Air Conditioning Load Research Articles

Study of thermal and humidity environment and prediction model in impinging jet ventilation rooms based on thermal and moisture coupling

Variations of indoor humidity have significant influence on thermal comfort, calculation accuracy of air-conditioning load and selection of design parameters. As one of the most promising advanced ventilation strategies, impinging jet ventilation (IJV) has received much attention. However, few study concerned its coupled indoor thermal and humidity environment and there is also no complete mathematical model for the IJV to synchronously predict its indoor temperature and humidity distributions. Therefore, this study first simulated the thermal and humidity environment in IJV rooms with considering the coupled thermal and moisture transfer effect. The results demonstrate a significant correlation between temperature and humidity distributions in IJV, with a correlation coefficient of approximately 0.4. Then, a simplified theoretical model for predicting temperature and humidity distributions in the IJV was established based on the theory of nodal model, and the key parameters within the model that are influenced by indoor airflow pattern were also identified. Next, the model results were compared with the simulation ones and showed that the proposed model is with a mean absolute error of 0.4°C for the prediction of temperature distribution and of 0.21g/kg for the prediction of humidity distribution. These discrepancies are sufficiently minor to validate the accuracy of the proposed model. Last, the potential application value of the proposed model was discussed. Overall, the current study contributes to extend the application of the existing coupled thermal and humidity models to IJV scenarios, and provides a robust theoretical foundation for the more rational design and practical application of the IJV.

Optimising load control of multi-type air conditioning in demand-side management

ABSTRACT With the continuous expansion of renewable energy capacity and the increasing demand for thermostatically controlled loads in China, new challenges have arisen for maintaining power supply stability. This paper establishes a control model for the aggregation of different types of air conditioning loads. To improve unsatisfactory demand response outcomes, a novel incentive mechanism based on tracking the target load curve is proposed. Additionally, a cost model for air conditioning load aggregators and the regional grid operator, along with an implementation scheme for trading mechanisms, is also developed. Case simulations validate the effectiveness and rationality of the proposed incentive mechanism.

A simplified method for calculating air conditioning load of multi-family housing community considering the spatiotemporal distribution of occupants

Occupant behavior is considered a vital factor influencing housing energy consumption by adjusting the indoor thermal environment and the use of service system. Multi-family housing is popular in the hot summer and cold winter (HSCW) zone of China, where residents typically adopt a “partial time, partial space” approach to environmental control. Therefore, the behavioral differences among families pose challenges for predicting hourly energy consumption at community scale. Taking Changsha, a typical city in the HSCW zone, as an example, this study investigates and summarizes the spatiotemporal distribution characteristics of multi-family occupants, analyzing their impact on air conditioning (AC) load under ideal environmental control conditions (i.e., AC is on where is occupied). Hourly AC load modified equations and multi-family occupancy modified coefficients are introduced to construct a method for estimating hourly AC load of residential communities that comprehensively considers the spatiotemporal distribution of multi-family occupants. The results show that the proposed method achieves fitting degrees of 87%-92.4% for predicting heating load and 77.9%-84.9% for predicting cooling load. This method not only dramatically reduces the workload and time required for physical modeling of residential communities, but also enables fast and refined simulation of the hourly AC load.

Optimal cost predictive BMS considering greywater recycling, responsive HVAC, and energy storage

A crucial aspect of a sustainable city is ensuring that energy and water supplies can adequately meet urban demand. With the scarcity of natural resources and growing electricity and water demand, it becomes increasingly important for consumers to manage their resource usage more efficiently. This paper proposes a novel perspective of demand-side management coordination strategy for a building's water-energy nexus to enhance the resilience and efficiency of the overall electricity-water-heating system. The model is formulated to optimally coordinates the onsite greywater recycling system, heating, ventilation, air conditioning (HVAC) loads, and distributed energy generation systems with a bidirectional grid connection in a residential building. All subsystems are controlled by a model predictive controller (MPC) receiving real-time time of use (ToU) pricing from electricity and water utilities. The presented mixed integer linear programming model is verified to meet the customers' demands while reducing the operational costs. Results are compared with benchmark systems lacking the water recycling or energy storage system showing 8.3 % operational cost reduction while reducing potable water consumption by 21.5 %. The effect of increased MPC control horizon is also studied showing reduction in cost with increased horizon. Detailed analysis of the proposed framework computational burden and effect of prediction errors is performed to prove the MPC adaptability and robustness. Testing under increased room size and different user preferences further validate the efficacy of the proposed scheme in reducing the operational costs.

The Cost-Optimal Control of Building Air Conditioner Loads Based on Machine Learning: A Case Study of an Office Building in Nanjing

Building envelopes and indoor environments exhibit thermal inertia, forming a virtual energy storage system in conjunction with the building air conditioner (AC) system. This system represents a current demand response resource for building electricity use. Thus, this study centers on the CatBoost algorithm within machine learning (ML) technology, utilizing the LASSO regression model for feature selection and applying the Optuna framework for hyperparameter optimization (HPO) to develop a cost-optimal control method for minimizing building AC loads. This method addresses the challenges associated with traditional load forecasting and control methods, which are often impacted by environmental temperature, building parameters, and user behavior uncertainties. These methods struggle to accurately capture the complex dynamics and nonlinear relationships of AC operations, making it difficult to devise AC operation and virtual energy storage scheduling strategies effectively. The proposed method was applied and validated using a case study of an office building in Nanjing, China. The prediction results showed coefficient of variation in root mean square error (CV-RMSE) values of 6.4% and 2.2%. Compared with the original operating conditions, the indoor temperature remained within a comfortable range, the AC load was reduced by 5.25%, and the operating energy costs were reduced by 24.94%. These results demonstrate that the proposed method offers improved computational efficiency, enhanced model performance, and economic benefits.

Using the Groundwater Cooling System and Phenolic Aldehyde Isolation Layer on Building Walls to Evaluation of Heat Effect

This study examines the thermal performance of building walls under full sunlight conditions using various insulation strategies. Specifically, it evaluates: (1) the effects of heat on building walls and indoor spaces; (2) the impact of groundwater cooling systems on thermal environments; (3) the influence of phenolic aldehyde insulation layers on heat transfer; and (4) the combined effects of groundwater cooling and phenolic aldehyde thermal insulation. Fluent–CFD (Computational Fluid Dynamics) was used in the study to simulate temperature transmission between the sun, the groundwater cooling system, and both indoor and outdoor spaces. Experimental analysis and simulations reveal that both the phenolic aldehyde insulation layer and the groundwater cooling system effectively reduce heat transfer, with the groundwater cooling system demonstrating the most significant impact. The phenolic aldehyde layer decreases the temperature difference between inner and outer walls by approximately 8 °C. The groundwater cooling system further reduces both inner and outer wall temperatures, helping to maintain cooler indoor environments. Simulation results indicate that, while the phenolic aldehyde layer effectively prevents external heat from penetrating into the room, it does not eliminate heat accumulation. In contrast, the groundwater cooling system efficiently dissipates heat, mitigating this issue. Groundwater analysis shows that maximum temperature differences occur at specific times of the day, with water flow effectively cooling the space. The combined use of the phenolic aldehyde insulation layer and the groundwater cooling system offers superior thermal performance. The phenolic layer provides effective heat blocking, while the groundwater system facilitates heat dissipation, optimizing indoor temperature and reducing air conditioning loads. This combination enhances overall comfort and energy efficiency, with the groundwater cooling system benefiting from reduced flow velocity and lower energy consumption.

Resilient robust model predictive load frequency control for smart grids with air conditioning loads

AbstractThis paper investigates the robust model predictive load frequency control problem for smart grids with wind power under cyber attacks. To accommodate intermittent power generation, the demand response of the system is considered by involving the air conditioning loads in the frequency regulation. In addition, the system uncertainties produced by the air conditioning load users and wind turbines when replacing traditional generator sets are considered. By using the cone complementary linearization algorithm and the linear matrix inequality technique, a resilience robust model predictive control strategy with mixed performance indexes is proposed. Furthermore, a rigorous derivation of the recursive feasibility of robust model predictive control is given. Finally, the simulation results of the two‐area load frequency control scheme show that the proposed model predictive control strategy is capable of realizing the load frequency control of the multi‐area smart grid and is robust to the parameter uncertainties and frequency regulation of the system. The results also show that the proposed model predictive control strategy has some resistance to cyber attacks and external disturbances.

A training-free non-intrusive air conditioning load monitoring method based on fuzzy comprehensive evaluation

Air conditioner (AC) is a grid friendly load, monitoring its operating status plays a key role in evaluating user side potential for participation in demand response. With continuous innovations in AC technology, the AC operation characteristics have shown increasing complexity, which poses significant challenges for non-intrusive AC load monitoring. Furthermore, obtaining labeled training data for target scenarios is often challenging, which hinders deployment of many Non-Intrusive Load Monitoring (NILM) methods in practice. In this regard, this article proposes a training-free non-intrusive AC load monitoring method based on fuzzy comprehensive evaluation (FCE), which is very easy to deploy and has good robustness. Firstly, considering various influence factors, a FCE model is constructed based on common knowledge about AC. This model simulates the manual labeling process to locate AC operation periods. Secondly, an AC power estimation method based on weighted filtering is established, in which weighted filter is used to eliminate the non-AC load components that may overlap during AC operation periods. Specifically, an optimization model is constructed to make the cumulative power of the AC events in its working cycle approach zero, with the filter weight vector as the objective variable, in which a heuristic greedy algorithm is used to find the optimal filter weight vector. When multiple feasible solutions with similar objective function values are derived in the solving process, the probability of load events appearing in AC and non-AC operation periods are compared. The final solution is selected, taking into account the likelihood of each feasible solution corresponding to a load event sequence belonging to AC load. The testing results on the PecanStreet dataset and multiple real-world measured datasets prove the effectiveness of the proposed method in monitoring complex ACs with poor waveform consistency.

Demand response strategies for air-conditioning systems based on direct load control: Considering envelope structures

In recent years, the surge in rural air-conditioning (AC) electricity consumption has led to an overall increase in peak loads in both winter and summer, presenting a major challenge to the reliability and stability of low-voltage distribution networks. Demand response (DR) strategy not only relieves the peak load, but also reduces the energy consumption and achieves the purpose of energy saving and emission reduction. Among the many DR strategies, direct load control (DLC) stands out as an effective way to quickly reduce building AC loads. Meanwhile, the building envelope is also one of the important factors affecting the AC load. A typical rural house in the Xi’an area of Shaanxi Province was used as a case study. A ground source heat pump system was developed, installed and used as an experimental platform in order to aid the study. Therefore, in this study, the combination of external envelope and DLC strategies was investigated to establish a simulation model of energy consumption in rural building complexes. Two DLC load control strategies were implemented under different external envelope conditions for annual AC energy simulation. The results show that the thickness of the external insulation layer has the greatest impact on the AC load of the building compared to the window-wall ratio and orientation. With a thickness of 0 mm–75 mm insulation, the DCCM strategy can effectively reduce AC energy consumption by 16%–18%; With a thickness of 0 mm–75 mm insulation, the TSCM strategy can effectively reduce AC energy consumption by 5%–8%.

Solutions for decarbonising urban bus transport: a life cycle case study in Saudi Arabia

With heavy reliance on fossil fuels, countries like Saudi Arabia face challenges in reducing carbon emissions from urban bus transportation. Herein, we address the gaps in evaluating proton-exchange membrane fuel cell buses and develop a globally relevant life-cycle assessment model using Saudi Arabia as a case study. We consider various bus propulsion technologies, including fuel cell buses powered by grey and blue hydrogen, battery electric buses, and diesel engines, and include the shipping phase, air conditioning load, and refuelling infrastructure. The assessment illustrates fuel cell buses using blue hydrogen can reduce emissions by 53.6% compared to diesel buses, despite a 19.5% increase in energy use from carbon capture and storage systems. Battery electric buses are affected by the energy mix and battery manufacturing, so only cut emissions by 16.9%. Sensitivity analysis shows climate benefits depend on energy sources and efficiencies of carbon capture and hydrogen production. By 2030, grey and blue hydrogen-powered fuel cell buses and battery electric buses are projected to reduce carbon emissions by 19.3%, 33.4%, and 51% respectively, compared to their 2022 levels. Fully renewable-powered battery electric buses potentially achieve up to 89.6% reduction. However, fuel cell buses consistently exhibit lower environmental burdens compared to battery electric buses.

When thermochromic material meets shape memory alloy: A new smart window integrating thermal storage, temperature regulation, and ventilation

Traditional windows have poor thermal insulation performance, resulting in significant indoor heat loss in winter and outdoor heat entry in summer. Thermochromic smart windows can effectively block solar radiant heat by automatically adjusting light transmittance, thereby reducing air conditioning loads and leading to significant energy savings. In this study, the poly N-isopropyl acrylamide (PNIPAm)-based thermochromic hydrogel, modified MXene nanoparticles, and NiTi shape memory alloy (SMA) are integrated to endow the smart window with heat storage, temperature control, and ventilation. The smart window achieves 88.6% visible light transmission and 70% solar modulation. The inclusion of MXene nanoparticles further enhances photothermal response efficiency, while the ventilation system ensures efficient and fresh indoor air circulation. Compared to the common glass, the smart window reduces the indoor temperature by 8 °C, demonstrating its excellent temperature regulation ability. Simulation results indicate that in Shanghai, Cairo, Singapore, and Kuwait, the employment of thermochromic smart windows can reduce heating, ventilation, and air conditioning energy consumption (HVAC) by 32.6%, 49.9%, 42.7%, and 34.1%, respectively. This versatile thermochromic smart window is expected to significantly improve building efficiency and occupant comfort, offering a sustainable solution for future building designs.

Model-based investigation on building thermal mass utilization and flexibility enhancement of air conditioning loads

Model-based investigation on building thermal mass utilization and flexibility enhancement of air conditioning loads

An evidence theory-based uncertainty quantification method of building thermal parameters for accurate reliability assessment of building air-conditioning design loads

Probabilistic methods are frequently employed in the reliability assessment of building air-conditioning design loads to quantify the uncertainty associated with building thermal parameters. However, its implementation requires some assumptions because of inadequate data in the early stages of design. These assumptions cause the uncertainty quantification results of the building thermal parameters to differ from reality, which may lead to a significant overestimation or underestimate of reliability. Therefore, an evidence theory-based method was proposed to quantify the uncertainty of the building thermal parameters. In this method, evidence theory was employed instead of probability theory because the former could make full use of small sample information to achieve objective uncertainty quantification. Taking an office building in Tianjin, China, as an example, the new method was tested, and its effectiveness was verified. The results showed that the uncertainty quantification results of the building thermal parameters based on the new method included those of the benchmark method. Under small sample conditions, the reliability assessment results based on the new method were closer to those of the benchmark compared with the traditional method.

Improved robust model predictive control for residential building air conditioning and photovoltaic power generation with battery energy storage system under weather forecast uncertainty

The rising demands for comfort alongside energy conservation underscore the importance of intelligent air conditioning control systems. Model Predictive Control (MPC) stands out as an advanced control strategy capable of addressing these demands. However, accurate prediction of all relevant variables remains a challenge in practical scenarios, complicating MPC’s ability to devise effective control actions amid prediction inaccuracies. To counteract this issue, this paper introduces an enhanced Double-Layer Model Predictive Control (DLMPC) algorithm. This innovative approach adjusts for discrepancies between forecasted and actual values without the need for additional variables and models, thereby reducing the adverse effects of prediction errors. Additionally, we develop precise models for room temperature simulation and for calculating air conditioning (AC) load and energy consumption, grounded in empirical data from residential settings and AC performance tests. Validation of these models demonstrates their efficacy in enabling MPC to formulate efficacious control strategies. When juxtaposed with a baseline model, the DLMPC algorithm significantly improves temperature regulation accuracy by up to 15.12% and achieves a 10.50% reduction in energy consumption over the heating season.

A novel vacuum-photovoltaic glazing integrated thermoelectric cooler/warmer for environmental adaptation: thermal performance modelling

Vacuum-photovoltaic glazing, renowned for exceptional thermal insulation and solar energy utilization, faces limitations in its adaptability to varying seasons. While it effectively reduces heat transmission into indoor spaces during summer, it becomes detrimental during winter. To address this challenge, this study introduces an innovative solution: vacuum-photovoltaic-thermoelectric (VPT) glazing, which integrates vacuum, photovoltaic and thermoelectric cooling/warming technologies. The theoretical models were developed and validated through WINDOW and Fluent simulations. A comparative analysis is conducted considering thermal performance under various environmental parameters. The results demonstrate that VPT glazing exhibits enhanced thermal performance, with interior glass temperature decreased by 3.0–9.6oC in summer while increased by 2.5–6.2oC in winter, accounting for ∼55.0 % reduction in air-conditioning load. Compared to vacuum-photovoltaic glazing, VPT glazing reduces the coupling U-value from 7.88 to 5.87 W m−2 K−1 in summer and increases from −0.31 to 2.61 W m−2 K−1 in winter. The solar heat gain coefficient decreases from 0.37 to 0.30 in summer and increases from 0.24 to 0.29 in winter. These results demonstrate the effectiveness of VPT glazing in adapting to different seasons and achieving better thermal environment performance. This study provides insights into VPT glazing design for environmental adaptation and offers implications for future research and applications in energy-efficient building technologies.

High-precision air conditioning load forecasting model based on improved sparrow search algorithm

Air conditioning load prediction is a crucial prerequisite for ensuring the efficient operation of air conditioning systems and enhancing demand-side response in electricity. Due to the uncertainty in model parameter settings and limitations of the Sparrow Search Algorithm (SSA2) itself, leading to significant human and material resources consumption during the prediction process, this study improves SSA (ISSA) to enhance air conditioning prediction efficiency and accuracy. The enhanced SSA is then combined with Long Short-Term Memory (LSTM). Firstly, Singular Spectrum Analysis (SSA1) is employed for feature extraction on the load to obtain more regular sub-signals, facilitating subsequent prediction tasks. Simultaneously, the Maximum Information Coefficient (MIC) is used to measure the correlation between data, assisting in selecting input variables for the model. Subsequently, each sub-signal is input into the ISSA-LSTM model for prediction, and the prediction results of each sub-signal are reconstructed to obtain the final prediction. Finally, data from air conditioning loads in two different types of buildings are collected to validate the model. Upon validation, it was found that the SSA1-ISSA- LSTM model exhibits superior performance compared to other advanced combination models. When predicting air conditioning loads in office building, the R2 value reaches 0.9955, with MAPE, MAE, and RMSE being 0.73 %, 4.658RT, and 6.1151RT respectively. When predicting air conditioning loads in medical building, the R2 is as high as 0.9920, with MAPE, MAE, and RMSE being 0.55 %, 5.8543RT, and 7.9599RT respectively.

Coordinated operation of PV/EV/Temperature controlled loads in distribution network

Abstract In order to solve the problems of voltage exceeding limits, local use, and reverse power flow due to the high proportion of distributed photovoltaic grid connections, a distributed photovoltaic uptake method considering demand-side resources and energy storage is proposed. Firstly, based on the equivalent thermal parameter (ETP) method, a load scheduling model for temperature-controlled loads is established. Secondly, with the objective function of minimizing the comprehensive operating cost of the distribution system, combined with the regulating effect of energy storage, stability and safety constraints for the distribution network, as well as the adjustable characteristics of thermal storage electric heating, air conditioning load, and electric vehicles, a demand side optimization model for the distribution network is established, which takes into account voltage safety and distributed photovoltaic uptake. Finally, based on historical data in Fengning County, Chengde City, and a certain park in Tangshan City, simulation analysis was conducted to obtain scheduling schemes and various scheduling indicators for the joint participation of demand-side resources and energy storage in the distributed photovoltaic local use of the distribution network under different permeability. The effectiveness and rationality of the proposed distributed photovoltaic consumption model were verified.

Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation, which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems. The online identification method is a computer-involved approach for data collection, processing, and system identification, commonly used for adaptive control and prediction. This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration, aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods. The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics, economic efficiency, and comfort. The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes, the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57, indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term. Overall, the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

Air-conditioning load characteristics and grey box predicting model in subway stations

With the continuous expansion of metro, energy conservation of air-conditioning system in subway stations draws the attention of the whole world. Most of the researches are focused on improving the energy efficiency of the system, while the load characteristics of subway stations are paid little attention. The inaccurate load prediction in the designing of air-conditioning system results in redundancy and low operating efficiency. Therefore, accurately predicting the air-conditioning load is crucial for energy saving in subway stations. In this paper, an innovative three-stage subway air-conditioning load test method was proposed and implemented in nine subway stations in Qingdao. Based on the test data, the special air-conditioning load characteristics of subway stations as underground buildings were analyzed. The results showed that electrical equipment load accounted for the largest part, reaching 53.7 %, while only 42 % of the equipment power was converted into air-conditioning load. The positive role of the envelope should not be ignored, especially in the case of entrance and exit walls, which bore almost all of the fresh air load. Based on the three-stage test, a grey box model was established and the error was validated to be within 6 %. With this model, the load of air-conditioning system can be calculated by inputting passenger flow, power of electrical equipment and public area. This paper provides a reference for the design and operation of air-conditioning system in subway stations.

A hierarchical coordinated control strategy of air‐conditioning loads for peak regulation service

AbstractThe increased penetration of renewable energy sources and the intensification of peak‐valley differences present challenges to peak regulation in the power system. Fulfilling the peak regulation needs of the power system solely through generation‐side resources proves to be challenging. Large‐scale fixed frequency air‐conditioning (FFAC) and inverter air‐conditioning (IAC) are high‐quality flexible load resources. This paper proposes a hierarchical coordinated control strategy of air‐conditioning (AC) loads for peak regulation service. In the first layer of the control strategy, the load aggregator collaborates with multiple AC groups to ensure the completion of peak regulation tasks with specific power‐constrained requirements. In the second layer of the control strategy, the control centre of each group optimizes the temperature adjustment values for each AC load, aiming to reduce incentive costs. Finally, the proposed method is validated through simulations, demonstrating its capability to achieve coordinated control of FFAC and IAC loads under various power‐constrained requirements. Furthermore, the simulation demonstrates the effectiveness of the control strategy in reducing user discomfort and the AC's incentive costs.