Building Air Conditioning Systems Research Articles

Multi-objective optimization design of green building envelopes and air conditioning systems for energy conservation and CO2 emission reduction

Configurations of the building envelope and the air conditioning system have a considerable effect on annual building energy efficiency. Taiwan government proposed two green building indices, namely the Envelope Energy Load (ENVLOAD) and the Performance of Air Conditioning System (PACS), as energy performance models of building envelopes and air conditioning systems. Relatively few studies, however, have been mainly conducted, using multiobjective optimization design for building envelope and air condition systems, with a goal to treat both the building energy systems and the envelope. Consequently, this study is to make contribution to this related filed by proposing a multi-objective optimal building envelope and air conditioning system energy performance decision model (MOBELM).This model was used to design the optimal configurations of building envelopes and air conditioning systems based on the ENVLOAD and PACS constrictions and the two conflicting objectives. The first refers to the minimal construction cost when the building envelope and air condition system are built. The second is concerned with the minimal CO2 emission when the envelope materials are manufactured and used to form the building envelope. In the case study, the optimized configurations that dominated the original unoptimized design could validate the MOBELM.

Empirical Modeling of Direct Expansion (DX) Cooling System for Multiple Research Use Cases

This study provides a general procedure to generate a direct expansion (DX) cooling coil system for a roof top unit (RTU), which is a typical heating ventilation and air-conditioning (HVAC) system for commercial buildings in the United States. Experimental data from a full-scale unoccupied 2-story commercial building is used for the HVAC modeling. The regression for identifying the model coefficients was carried out with multiple stages, and the results were validated with measured data. The model’s applicability was evaluated with multiple case studies, including a building energy simulation (BES) program validation, model-based predictive control (MPC), and fault diagnostics and detection (FDD).

Potential and Future Prospects of Geothermal Energy in Space Conditioning of Buildings: India and Worldwide Review

This paper presents modern trends in geothermal energy utilization, mainly focusing on ground source heat (GSH) pumps for space conditioning in buildings. This paper focuses on India along with a general review of studies around the world. Space conditioning of a building contributes to about 40–50% of the total energy consumed in buildings and has an adverse impact on the environment and human health. The India Cooling Action Plan (ICAP) estimates that the demand for electricity for heating and cooling of buildings will increase by over 700% in India at current levels by 2047 with an additional 800 GW of power generation capacity needed just to meet heating and cooling needs by 2050, of which about 70% is required for the residential sector only. It further intensifies as the demand for peak electric load sharply increases in summer because of the extensive use of building air conditioning systems. Researchers across the globe have tried different cooling systems and found that some systems can offer a certain amount of energy-efficient performance, and also occupant comfort. Therefore, this article examines the geothermal potential in buildings for space conditioning by critically reviewing experimental and numerical studies along with the future prospects of GSH pumps.

Predictive maintenance scheduling optimization of building heating, ventilation, and air conditioning systems

We present optimization methods for predictive maintenance scheduling of building heating, ventilation, and air conditioning (HVAC) systems via mixed-integer programming. The optimization framework we introduce is composed of optimization models and parameter generation methods. Optimization models with time discretized into daily time periods are developed to enable the consideration of slow operation-dependent degradation during long horizons while maintaining computational efficiency. To improve the quality of the obtained maintenance schedules, system operation is simultaneously optimized. Parameter generation methods are introduced to provide parameters for constructing operation-related constraints. The proposed optimization framework can account for various HVAC systems with complex configurations. We show the quality of the generated operation-related parameters, and we provide medium-horizon case studies of central plants to show the model performance.

Analysis of indoor human thermal comfort in Pelotas municipality, extreme southern Brazil.

The indoor human thermal comfort (HTC) was investigated in residences located in the Pelotas City, southern Brazil, by the effective temperature index (ETI). In this study, temperature and relative humidity were measured inside 429 houses, located in different regions of Pelotas city, from January 11 to August 27, 2019. Samples were obtained using HOBO data loggers, indoor sensors, installed in different regions of the municipality, in the context of a cohort study of children between 2 and 4years old and their respective mothers, led by Epidemiological Research Center of the Federal University of Pelotas (UFPEL). In general, all regions had average hourly values of effective temperature index above the comfort zone in summer and below the comfort zone in the winter. In terms of spatial variability, the indoor HTC was dependent on environmental factors such as lake breeze and indoor behavior factors, such as the use of air conditioning system in the downtown buildings.

Demand response through automated air conditioning in commercial buildings\u2014a data-driven approach

Building operation faces great challenges in electricity cost control as prices on electricity markets become increasingly volatile. Simultaneously, building operators could nowadays be empowered with information and communication technology that dynamically integrates relevant information sources, predicts future electricity prices and demand, and uses smart control to enable electricity cost savings. In particular, data-driven decision support systems would allow the utilization of temporal flexibilities in electricity consumption by shifting load to times of lower electricity prices. To contribute to this development, we propose a simple, general, and forward-looking demand response (DR) approach that can be part of future data-driven decision support systems in the domain of building electricity management. For the special use case of building air conditioning systems, our DR approach decides in periodic increments whether to exercise air conditioning in regard to future electricity prices and demand. The decision is made based on an ex-ante estimation by comparing the total expected electricity costs for all possible activation periods. For the prediction of future electricity prices, we draw on existing work and refine a prediction method for our purpose. To determine future electricity demand, we analyze historical data and derive data-driven dependencies. We embed the DR approach into a four-step framework and demonstrate its validity, utility and quality within an evaluation using real-world data from two public buildings in the US. Thereby, we address a real-world business case and find significant cost savings potential when using our DR approach.

Study on the calculation of unsteady radiant heat transfer load with stratified air distribution based on harmonic reaction method

The stratified air conditioning systems are widely used in large space buildings. Stratified air conditioning load (SACL) is the basis for determining the cooling capacity of air conditioning system, and it is also the key to assess its energy savings of stratified cooling. Calculation of the radiant heat transfer load (RHTL) is the difficult point to determine the SACL. In recent years, there have been many researches on the calculation of SACL. Most of them are steady-state calculation processes, and there are few studies on dynamic calculation processes. Therefore, the RHTL is taken as the research object, a novel calculation method is proposed according to the harmonic reaction method (HRM) and verified by the experiments. Experimental radiant heat transfer load can be received through convection and radiation separating method. The experimental and theoretical values of RHTL were compared and it was found that the theoretical values were in good agreement with experimental values. Therefore, the proposed method can be used to predict the unsteady RHTL accurately. Based on the verified calculation method, the effects of outdoor climatic conditions, building types and stratified heights on RHTL of an actual large space building are analyzed, which will contribute to the design and optimization of stratified air conditioning system for large space buildings.

Early prediction of cooling load in energy-efficient buildings through novel optimizer of shuffled complex evolution

The crucial significance of proper management of heating, ventilating, and air conditioning systems in energy-efficient buildings were the main reason for dedicating this study to test a novel approach for this task. Shuffled complex evolution (SCE) is an efficient metaheuristic technique that is used to optimize the performance of a multi-layer perceptron neural network (MLP) for accurate prediction of cooling load (CL). The CL information of 768 residential buildings, obtained from a vast computer simulation in the published literature, is used to train and validate the performance of the proposed model. The results showed that the SCE could properly surmount the computational drawbacks of the MLP, as its learning and prediction accuracies are enhanced by 19.52 and 22.84%, respectively. Also, the SCE outperformed two benchmark optimizers of moth–flame optimization and optics inspired optimization in both training and testing phases. Another advantage of the tested SCE-MLP was the considerably simpler structure, and consequently, shorter computation time (722 vs. 1050 and 46,192 s). Therefore, the proposed model can be promisingly used in practice for the early prediction of CL in energy-efficient buildings.

Baseline estimation of commercial building HVAC fan power using tensor completion

Commercial building heating, ventilation, and air conditioning (HVAC) systems have been studied for providing ancillary services to power grids via demand response (DR). One critical issue is to estimate the counterfactual baseline power consumption that would have prevailed without DR. Baseline methods have been developed based on whole building electric load profiles. New methods are necessary to estimate the baseline power consumption of HVAC sub-components (e.g., supply and return fans), which have different characteristics compared to that of the whole building. Tensor completion can estimate the unobserved entries of multi-dimensional tensors describing complex data sets. It exploits high-dimensional data to capture granular insights into the problem. This paper proposes to use it for baselining HVAC fan power, by utilizing its capability of capturing dominant fan power patterns. The tensor completion method is evaluated using HVAC fan power data from several buildings at the University of Michigan, and compared with several existing methods. The tensor completion method generally outperforms the benchmarks.

Reinforcement learning for whole-building HVAC control and demand response

This paper proposes a novel reinforcement learning (RL) architecture for the efficient scheduling and control of the heating, ventilation and air conditioning (HVAC) system in a commercial building while harnessing its demand response (DR) potentials. With advances in automated building management systems, this can be achieved seamlessly by a smart autonomous RL agent which takes the best action, for example, a change in HVAC temperature set point, necessary to change the electricity usage pattern of a building in response to demand response signals, and with minimal thermal comfort impact to customers. Previous research in this area has tackled only individual aspects of the problem using RL. Specifically, due to the challenges in implementing demand response with whole-building models, simpler analytical models which poorly capture reality have been used instead. And where whole-building models are applied, RL is used for HVAC control mainly to achieve energy efficiency goals while demand response is neglected. Thus, in this research, we implement a holistic framework by designing an efficient RL controller for a whole-building model which learns to optimise and control the HVAC system for improved energy efficiency and thermal comfort levels in addition to achieving demand response goals. Our simulation results show that by applying reinforcement learning for normal HVAC operation, a maximum weekly energy reduction of up to 22% can be achieved compared to a handcrafted baseline controller. Furthermore, by employing a DR-aware RL controller during demand response periods, average power reductions or increases of up to 50% can be achieved on a weekly basis compared to the default RL controller, while keeping occupant thermal comfort levels within acceptable bounds.

Energy management and demand response with intelligent learning for multi-thermal-zone buildings

This paper presents an optimal building energy management strategy for the demand response of multi-thermal-zone buildings in the smart electricity grid environment. The proposed method includes a machine learning model, based on a neural network, for a building heating ventilation and air conditioning system. The learned model is then applied to an optimization problem to determine the optimal management scheduling of building loads. The goal of the optimization problem is to minimize building electricity costs and reduce the overall building energy consumption during peak load hours while satisfying human comfort demand. To overcome the coupling issue between the building internal-heat-gain loads and the building heating ventilation and air conditioning system, an iterative algorithm is proposed to solve the optimization problem. In each iteration, a mixed-integer linear programming technique is used to solve a sub-optimization problem for the building internal-heat-gain loads and its results are then applied to another sub-optimization problem, solved by using a particle swarm technique, for the building heating ventilation and air conditioning system. The iterative optimization algorithm stops when convergence between the optimization for the building heating ventilation and air conditioning system and the optimization for the building internal-heat-gain loads is properly reached. EnergyPlus is used to build and simulate complex buildings with multiple-thermal zones according to real-life conditions. The simulation model is also used to test and evaluate the effectiveness of the proposed machine-learning model and the iterative optimization algorithm and the improvement of building energy management in terms of energy consumption efficiency, cost saving, and satisfaction of human comfort.

Estimating cooling production and monitoring efficiency in chillers using a soft sensor

Intensive use of heating, ventilation and air conditioning systems in buildings entails monitoring their efficiency. Moreover, cooling systems are key facilities in large buildings and can account up to 44% of the energy consumption. Therefore, monitoring efficiency in chillers is crucial and, for that reason, a sensor to measure the cooling production is required. However, manufacturers rarely install it in the chiller due to its cost. In this paper, we propose a methodology to build a soft sensor that provides an estimation of cooling production and enables monitoring the chiller efficiency. The proposed soft sensor uses independent variables (internal states of the chiller and electric power) and can take advantage of current or past observations of those independent variables. Six methods (from linear approaches to deep learning ones) are proposed to develop the model for the soft sensor, capturing relevant features on the structure of data (involving time, thermodynamic and electric variables and the number of refrigeration circuits). Our approach has been tested on two different chillers (large water-cooled and smaller air-cooled chillers) installed at the Hospital of Leon. The methods to implement the soft sensor are assessed according to three metrics (MAE, MAPE and $$R^2$$ ). In addition to the comparison of methods, the results also include the estimation of cooling production (and the comparison of the true and estimated values) and monitoring the COP indicator for a period of several days and for both chillers.

Field-based Analyses on Approach Temperatures for Performance Evaluation of Centralized Air Conditioning System in a Shopping Mall Building

This paper presents field analyses on the influences of condenser and evaporator approach temperatures of water cooled air conditioning system for a shopping mall. The mall building comprises food and non-food retail stores and uses a centralized air conditioning (AC) system to condition the entire building. The AC system comprises three chillers with the same cooling capacity. Chillers’ performance parameters included power consumption, condensing and evaporating temperatures, evaporator-condenser approach temperatures, pressure drop and cooling capacity parameters were hourly recorded for a period of one year. Performance characteristics of each chiller were evaluated at different condenser and evaporator approach temperatures and regression analyses were also established to examine the correlations and influences of the approach temperatures on the chillers’ performance. The analyses results showed that the increase of condenser approach temperature could increase the power consumption and reduce the coefficient of performance (COP) of the chillers. While evaporator approach temperature was found to have insignificant effect to the chiller performance. The results also showed that for the investigated chillers with condenser and evaporator approach temperatures maintained below 2 K, the approach temperatures had negligible correlation to the power consumption and the overall performance of the chillers. At this operational conditions, the water cooled air conditioning systems could operate at their best possible performance.

Optimal energy management of Ice thermal energy storage-based air conditioning system for commercial buildings in real-time – A review based on POET framework

This paper applied the POET framework to analyze and identify possible energy efficiency activities that may reduce energy costs in HVAC cooling systems with Ice Thermal Energy Storage (ITES) in order to achieve maximum potential cost-savings, particularly for cooling loads in commercial buildings. Significant cost savings may be realized by the optimal shifting of the energy usage profile to the least costly regions of the Time-of-Use (TOU) using the ITES system. Moreover, a further reduction of energy costs may be achieved by introducing renewable energy (RE) sourced systems. A potential reduction of approximately 50% in energy cost savings could be achieved with the application of the POET framework. Improvement in operational efficiency may be achieved if systems were optimally sized and controlled. Studies conducted on control methods have shown that Model Predictive Control (MPC) offers significant benefits of achieving reasonable operational efficiency compared to other methods, particularly on HVAC systems. Combined ITES and hybrid energy systems allow an additional advantage in demand-side management, per the TOU tariff and peak demand. This may ultimately result in minimal energy usage during the costly regions of the TOU tariff, which would minimize energy costs and technology and equipment efficiency may be improved.

Methodology for Energy Efficiency on Lighting and Air Conditioning Systems in Buildings Using a Multi-Objective Optimization Algorithm

The purpose of this article is to develop a methodology to apply to multi-objective optimization algorithms aimed at energy efficiency in buildings, considering aspects such as incremental cost, energy consumption, greenhouse gas emissions and energy efficiency levels of lighting and air conditioning system, according to the mandatory technical regulation in public buildings in Brazil. Presenting a solution to assist in the decision making of engineers, architects or building managers for the optimal arrangements’ choice for lighting and air conditioning equipment, considering each built environment and project profile. For the validation process, a basic building was created with 15 rooms spread over three floors, according to the most common construction parameters in the North of Brazil. First, different combinations of objective-function candidates were investigated to compose the multi-objective algorithm fitness function, analyzing its performance in two central scenarios: (1) adding some “baits” in air conditioning equipment files, and (2) without this inclusion. Thus, it was found that considering only three objective functions—incremental cost, energy consumption and the air conditioning energy efficiency coefficient—it is possible to get optimal non-dominated solutions in both scenarios, thus highlighting the robustness of the proposed methodology.

An encoder–decoder LSTM-based EMPC framework applied to a building HVAC system

Numerous studies have demonstrated the benefit of economic model predictive control (EMPC) applied to building heating, ventilation, and air conditioning (HVAC) systems. However, the construction and training of predictive models for building HVAC systems are widely recognized as a key technological barrier preventing large-scale adoption of EMPC for buildings. In this work, an encoder–decoder long short-term memory-based EMPC framework is developed. The key advantage of the approach is that a model may be automatically generated from a list of inputs and outputs. From the definition of inputs and outputs, the constructed model may be trained and automatically embedded into the EMPC framework for real-time estimation and control. The overall end-to-end EMPC framework from model training to on-line estimation and control are described. To this end, the encoder–decoder model provides a natural framework for state estimation (encoder), which is required to provide an initial condition for the predictive model of EMPC (decoder). Closed-loop simulations using EnergyPlus are performed to demonstrate the approach. The simulated closed-loop system consists of a building zone from a multi-zone building, which is served by an air handling unit-variable air volume HVAC system. For the HVAC example considered, the trained encoder–decoder model can predict the indoor air temperature and HVAC sensible cooling rate of a building zone over a two-day horizon with high accuracy. Considering a time-of-use electric rate structure, the EMPC, which manipulates the zone temperature setpoint, can reduce the HVAC power consumption cost relative to keeping the zone temperature setpoint at its maximum value (i.e., minimum energy approach).

Influence of sensible cooling load on indoor air distribution in an office room with ceiling cooling and mixing ventilation

A mixing ventilation system integrated with a ceiling cooling system is a potential advanced heating, ventilation and air conditioning system for modern office buildings with a high sensible cooling load. This paper presents an evaluation of the effect of sensible cooling load on the indoor air distribution in a typical office room with mixing ventilation and ceiling cooling. The vertical distributions of indoor air temperature, air velocity and contaminant (CO2) concentration were evaluated by the vertical air temperature difference, turbulence intensity and contaminant removal effectiveness. The results showed that when the chilled ceiling surface temperature was increased from 15.0°C to 23.0°C and supply air temperature was decreased from 22.0°C to 19.0°C, the average vertical air temperature difference, turbulence intensity and contaminant removal effectiveness were 0.2°C–0.3°C, 27%–32% and 0.53–0.81 as both internal and external sensible cooling loads were 41.5 W/m2. Moreover, these evaluation indices varied slightly as the external sensible cooling load was increased from 41.5 W/m2 to 69.5 W/m2, whereas they varied greatly as the internal sensible cooling load was increased from 41.5 W/m2 to 69.5 W/m2. Hence, the external sensible cooling load could slightly affect the indoor air distribution, whereas the internal sensible cooling load could clearly affect the indoor air distribution in an office room with mixing ventilation and ceiling cooling.

Annual Energy Consumption Cut-Off with Cooling System Design Parameter Changes in Large Office Buildings

A variety of greenhouse gas reduction scenarios have been proposed around the world to ensure sustainable developments and strengthen the global response to the climate change. To cope with this, it is urgently needed to reduce the amount of energy used for the heating, ventilating, air conditioning, and refrigerating (HVAC&R) systems in large buildings. This study discusses the reduction of cooling energy in large office buildings through the minimization of changes in components and equipment, such as heat source equipment and pumps, changes in the layout and operating methods of chilled water circulation pumps, and changes in the temperatures of chilled and condenser water. To do this, this study targeted an entire cooling system consisting of a hydronic system, a chiller, and a cooling tower, and conducted a quantitative analysis of the energy consumption and of the reduction achieved through a change in the pumping system type in the cooling system and a change in the Korean standard design and temperature of chiller and cooling tower via EnergyPlus simulations. The simulation results showed a cooling energy reduction of 103.2 MWh/yr, around 15.7%, where the primary constant-speed system (Case A) was changed to a primary variable-speed pump (Case B) in the configuration with a chilled water circulation pump. To reduce the cooling energy further, annually 142.3 MWh, around 21.7%, Case C in this study changed the outlet temperature of the chiller and temperature difference from 7 °C, 5 K to 9 °C, 9 K. Finally, when applying a change in the condenser water production temperature from 32 to 23.9 °C in accordance with ASHRAE Standard 90.1 for Case D, a cooling energy saving of 182.4 MWh/yr was observed, which is about 27.8%.

Evaluating the perception of thermal environment in naturally ventilated schools in a warm and humid climate in Nigeria

Field study was conducted in naturally ventilated primary school buildings in a warm and humid environment in Imo State, Nigeria to determine the thermal comfort perception of young children (aged 7–12 years) and to understand the thermal conditions in the classrooms. The comfort temperature was investigated in two types of classroom buildings during the rainy and dry seasons from October 2017 to May 2018. Approximately 7050 completed valid questionnaires were collected from 330 young children repeatedly surveyed twice a day. The children answered comfort questions at the same time the indoor and outdoor thermal variables were being measured. Results indicated that the combined ‘open-space’ classrooms produced a neutral temperature of 28.8°C with comfort range, 25.2–32.3°C. The neutral temperature of the combined ‘enclosed-plan’ classrooms is 28.1°C with 25.8–30.5°C as the comfort range. The differences in the comfort perceptions may be attributed to the differences in the architectural characteristics of both categories of classroom buildings. High temperature tolerance was shown by the participating children in the study area. This article, therefore, suggests that installing air conditioning in primary schools in the warm humid environment in Nigeria may not be necessary as it could lead to unnecessary energy consumption and carbon emission. Practical application: This work is part of the main research work that pioneers research on thermal comfort in public primary school classrooms in Nigeria. The findings from this study on the acceptable indoor temperatures in naturally ventilated classrooms in the warm and humid climate in Nigeria are important information for building services engineers and architects. The young children in these classrooms can accept high indoor temperatures. The intention of this information is to discourage high energy usage in heating, ventilation and air-conditioning (HVAC) system in primary school buildings in the study area, while maintaining the acceptable thermal comfort levels.

Retrofitting an Existing Office Building in the UAE Towards Achieving Low-Energy Building

Retrofitting an existing building can oftentimes be more cost-effective than building a new facility. Since buildings consume a significant amount of energy, particularly for heating and cooling, and because existing buildings comprise the largest segment of the built environment, it is important to initiate energy conservation retrofits to reduce energy consumption and the cost of heating, cooling, and lighting buildings. However, conserving energy is not the only reason for retrofitting existing buildings. The goal should be to create a high-performance building by applying an integrated, whole-building design process to the project during the planning phase that ensures that all key design objectives are met. This paper presents a real case study of the retrofitting of an existing building to achieve lower energy consumption. Indeed, most of the constructed buildings in the UAE are unsuitable for the region, which is characterized by a very harsh climate that causes massive cooling loads and energy consumption due to an appropriate selection of design parameters at the design level. In this study, a monthly computer simulation of energy consumption of an office building in Sharjah was carried out under UAE weather conditions. Several parameters, including the building orientation, heating, ventilation, and air conditioning (HVAC) system, external shading, window-to-wall ratio, and the U-values of the walls and the roof, were investigated and optimized to achieve lower energy consumption. The simulation shows that the best case is 41.7% more efficient than the real (original) case and 30.6% more than the base case. The most sensitive parameter in the retrofitting alternatives is the roof component, which affects the energy savings by 8.49%, followed by the AC system with 8.34% energy savings if well selected using the base case. Among the selected five components, a new roof structure contributed the most to the decrease in the overall energy consumption (approximately 38%). This is followed by a new HVAC system, which leads to a 37% decrease, followed by a new wall type with insulation, resulting in a 20% decrease.