates an optimal On-Off control strategy to improve the AC utilization process. To overcome complexities of online optimization, a Elman Neural Networks (NN)-based estimator is proposed to estimate real values of the outdoor temperature, and make off-line optimization available. By looking up the optimum values solved from an off-line optimization scheme, the proposed control solutions can adaptively regulate the indoor temperature regardless of outdoor temperature variations. In addition, a cost function of multiple objectives, which consider both Coefficient of Performance (COP), and AC compressor weariness due to On-Off switching, is designed for the optimization target of minimum cost. Unlike conventional On-Off control methodologies, the proposed On-Off control technique can respond adaptively to match large-range (up to 20 ∘ C) ambient temperature variations while overcoming the drawbacks of long-time online optimization due to heavy computational load. Finally, the Elman NN based outdoor temperature estimator is validated with an acceptable accuracy and various validations for AC control optimization under Qatar’s real outdoor temperature conditions, which include three hot seasons, are conducted and analyzed. The results demonstrate the effectiveness and robustness of the proposed optimal On-Off control solution.

Heat Sink Shape and Topology Optimization with Pareto-Vector Length Optimization for Air Cooling

“…Works by both Lineykin et al [15] and Al-Rubaye et al [16], where they described every essential parameter of TEC devices using specifications provided by manufacturers were used for Equations (1)- (5). The expressions assume that the temperature difference between cold and warm side of the TEC device (∆T TEC ); the warm side temperature (T warm ); the maximum current (I max ) and the applied voltage (U max ) are known.…”

“…With these Equations (1)- (5) the extracted heat on the cold side of the TEC device (Q cold ) (W) can be calculated:…”

“…Equation (5) shows that the cold side temperature of the TEC device (T TEC ) is a limiting factor of the possible extracted heat. A TEC cannot indefinitely decrease its cold surface temperature (T TEC cold ) at a given cooling power.…”

“…it can be seen what type of measuring devices were used for the experiment. Based on the measurement result the cooling power of TEC modules (Q cold ) were calculated with Equations (1)- (5) to evaluate the cooling efficiency.…”

“…For energy demand reduction several actions have been taken, such as the application of renewable energy harvesting systems and the use of low power demand devices [1,2]. The concept of using robust HVAC systems is a generally accepted method [3][4][5]. However, high performance HVAC systems cannot effectively modulate down indefinitely when the demand is low.…”