Air Conditioning Temperature Control Research Articles

Reliable sleep temperature regulation based on PMV model and dynamic thermal parameters

Quality of nighttime sleep is crucial. The common practice of air conditioners' sleep mode is to recommend a fixed sleep curve or suggest a pre-trained temperature curve based on geologic location. However, these models neglect the user preference and dynamic changes in the room's thermal environment, making it difficult to satisfy people of different ages and genders and resulting in discomfort during sleep. Demanding user to input their preferences or private data causes privacy issues, which leads to a bottleneck in solving this problem. Therefore, the paper proposes a new dynamic thermal parameter model for sleep, which learns the users' metabolic rate and thermal resistance of clothing and bedding through the user's undetected setting before sleep and simple feedback after waking up. These parameters fully reflect the inherent preferences of users and the thermal environment of rooms. After 1–2 rounds of feedback, the model can simulate the needs of users pretty well. In addition, we have optimized the PMV sleep model based on the dynamic thermal parameter model to regulate the temperature of air conditioner. In the controlled experiment, 84% of the participants expressed satisfaction with the regulated sleep temperature environment using the new model, which is 17% higher than that of the fixed curve. This model establishes a personalized model for users without obtaining their privacy information. In future research, wind speed control and humidity control can be introduced, and other sleep monitoring devices can be added to achieve more precise air conditioning temperature control.

Economic Model Predictive Control in Buildings Based on Piecewise Linear Approximation of Predicted Mean Vote Index

Energy shortage is a challenge for many countries, and building energy consumption accounts for a considerable proportion of global energy consumption. The main work of this paper is to optimize the energy consumption of heating, ventilating, and air conditioning (HVAC) systems in buildings based on economic model predictive control (EMPC). The cost in EMPC design includes energy consumption and predicted mean vote (PMV), which is an index that evaluates the thermal comfort of indoor occupants. In order to model the nonlinearity of the PMV index, we propose a lattice piecewise linear (PWL) approximation, which has high approximation precision and facilitates the resulting optimization problem, which is basically a piecewise quadratic programming problem. For the piecewise quadratic programming, we propose a descent algorithm that converges quickly and scales well with the length of the prediction horizon in the EMPC problem. The experimental results demonstrate that the proposed method saves 19.78% of the electricity cost compared to the conventional control strategy and significantly increases indoor comfort. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> — The motivation of this article is to provide a control strategy to reduce building energy consumption and ensure indoor thermal comfort. In most of the existing methods for air conditioning temperature control, the occupants’ comfort hasn’t been considered. In this paper, thermal comfort is described by the PMV index, which is basically nonlinear. In order to model the thermal comfort more accurately, in this paper, the PMV index is approximated piecewise linearly in order to meet the requirements of accuracy and computational efficiency. The resulting optimization problem is not hard to solve, and we provide an efficient algorithm for solving this optimization problem. Preliminary simulation experiments demonstrate that this approach is practical, i.e., it achieves energy reduction and ensures thermal comfort. Our strategy, however, has not yet been deployed in real buildings. In future research, we will propose similar techniques for large-scale systems in order to solve energy optimization problems containing multiple thermal zones and realize the proposed technique in real buildings.

Effects of weather forecasting on indoor comfort and energy savings in office buildings

ObjectiveTo explore whether a climate services approach can enable both energy saving and indoor comfort. MethodsAfter analysis of the local weather forecast, the air conditioning temperature of a government building was set to be adjusted at 7:30, 11:00, and 17:00. The indoor temperature and humidity were measured by using a MAPS6.0 sensor, and thermal comfort was calculated using ISO 7730 standards. Multiple regression analysis was used to examine the correlation between the weather forecast and chiller energy consumption. ResultsAfter a three-stage air conditioning temperature control process was implemented, predicted mean vote values between 0 and 0.5 were achieved for 79.1% of the working hours in the experimental period. Outdoor and air conditioning temperatures were the most critical factors affecting the hourly energy consumption of the air conditioner and water-cooled chiller. With every 1 °C increase in the outdoor temperature, the hourly energy consumption increased by 6.57. Moreover, with every 1 °C increase in the air conditioner temperature, the hourly energy consumption decreased by 8.63. DiscussionA predicted mean vote value of >0.5 was recorded for 15.9% of the working hours; this may be related to high indoor humidity, weather forecast inaccuracies, and the air conditioner being turned on too late in the day. From July to October 2021, 8749 kW h of electricity were saved, and carbon emissions were reduced by 1405 kg. Regulating air conditioning temperatures through weather forecasts can achieve energy savings.

Temperature Control Algorithm of Underground Air Conditioning in Station

Aiming at the air-conditioning temperature control of underground station, the air-conditioning temperature controller of underground station is designed based on proportional-integral-differential neural network, the bat algorithm is used to select the initial weights, the radial basis function neural network prediction model is used to optimize the design of the predictor, and the designed control system is simulated on the MATLAB simulation platform. The simulation results show that the dynamic characteristics and steady-state characteristics of the PID controller optimized by bat algorithm are better than those of the PID controller tuned by conventional methods. After optimization by radial basis function neural network prediction model, the rapidity and stability of controller are effectively improved.

A feedforward air-conditioning energy management method for high-speed railway sleeper compartment

In this paper, we propose a feedforward air conditioning temperature control method for high-speed railway locomotives with sleeper compartments to improve energy efficiency. First, we construct the geometric model of two typical types of compartments and three types of passengers. Then, based on the analysis of possible passenger layout patterns in each compartment, we utilize computational fluid dynamics simulations to calculate the optimal air volume for each pattern. The optimal air volume is calculated to guarantee the passenger comfort level and reduce the energy cost. In addition, we adopt an image recognition method to detect the number and types of passengers in each compartment. Passenger layout patterns serve as independent variables to determine the corresponding optimal air volume. Finally, numerical simulations were conducted to verify the effectiveness of the proposed method.

Improving the application of fuzzy RBF neural network in temperature control system

The temperature control system of aircraft ground air conditioning has the characteristics of complex work, non-linearity and time-varying working, the traditional PID control overshoot is too large and the response speed is low, which can not meet the needs of high efficiency and speed. In order to improve the control performance of aircraft ground air conditioning, a fuzzy RBF neural network optimized by the improved bacterial foraging(BFO) and flower pollination(PFA) algorithm was designed, and the air conditioning system was modeled and simulated. The analysis results show that the improved algorithm has better control ability than other algorithms in terms of control accutacy and anti-interference ability, effectively restraining the large lag and other problems, which provides a theoretical reference for the optimization of air conditioning temperature control system.

Research on Plant Air-conditioning Temperature Control Model and Simulation

Research on Plant Air-conditioning Temperature Control Model and Simulation

Design and Implementation of Humanized Temperature Control for Hotel Air-Conditioning

In some star hotels, humanized air-conditioning temperature control has become a necessary condition to improve the service. Obviously, the traditional single function of air temperature control cannot meet this requirement. This paper proposes an intelligent control system for air-conditioning combining, through which users can set desire temperature compared with the actual temperature detected, thus achieving humanized control. The experiments show that the proposed system achieves good results in humanized control of the air-conditioning temperature.

Study on PID Parameters Tuning Based on Particle Swarm Optimization

To address the PID parameter tuning problem, inspired by the swarm intelligence optimization algorithm, a tuning method of PID parameters based on particle swarm algorithm is proposed. To find an optimal set of PID control parameters in the target space, three parameters of the PID are as particles based on the fitness function. By designing optimization programs, air-conditioning temperature control system experiments were carried out as an example. The results show that the optimization can improve the dynamic control performance of the system.