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.
Highlights
To validate the proposed On-Off control and optimization scheme, the studied house model parameters are referred from [13], and the Air Conditioning (AC) power is changed as 1500 W but not 300 W because It is more reasonable due to the hot climate in Qatar
This paper presents a new optimal control strategy that improves existing conventional On-Off control for air conditioning under the harsh desert climate of Qatar
The optimum AC control is achieved based on an integration between off-line optimization and online adaptive control
Summary
Famous for its unique geographic location between desert and sea, Qatar has significant desert climate and extremely hot weather conditions with a high temperature, humidity, and sand storms. PWM controller has to change under very fast switching frequency at the initial stage of the control process or scenarios of large variations in the working conditions This fast change could degrade the AC compressor, and it has been proven not to be optimal for all the HVAC control cases. On-Off control schemes, the proposed On-Off control and optimization scheme can process more complex cooling scenarios such as large range and fast temperature variations. This is because the controller can adaptively accommodate the variations by tuning the parameters optimization online. Compared to the MPC control, the proposed control scheme performs optimization work offline which does not require a huge computational power in a real-time way like the online optimization technique.
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