Air Conditioning Energy Consumption Research Articles

Analysis and prediction of energy consumption in office buildings with variable refrigerant flow systems: A case study

Accurately predicting building air-conditioning energy consumption is particularly important for energy management. However, for small datasets, there is a lack of simple and effective prediction methods applicable to multiple buildings. In order to fully explore the energy consumption mode of Variable Refrigerant Flow (VRF) air conditioning system and improve the accuracy of the model. This study uses the weather data of an outdoor meteorological data collection system and the air conditioning system and energy consumption data of an office building in Jinan. Initial analysis revealed that occupant behavior and daily variations in outdoor air temperatures significantly impact the energy consumption of air conditioning systems. Then, by applying the random forest algorithm and Pearson correlation analysis, the study identified daily average outdoor air temperature and VRF indoor unit running time as critical features for prediction. Subsequently, three machine learning models—Multiple Linear Regression (MLR), Back Propagation Neural Network (BPNN), and Long Short-Term Memory (LSTM) neural network—were developed and assessed using a 4:1 training-to-testing set ratio. Comparative analysis revealed that all models performed robustly, with the MLR model exhibited superior predictive accuracy (R2 (Coefficient of Determination) = 0.98, MAE (Mean Absolute Error) = 0.005 kWh/(m2·day), and RMSE (Root Mean Square Error) = 0.007 kWh/(m2·day) for the testing set) and simplicity. Furthermore, the effectiveness of the MLR model in handling small sample sizes was confirmed through cross-validation of data from two additional office buildings, highlighting its suitability for predicting energy consumption of VRF air conditioning systems in office buildings.

A novel energy-saving and emission-reduction strategy based on facile preparation of thermal insulation coatings doped with modified vacuum ceramic microbeads for concrete protection

The development of thermal insulation protective coatings suitable for constructions and buildings offers a multifaceted approach: it substantially reduces indoor temperatures, enhances residential comfort, decreases air conditioning energy consumption, and conserves energy. Additionally, these coatings provide robust resistance against corrosive substances, thereby significantly prolonging the service life of concrete. In this work, the modification of vacuum ceramic microbeads (VCM) through the polymerization of catechol and hexamethylene diamine to form poly(catecholamine) (PCA) was investigated by FT-IR, XRD, and TEM analysis. Increased levels of incorporation of VCM@PCA further decreased thermal conductivity and increased surface hydrophobicity, while excessive incorporation detrimentally affected the coating's mechanical properties. PDMS/VCM@PCA coating demonstrated exceptional thermal insulation performance, reducing coating temperature by 15.9 °C under simulated sunlight exposure, and in outdoor experiments, the interior temperature of the simulated house with this coating was 4.2 °C lower than the external temperature, highlighting its potential for energy conservation, emission reduction, and building temperature regulation. Additionally, PDMS/VCM@PCA coating provided excellent protection for concrete structures, significantly enhancing abrasion resistance. It also improved the interfacial adhesion between the organic layer and concrete under dry, wet, and 10% NaCl immersion conditions. The addition of VCM@PCA further enhanced resistance to chloride ion penetration, effectively reducing chloride ingress. This thermal insulation protective coating presents a novel strategy for energy conservation, emission reduction, and sustainable development in the civil construction industry.

Increasing photovoltaic self-consumption for objects using domestic hot water systems

The technique of estimating the expected decrease in electricity consumption from the grid and using PV energy for the taken load schedule based on archival data for 5 years is refined. With full self-consumption (SC), the reduction of consumption from the grid can be increased by 9.5%–30.7% for a year according to the rated PV power. Consumption should increase when PV generation exceeds a certain value. A discrete time control of the power of an electric storage boiler (ESB) is proposed based on the deviation of the storage battery (SB) state of charge from a given schedule with a heating concentration during hours of high PV generation. In the considered application, it is possible to increase SC by up to 21%. Reducing the load in the evening allows us to use SB energy to reduce consumption from the grid at night. The possibility of complete photovoltaic SC when the ESB is used with an air conditioner is substantiated. Limitations for air conditioner energy consumption according to PV generation are determined. The system’s 24h model of energy processes is supplemented with a thermal model. The standard use of ESB with water temperature maintenance was also considered for comparison. ESB power control allows you to reduce daily energy consumption from the grid by 1.7–2 times. When combining an adjustable ESB with an air conditioner, it is possible to reduce consumption from the grid by 1.466–1.558 times at minimum and increase consumption from the grid by 2–5% at maximum air conditioner consumption.

Simulation and optimization design of air distribution in a cigarette production factory

Air-conditioning energy consumption accounts for a significant proportion of the total energy usage in large commercial buildings. This study utilizes computational fluid dynamics (CFD) to examine the airflow distribution in a cigarette factory with a high ceiling. Numerical simulations were performed to assess the effectiveness of various air supply methods. Using FLUENT, the researchers analyzed the uniformity of airflow, temperature and humidity fields under summer conditions. The findings indicate that employing an upper supply and lower return air method can decrease air-conditioning energy consumption by 7.25%. Field measurements were carried out to validate the numerical simulations, with the measured values used as initial parameters. The average deviation between the measured and simulated temperature and humidity in critical work areas was within 2%. The comparative analysis of the simulation and measurement results confirms the reliability and effectiveness of airflow organization simulations in large commercial buildings. These results offer a scientific foundation and computational guidance for designing and implementing energy-efficient upgrades to air-conditioning systems in similar industrial facilities.

The indoor thermal environment performance of various air-conditioning system configurations and airflow modes in a large space museum building

Optimizing air distribution in site museums is crucial for the long-term protection of heritage sites and the thermal comfort of visitors and staff. This study examined a site museum, analyzing the traditional suspended ceiling air-conditioning system and proposing a more rational renovation. Using computational fluid dynamics (CFD) numerical simulations, the study evaluated the effects of both systems on site protection and thermal comfort. The results indicated that the retrofit scheme could significantly reduce the indoor air velocity and temperature with the same air supply parameters, which would improve human thermal comfort and also reduce the negative impact of temperature and wind speed on the site. Additionally, the retrofit scheme cut air-conditioning energy consumption by 40% and reduced initial investment costs for a unit with a rated airflow of 62,000 m³/h. The optimization of the air-conditioning system ends was also carefully considered, resulting in a safer and easier installation and maintenance process, as well as enhancing the visual appeal of the museum’s interior. The proposed retrofit scheme can provide a reference for the design of air-conditioning systems in similar types of buildings.

Coordination Compound Guided a Novel Composite Film with Zn2V2O7 Nanoflake and VO2@Zn2V2O7 Nanoparticle for Enhanced Thermochromism Smart Window.

Thermochromic vanadium dioxide (VO2) can intelligently modulate the transmittance of indoor solar radiation to reduce the energy consumption of air conditioning in buildings. Nevertheless, it remains a great challenge to simultaneously improve the luminous transmittance (Tlum) and solar modulation ability (ΔTsol) of VO2. In this study, a novel approach is employed utilizing a coordination compound to finely tune the growth of a VO2 based composite film, yielding a hierarchical film comprising Zn2V2O7 nanoflakes and VO2@Zn2V2O7 core-shell nanoparticles. Remarkably, the resulting composite films showcase exceptional optical performance, achieving a Tlum of up to 73.0% and ΔTsol of 15.7%. These outcomes are attributed to the antireflection properties inherent in the nanoflake structure and the localized surface plasmon resonance of well-dispersed VO2 nanoparticles. In addition, the Zn2V2O7-VO2 film demonstrates remarkable environmental durability, retaining 90% of its initial ΔTsol even after undergoing aging at 100°C under 50% relative humidity for a substantial period of 30 days - a durability equivalent to ≈20 years under ambient conditions. This work not only achieves a harmonious balance between Tlum and ΔTsol but also introduces a promising avenue for the design of distinctive composite nanostructures.

Research on the Design of Green Roofs for Office Buildings in Xuzhou Based on Building Energy Consumption Evaluation

The roof is the part of a building that is exposed to solar radiation for the longest period, making green roofs particularly effective in reducing air conditioning energy consumption during the summer. This study aims to assess the advantages of modular green roofs in terms of energy savings and cost reduction during the summer in Xuzhou. By conducting field measurements and surveys under both air-conditioned and non-air-conditioned conditions and utilizing building energy simulation tools, the performance of green roofs with different parameters was compared. Using EnergyPlus, factors such as soil thickness, thermal conductivity, and leaf area index were simulated. The results indicated that green roofs have superior thermal performance in summer, with the daily cooling load per unit area for top-floor rooms being 1.05 kWh/m2, 0.21 kWh/m2 lower than that for bare roofs, achieving an energy saving rate of 16.7%. It is recommended that soil thickness not exceed 0.3 m and insulation thickness not exceed 0.05 m or be set to 0 m. Take building no. 2 of the Xuzhou material market as an example: with the optimized green roof, the energy saving rate increased to 27.0%, which is 12.4% higher than that of the original green roof. The suggested cost for modular green roofs is 204 RMB/m2.

Comparison of energy consumption prediction models for air conditioning at different time scales for large public buildings

Large public buildings have complex functions and high air conditioning energy consumption, and the prediction of energy consumption for air conditioning in different time scales can realise different energy saving management needs. Taking a large public building in Guangzhou as a research case, the long and short-term memory neural network (LSTM) is used as the basis to establish the machine learning model for energy consumption prediction for air conditioning at different time scales in terms of 10 min, hourly and daily, and a comparative analysis is carried out. The original data were analysed and processed by Z-Score and Local Outlier Factor (LOF) outlier detection methods. The relationship between meteorological parameters for Typical Meteorological Year (TMY) and the actual local meteorological parameters and energy consumption for air conditioning was analysed using linear regression, Pearson and Spearman correlation coefficients, taking meteorological parameters as input variables. The results show that comparing energy consumption for air conditioning with the meteorological parameters in TMY and actual local meteorological parameters, the actual meteorological parameters had a stronger correlation energy consumption for air conditioning. When the actual local meteorological parameters are taken as input variables, the mean absolute percentage error (MAPE) is reduced by about 0.85 %, and the coefficient of determination (R2) is increased by about 0.01. At the same time, the MAPE and R2 of the 10-min energy consumption prediction model are about 6.43 % and 0.9738, respectively, the MAPE and R2 of the hourly prediction model are about 9.29 % and 0.9568, respectively, while the MAPE and R2 of the daily prediction model are about 25.76 % and 0.7998. Meanwhile, an improved energy consumption prediction model is proposed to reduce the daily energy consumption prediction error to 6.36 % for MAPE, and the R2 of this model is 0.9927.

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%.

Reinforcement Learning-Based Auto-Optimized Parallel Prediction for Air Conditioning Energy Consumption

Reinforcement Learning-Based Auto-Optimized Parallel Prediction for Air Conditioning Energy Consumption

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.

Advancing personal thermal comfort prediction: A data-driven framework integrating environmental and occupant dynamics using machine learning

Various individual thermal preferences do not always lead to optimal thermal comfort, despite the high energy consumption of Heating, Ventilation, and Air Conditioning (HVAC) systems. Thermal comfort models often struggle to accurately predict occupants’ thermal sensations due to their inability to adapt to diverse scenarios. This research employs machine learning techniques to develop personalized data-driven thermal comfort models using a global database of 17,814 samples from the ASHRAE Global Thermal Comfort Database II. Specifically, the study implements random forest and k-nearest neighbor algorithms to analyze the influence of environmental factors (such as air temperature, outdoor temperature, relative humidity, and air velocity) and personal factors (including clothing insulation, activity level, and individual differences) on thermal comfort perception. The study systematically compares the predictive performance of different output types (thermal sensation vs. preference vote), input parameters (6 vs. 12), and machine learning methods. The optimized random forest model with 12 inputs demonstrated an accuracy of over 70% in predicting thermal preference votes, which was significantly higher than the 34% accuracy of the PMV model. The results present new optimized data-driven models and offer insights into the relative influence of parameters such as climate, building cooling strategies, and occupant age and gender. This research aims to enable personalized thermal comfort predictions to improve satisfaction and energy efficiency in various buildings.

A Method of Integrating Air Conditioning Usage Models to Building Simulations for Predicting Residential Cooling Energy Consumption

Great deviations in building energy consumption simulation are attributed to the simplified settings of occupants’ air conditioning (AC) usage schedules. This study was designed to develop a method to quantify the uncertainty and randomness of AC usage behavior and incorporate the model into simulations, in order to improve the prediction performance of AC energy consumption. Based on long-term onsite monitoring of household thermal environments and AC usage patterns, two stochastic models were built using unsupervised cluster and statistical methods. Based on the Monte Carlo method, the AC operation schedule was generated through AC opening duration, setpoints, and other relevant parameters, and was further incorporated into EnergyPlus. The results show that the ideally deterministic AC operation settings from the standard significantly overestimate the cooling energy consumption, where the value based on the fixed mode was 6.35 times higher. The distribution of daily AC energy consumption based on the stochastic modeling was highly consistent with the actual situation, thanks to the accurate prediction of the randomness and dynamics of residents’ AC usage patterns. The total cooling energy consumption based on two stochastic models was found to be much closer to the actual values. The work proposes a method of embedding stochastic AC usage models to EnergyPlus 22.1 benefits for an improvement in building energy consumption simulation and the energy efficiency evaluation regarding occupant behavior in the future.

Air-conditioning anthropogenic heat in high-density residential areas: Spatial patterns and impacts

The overheated urban environment has drastically increased air-conditioning (AC) usage, which induces extensive heat emissions from outdoor units. This phenomenon is more adverse in high-density residential areas as the air-conditioning anthropogenic heat (ACAH) would further cause localized heat accumulation in canyons, negatively impacting AC energy consumption and residents’ comfort. While previous research has explored the impact of ACAH on urban canyons, there is a lack of studies that systematically investigate the pattern, impacts, and mitigations of ACAH at a refined scale in high-density residential areas. This study aims to investigate the temperature distribution under ACAH and quantify its impact on cooling energy consumption and pedestrian thermal discomfort. High-density residential areas were categorized by different heights of buildings and directions of outdoor unit placement. Numerical simulations were conducted to study ambient temperature distribution. Results showed that the worst heat accumulation was at the windward position of the AC outdoor unit, and the outdoor temperature on the middle floor was 2.3 °C higher than that on the upper floor, resulting in a higher energy consumption of 6.3 W/m2. The worst case with taller windward buildings caused 55 % of the pedestrian areas to be more uncomfortable. Mitigation strategies were also proposed from multiple perspectives, including heat source optimization (e.g., cross-layout of AC outdoor units) and offering subsidies for AC electricity consumption gaps. The study provides a systematic research perspective on spatial heat accumulation patterns in high-density residential areas, which is instructive for mitigating urban warming and promoting sustainable urban development.

Study on indoor temperature optimal control of air-conditioning based on Twin Delayed Deep Deterministic policy gradient algorithm

The optimization of HVAC system control affects the thermal comfort of building environment and air-conditioning energy consumption. However, most existing studies concentrate on human thermal comfort and neglects to incorporate air-conditioning energy consumption required to establish a controlled indoor thermal environment within the policy, which results in air-conditioning system high energy consumption. This paper suggests an indoor temperature intelligent control method based on thermal comfort and energy consumption of air-conditioning, and the TD3 algorithm is employed as an intelligent control algorithm to obtain the optimal setpoint that maximizes the joint benefit of the occupant’s thermal comfort and the energy saving rate of air-conditioning system. The air-conditioning indoor temperature intelligent control model based on TD3 algorithm is trained by using the experimental data, and the method is simulated in TRNSYS simulation software to corroborate the efficacy and energy saving rate of the method. The results indicate that the indoor temperature intelligent control model based on thermal comfort and energy consumption of air-conditioning has less optimal adjustment of indoor temperature setpoints, which is more stable for the air-conditioning system and can satisfy the thermal comfort demand at the initial control stage to prevent significant fluctuations in human thermal sensation compared to the linear control method based on thermal sensation and the fuzzy control method based on thermal sensation. Besides, the average daily energy consumption can be reduced by 6.54% and 3.37% respectively, when compared to the linear control method based on thermal sensation and the fuzzy control method based on thermal sensation.

A systematic review of multi-output prediction model for indoor environment and heating, ventilation, and air conditioning energy consumption in buildings

A systematic review of multi-output prediction model for indoor environment and heating, ventilation, and air conditioning energy consumption in buildings

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.

The operation strategy of river water source heat pump cold source system in a chemical industrial park in a region with hot summers and cold winters

At present, the energy consumption of the whole process of buildings in China accounts for 45 % of the total energy consumption of the country, and the carbon emissions account for 50.6 % of the total emissions of the country. Among the energy consumption of buildings, air conditioning energy consumption occupies a major proportion, about 2/3. Traditional air conditioning cooling mainly uses refrigerants such as Freon to circulate and cool under high temperature and high pressure, which not only has high energy consumption but also can cause the external environment temperature to be too high. River water source heat pump is a renewable energy technology that can use water bodies including groundwater, surface water such as rivers, lakes, reservoirs and oceans. This project mainly studies the operation strategy of related units using river water as the cold source in a chemical industrial park in Chongqing, from unit selection, establishment of energy consumption model to unit operation strategy analysis and research, to achieve the best state of energy utilization.

Research on quantitative assessment method of multi-time scale regulation capability of heat source tower heat pump for grid regulation demand

Abstract In the context of power saving, the increasing energy consumption of air conditioning and the limited energy demand has become an urgent problem. Therefore, according to the feature that the accuracy of grid load forecasting improves gradually with the shortening of time scale and the flexible intra-day regulation characteristics of heat pump units, a multi-timescale regulation model of a heat pump system is developed. Aiming for the lowest operating costs and best performance, three system scheduling scenarios were developed: day-ahead, mid-day, and real-time, respectively. Through the coordination of multiple time scales, it is ensured that the output of the system meets the requirements of the grid. Taking the Wuhan area with four heat source tower units as an example, a simulation experiment comparing the performance of the multi-timescale scheduling method with the day-ahead scheduling method was conducted in the same system, which showed that compared to the day-ahead scheduling method, the multi-timescale scheduling method can reduce the system energy consumption by 13.1% and improve the system performance factor by 12.4%.

Comparative analysis of energy efficiency for three heating and cooling supply schemes in a region with hot summers and cold winters in a chemical industrial park

Building energy consumption in China accounts for 45% of the total national energy consumption, with air conditioning energy consumption representing approximately two-thirds of that. Therefore, energy efficiency in buildings is of utmost importance. This study focuses on a chemical industrial park located along the Fujiang River and compares three heating and cooling supply schemes: the river water source heat pump system, which utilizes river water as the heat source and heat sink; the water cooling unit and boiler system, which uses water-cooled electric compression chillers for cooling and an oil-fired boiler system for heating; and the split air conditioning and gas water heater scheme, which relies on refrigerants such as fluorine-containing compounds for cooling and a gas water heater for heating. By calculating the energy consumption of the above three schemes and conducting a comparative analysis, it is found that the river water source heat pump system exhibits significantly higher energy efficiency throughout the year compared to the water cooling unit and boiler system and the split air conditioning and gas water heater scheme. This highlights the notable energy efficiency advantage of the river water source heat pump system.