Model predictive control of indoor thermal environment conditioned by a direct expansion air conditioning system

Xia, Yudong; Zhu, Meifang; Jiang, Aipeng; Wang, Jian; Bai, Xiaoxia; Deng, Shuai

doi:10.1007/s12273-022-0949-1

Cited by 10 publications

(1 citation statement)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One method is to fix the supply air volume and control the temperature and humidity of the supply air [3]. This system is commonly referred to as a constant air volume air conditioning control system (CAV); Another method is to keep the supply air temperature constant and control the supply air volume [4], which is called a variable air volume air conditioning system (VAV). In China, constant air volume control system has been used for a long time, but at present, due to some shortcomings of CAV system, such as condensed water mold, complex expansion, etc., designers of HVAC and Control engineering have to switch research daylight back to VAV system.…”

Section: Introductionmentioning

confidence: 99%

Energy saving adaptive control algorithm for air conditioning in high heat density computer rooms

Lv,

Chen,

Huang

2024

International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024)

View full text Add to dashboard Cite

When controlling air conditioning, due to the lack of comprehensive analysis of the relationship between energy consumption of different units, the energy consumption loudness of air conditioning is high. Therefore, a study on energy-saving adaptive control algorithm for high heat density computer room air conditioning is proposed. Based on the operational characteristics of air conditioning in high heat density computer rooms, with the objective function of minimizing unit operating power consumption to meet the cooling load demand of the front-end, a high heat density computer room air conditioning energy consumption model was constructed from three perspectives: chillers, chilled water pumps, and cooling water pump pipes. In the control algorithm design stage, the Double Pools based DQN algorithm (DPs DQN) was introduced. Based on the condition of whether optimization of system cooling load allocation is needed, the sample data of high heat density computer room air conditioning operation was divided into two categories: samples that require optimization of system cooling load allocation and samples that do not require optimization of system cooling load allocation, The energy consumption model of the high heat density computer room air conditioning constructed in section 1.1 is used to make action decisions throughout the entire action space, and the result of gradient descent update is the control parameter. In the test results, the designed energy-saving adaptive control algorithm for high heat density computer room air conditioning can effectively reduce energy consumption during the operation phase of the air conditioning system, while achieving stable control of the computer room temperature.

show abstract

Section: Introductionmentioning

confidence: 99%

Energy saving adaptive control algorithm for air conditioning in high heat density computer rooms

Lv,

Chen,

Huang

2024

International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024)

View full text Add to dashboard Cite

show abstract

Evaluation of model predictive control (MPC) of solar thermal heating system with thermal energy storage for buildings with highly variable occupancy levels

Wei,

Calautit

2023

Build. Simul.

View full text Add to dashboard Cite

The presence or absence of occupants in a building has a direct effect on its energy use, as it influences the operation of various building energy systems. Buildings with high occupancy variability, such as universities, where fluctuations occur throughout the day and across the year, can pose challenges in developing control strategies that aim to balance comfort and energy efficiency. This situation becomes even more complex when such buildings are integrated with renewable energy technologies, due to the inherently intermittent nature of these energy source. To promote widespread integration of renewable energy sources in such buildings, the adoption of advanced control strategies such as model predictive control (MPC) is imperative. However, the variable nature of occupancy patterns must be considered in its design. In response to this, the present study evaluates a price responsive MPC strategy for a solar thermal heating system integrated with thermal energy storage (TES) for buildings with high occupancy variability. The coupled system supplies the building heating through a low temperature underfloor heating system. A case study University building in Nottingham, UK was employed for evaluating the feasibility of the proposed heating system controlled by MPC strategy. The MPC controller aims to optimize the solar heating system’s operation by dynamically adjusting to forecasted weather, occupancy, and solar availability, balancing indoor comfort with energy efficiency. By effectively integrating with thermal energy storage, it maximizes solar energy utilization, reducing reliance on non-renewable sources and ultimately lowering energy costs. The developed model has undergone verification and validation process, utilizing both numerical simulations and experimental data. The result shows that the solar hot water system provided 63% heating energy in total for the case study classroom and saved more than half of the electricity cost compared with that of the original building heating system. The electricity cost saving has been confirmed resulting from the energy shifting from high price periods to medium to low price periods through both active and passive heating energy storages.

show abstract

Extraction method of typical IEQ spatial distributions based on low-rank sparse representation and multi-step clustering

Yang,

Geng,

Tang

et al. 2024

Build. Simul.

View full text Add to dashboard Cite

Model predictive control of indoor thermal environment conditioned by a direct expansion air conditioning system

Cited by 10 publications

References 48 publications

Energy saving adaptive control algorithm for air conditioning in high heat density computer rooms

Energy saving adaptive control algorithm for air conditioning in high heat density computer rooms

Evaluation of model predictive control (MPC) of solar thermal heating system with thermal energy storage for buildings with highly variable occupancy levels

Extraction method of typical IEQ spatial distributions based on low-rank sparse representation and multi-step clustering

Contact Info

Product

Resources

About