Modeling and optimization of HVAC energy consumption

A data-driven approach for minimization of the energy to air condition a typical office-type facility is presented. Eight data-mining algorithms are applied to model the nonlinear relationship among energy consumption, control settings (supply air temperature and supply air static pressure), and a set of uncontrollable parameters. The multiple-linear perceptron (MLP) ensemble outperforms other models tested in this research, and therefore it is selected to model a chiller, a pump, a fan, and a reheat device. These four models are integrated into an energy optimization model with two decision variables, the setpoint of the supply air temperature and the static pressure in the air handling unit. The model is solved with a particle swarm optimization algorithm. The optimization results have demonstrated the total energy consumed by the heating, ventilation, and air-conditioning system is reduced by over 7%.     

Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm

A data-driven approach for the optimization of a heating, ventilation, and air conditioning (HVAC) system in an office building is presented. A neural network (NN) algorithm is used to build a predictive model since it outperformed five other algorithms investigated in this paper. The NN-derived predictive model is then optimized with a strength multi-objective particle-swarm optimization (S-MOPSO) algorithm. The relationship between energy consumption and thermal comfort measured with temperature and humidity is discussed. The control settings derived from optimization of the model minimize energy consumption while maintaining thermal comfort at an acceptable level. The solutions derived by the S-MOPSO algorithm point to a large number of control alternatives for an HVAC system, representing a range of trade-offs between thermal comfort and energy consumption.     

A cost‐effective operating strategy to reduce energy consumption in a HVAC system

The operation of the building heating, ventilating, and air conditioning (HVAC) system is a critical activity in terms of optimizing the building's energy consumption, ensuring the occupants' comfort, and preserving air quality. The performance of HVAC systems can be improved through optimized supervisory control strategies. Set points can be adjusted by the optimized supervisor to improve the operating efficiency. This paper presents a cost‐effective building operating strategy to reduce energy costs associated with the operation of the HVAC system. The strategy determines the set points of local‐loop controllers used in a multi‐zone HVAC system. The controller set points include the supply air temperature, the supply duct static pressure, and the chilled water supply temperature. The variation of zone air temperatures around the set point is also considered. The strategy provides proper set points to controllers for minimum energy use while maintaining the required thermal comfort. The proposed technology is computationally simple and suitable for online implementation; it requires access to some data that are already measured and therefore available in most existing building energy management and control systems. The strategy is evaluated for a case study in an existing variable air volume system. The results show that the proposed strategy may be an excellent means of reducing utility costs associated with maintaining or improving indoor environmental conditions. It may reduce energy consumption by about 11% when compared with the actual strategy applied on the investigated existing system. Copyright © 2007 John Wiley & Sons, Ltd.     

System optimization for HVAC energy management using the robust evolutionary algorithm

For an installed centralized heating, ventilating and air conditioning (HVAC) system, appropriate energy management measures would achieve energy conservation targets through the optimal control and operation. The performance optimization of conventional HVAC systems may be handled by operation experience, but it may not cover different optimization scenarios and parameters in response to a variety of load and weather conditions. In this regard, it is common to apply the suitable simulation–optimization technique to model the system then determine the required operation parameters. The particular plant simulation models can be built up by either using the available simulation programs or a system of mathematical expressions. To handle the simulation models, iterations would be involved in the numerical solution methods. Since the gradient information is not easily available due to the complex nature of equations, the traditional gradient-based optimization methods are not applicable for this kind of system models. For the heuristic optimization methods, the continual search is commonly necessary, and the system function call is required for each search. The frequency of simulation function calls would then be a time-determining step, and an efficient optimization method is crucial, in order to find the solution through a number of function calls in a reasonable computational period. In this paper, the robust evolutionary algorithm (REA) is presented to tackle this nature of the HVAC simulation models. REA is based on one of the paradigms of evolutionary algorithm, evolution strategy, which is a stochastic population-based searching technique emphasized on mutation. The REA, which incorporates the Cauchy deterministic mutation, tournament selection and arithmetic recombination, would provide a synergetic effect for optimal search. The REA is effective to cope with the complex simulation models, as well as those represented by explicit mathematical expressions of HVAC engineering optimization problems.     

Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm

This paper examines optimal control strategies of variable air volume air conditioning system. The control strategies included a base control strategy of fixed temperature set point and two advanced strategies for insuring comfort and indoor air quality (IAQ). The first advanced control adjusts the fresh air supply rate and the supply air temperature to maintain the temperature set point in each zone while assuring indoor air quality. The second strategy controls the fresh air rate and the supply air temperature to maintain an acceptable thermal comfort and IAQ in each zone. The optimization problem for each control strategy is formulated based on the cost of energy consumption and constrained by system and thermal space transient models. The optimization problem is solved using genetic algorithm. The optimization scheme/model is applied to a case study for a building floor in Beirut weather. The thermal space and system component models were validated for the base strategy using Visual DOE 4.0 software [Architectural Energy Cooperation, San Francisco, USA; 2005 〈www.archenergy.com〉]. Energy savings up to 30.4% were achieved during the summer season of four months with the optimized advanced strategies when compared with the conventional base strategy while comfort and IAQ were satisfied.     

Optimization of wind turbine energy and power factor with an evolutionary computation algorithm

An evolutionary computation approach for optimization of power factor and power output of wind turbines is discussed. Data-mining algorithms capture the relationships among the power output, power factor, and controllable and non-controllable variables of a 1.5 MW wind turbine. An evolutionary strategy algorithm solves the data-derived optimization model and determines optimal control settings. Computational experience has demonstrated opportunities to improve the power factor and the power output by optimizing set points of blade pitch angle and generator torque. It is shown that the pitch angle and the generator torque can be controlled to maximize the energy capture from the wind and enhance the quality of the power produced by the wind turbine with a DFIG generator. These improvements are in the presence of reactive power remedies used in modern wind turbines. The concepts proposed in this paper are illustrated with the data collected at an industrial wind farm.     

Cooling output optimization of an air handling unit

A data-driven optimization approach for minimization of the cooling output of an air handling unit (AHU) is presented. The models used in this research are built with data mining algorithms. The performance of dynamic models build by four different data mining algorithms is studied. A model extracted by a neural network is selected for identifying the functional mapping between specific outputs and controllable and non-controllable inputs of the AHU. To minimize the cooling output while maintaining the corresponding thermal properties of the supply air within a certain range, a bi-objective optimization model is proposed. The evolutionary strategy algorithm is applied to solve the optimization problem with the optimal control settings obtained at each time stamp. The minimized AHU’s cooling output reduces the chiller’s load, which leads to energy savings.     

A multi‐objective evolutionary optimization of fuzzy controller for energy conservation in air conditioning systems

This paper presents the use of evolutionary optimization approach to design and tune smart fuzzy controllers for heating, ventilation, and air conditioning systems or HVAC. The objective is to optimize energy consumption while accounting for user comfort requirements. The problem of energy conservation in air conditioning systems becomes a multi‐objective optimization constrained problem, which enlarges the solution search space. To solve this problem, a multi‐objective evolutionary optimization technique based on genetic algorithm (GA) is proposed. A physical experimental setup is constructed for data collection and formulation of mathematical model. A fuzzy controller is initially designed through expert knowledge, and GA is then used to tune the rules and membership functions of the fuzzy controller in order to optimize multiple objectives. Simulations and real experiments are compared to determine the effectiveness of the proposed strategy. As compared to the controller present in the real experimental air conditioner, approximately 15% energy is successfully saved with no increase in average individual dissatisfaction or discomfort index. Also, a decrease in peak individual dissatisfaction or discomfort index from 91% to 62% is observed. Copyright © 2013 John Wiley & Sons, Ltd.     

基于建筑环境的空调系统节能分析

针对暖通空调节能的趋势 ,分析了建筑环境对暖通空调系统的影响 ,提出从建筑环境方面考虑问题是暖通空调节能的必要途径 ,概括地介绍了空调能源的发展方向和空调节能的多种手法     

Global optimization for overall HVAC systems––Part II problem solution and simulations

This paper presents the solution for the global optimization problem for overall heating, ventilating and air conditioning (HVAC) systems using a modified genetic algorithm. The whole implementation procedure of the proposed optimal method is provided. Simulation studies for a pilot scale centralized HVAC plant by the proposed optimal method show that the proposed method indeed improves the system performance significantly compared with traditional control strategies.     

Various criteria in optimization of a geothermal air conditioning system with a horizontal ground heat exchanger

Thermodynamic and thermoeconomic optimization of a horizontal geothermal air conditioning system has been performed. A model based on energy and exergy analysis is presented here. An economic model of the system is developed according to the Total Revenue Requirement (TRR) method. The objective functions based on the thermodynamic and thermoeconomic analysis are developed. An artificial intelligence technique known as evolutionary algorithm has been utilized for optimization. This approach has been applied to minimize either the total levelized cost of the system product or the exergy destruction of the system. Three levels of optimization including thermodynamic single objective optimization, thermoeconomic single objective optimization and multi‐objective optimization (with simultaneous optimization of thermodynamic and thermoeconomic objectives) are performed. In multi‐objective optimization, both thermodynamics and thermoeconomic objectives are considered, simultaneously. In the case of multi‐objective optimization, an example of decision‐making process for selection of the final solution from available optimal points on Pareto front is presented here. The results obtained using the various optimization approaches are compared and discussed. Copyright © 2009 John Wiley & Sons, Ltd.     

Control of a fuel cell based on the SIRMs fuzzy inference model

A typical working mode for the fuel cell hybrid system is that the fuel cell produces constant power output while the auxiliary storage energy device such as ultracapacitor or battery provides the deviation between the desired power demand and the value of the actual operation. This paper concentrates on the control of the fuel cell flow system. The system which is like an under-actuated mechanical system needs to control two objects (the cathode pressure and the air flow) with one manipulated variable (the set voltage of the air mass flow controller). A SIRMs-based fuzzy inference model is successfully implemented in the system. Online random search optimization algorithm based on Simulink C-S functions is developed to adjust the parameters in the model. By alternative control of the two objects, experimental results demonstrate the realization of the control strategy with one adjustment means.     

Application of terminal box optimization of single‐duct air‐handling units

Terminal boxes maintain room temperature by modulating supply air temperature and airflow in building heating, ventilation and air‐conditioning (HVAC) systems. Terminal boxes with conventional control sequences often supply inadequate airflow to a conditioned space, resulting in occupant discomfort, or provide excessive airflow that wastes significant reheat energy. In this study, the procedure for the optimal minimum airflow setpoint was developed to improve thermal comfort and reduce energy consumption. The determined minimum airflow setpoint was applied in an office building air‐conditioning system. Improvements in indoor thermal comfort and energy reduction were verified through measurement. The results show that the minimum airflow reset can stably maintain room temperature, satisfy comfort standards and reduce energy consumption compared with the conventional control. Copyright © 2009 John Wiley & Sons, Ltd.     

Application of neural networks to predict the steady state performance of a Run-Around Membrane Energy Exchanger

Modeling the performance characteristics of thermal systems has been a research interest for many decades with moisture transfer systems experiencing a resurgence over the last decade, especially in heating, ventilating, and air conditioning (HVAC) applications. In this study, a neural network (NN) model is developed to predict the heat and moisture transfer performances (i.e., the sensible and latent effectivenesses) of a novel HVAC energy exchanger called the Run-Around Membrane Energy Exchanger (RAMEE) which is able to transfer both heat and moisture between exhaust and supply air streams. The training data set for the NN model covers a wide range of design and operating parameters and is produced using an experimentally validated finite difference (FD) model. Two separate NNs (one for sensible and one for latent energy transfer) each with five inputs and one output, are selected to represent the RAMEE. The results from NN models are numerically and experimentally validated. The root mean squared error (RMSE) between the FD and NN models are 0.05 °C and 2 × 10^?5 kg_v/kg_a, indicating satisfactory agreement for energy exchange calculations. The paper reports the weights and biases to make the results of this study reproducible. These NN models are very fast and easy to use therefore, they might be used for design and for estimating the annual energy savings in different buildings which use the RAMEE in their HVAC system. Additionally, the NN models can be used with optimization algorithms to maximize energy savings and minimize life-cycle costs for a given system.     

Optimal decision making in ventilation control

In this paper, a two-mode ventilation control of a single facility is formulated as a scheduling model over multiple time horizons. Using the CO₂ concentration as the major indoor air quality index and expected room occupancy schedule, optimal solutions leading to reduced CO₂ concentration and energy costs are obtained by solving the multi-objective optimization model formulated in the paper. A modified evolutionary strategy algorithm is used to solve the model at different time horizons. The optimized ventilation schedules result in energy savings and maintain an acceptable level of indoor CO₂ concentration.     

Energy saving by integrated control of natural ventilation and HVAC systems using model guide for comparison

Integrated control by controlling both natural ventilation and HVAC systems based on human thermal comfort requirement can result in significant energy savings. The concept of this paper differs from conventional methods of energy saving in HVAC systems by integrating the control of both these HVAC systems and the available natural ventilation that is based on the temperature difference between the indoor and the outdoor air. This difference affects the rate of change of indoor air enthalpy or indoor air potential energy storage. However, this is not efficient enough as there are other factors affecting the rate of change of indoor air enthalpy that should be considered to achieve maximum energy saving. One way of improvement can be through the use of model guide for comparison (MGFC) that uses physical-empirical hybrid modelling to predict the rate of change of indoor air potential energy storage considering building fabric and its fixture. Three methods (normal, conventional and proposed) are tested on an identical residential building model using predicted mean vote (PMV) sensor as a criterion test for thermal comfort standard. The results indicate that the proposed method achieved significant energy savings compared with the other methods while still achieving thermal comfort.     

Uncertainties in energy and economic performance of HVAC systems and energy recovery ventilators due to uncertainties in building and HVAC parameters

Energy recovery ventilators (ERVs) are exhaust air energy recovery devices for outdoor ventilation air preconditioning in building HVAC systems. The energy and economic performance of an ERV depends on its effectiveness, cost, maintenance as well as other parameters such as climate, building design and HVAC system parameters. In this study, a sensitivity analysis is used to evaluate the impact of uncertainty of building and HVAC system parameters on the energy savings potential and economics of ERVs. Firstly, the impact of building parameters on HVAC system peak loads, capital cost, annual energy use and operating cost are investigated for an office building located in Chicago using TRNSYS simulations. The results show that the ventilation rate has the most significant impact on total HVAC system energy performance. Secondly, energy and economic analysis on the ERV’s payback period is conducted with a specified variation of each input parameter. The results illustrate that an ERV with 75% sensible and 60% latent effectiveness can reduce the peak heating load by 30%, the peak cooling load by 18%, the annual heating energy usage by 40% and the annual cooling energy usage by 8%, with a payback period of 2 years. The uncertainty of ERV’s payback period to its initial cost, recovery effectiveness, energy rate, HVAC equipment initial cost and efficiency as well as ventilation rate are also presented. A ±25% uncertainty in the 7 building and HVAC system input parameters studied results in a maximum 17% and 225% uncertainty in the payback period of the ERV respectively.     

Development and optimization of artificial neural network algorithms for the prediction of building specific local temperature for HVAC control

This research accounts for the outcome of a major cloud-based smart dual fuel switching system (SDFSS) project, which is a dual-fuel integrated hybrid heating, ventilation, and air conditioning (HVAC) system in residential homes. The SDFSS was developed to enable optimized, flexible, and cost-effective switching between the natural gas furnace and electric air source heat pump (ASHP). In order to meet the optimal energy consumption requirements in the house and provide thermal comfort for the residents, various high-quality sensors and meters were installed to record multiple data points inside and outside the house. The performance of the system was monitored in the long term, which is a common practice in energy monitoring projects. Outdoor temperature data plays the most crucial role in operating HVAC systems and also is a key variable in the decision-making algorithm of the SDFSS controller. Therefore, this study introduces an innovative and unique approach to obtain the outdoor temperature that could potentially replace high precision sensors with a data-driven model utilizing weather station data at a time resolution of 2 minutes and 1 hour. In this work, a series of artificial neural network algorithms were developed, optimized, and implemented to predict the outdoor temperature with an average of 0.99 coefficient of correlation (R), 1.011 mean absolute error (MAE), and 1.315 root mean square error (RMSE). It has been demonstrated that the developed ANN is a reliable and powerful tool in predicting outdoor temperature. Thus, the proposed model is strongly suggested to be implemented as an alternative to temperature sensors in hybrid energy systems or similar systems requiring accurate ambient temperature measurements.     

Mixed-mode ventilation and air conditioning as alternative for energy savings: a case study in Beirut current and future climate

The aim of this work is to assess the use of mixed-mode ventilation for a typical office building in Lebanon and consequently reduce Heating Ventilation and Air Conditioning (HVAC) energy consumption in the observed current and under the future projected climatic conditions. Mixed-mode cooling is considered a compromise between the insufficient natural ventilation and the expensive year round-operated HVAC. A control algorithm is set for windows and HVAC system to ensure mixed-mode operation. Dynamic simulations are performed on a typical office building in Beirut City under the mixed-mode operation in the present and the future using commercial IES-VE software. The results of the software were validated against measured HVAC and total energy consumption of the typical office base case with conventional mechanical system. The results of the simulations are evaluated in terms of potential reduction in energy consumption under the present and the future weather data. Finally, a lifecycle cost analysis is performed for the proposed system, and its payback period is computed. Under present construction practices and weather data, 31% annual energy savings were achieved using mixed-mode system. Under future 2050s projected weather data, annual energy savings of 21% was attained with a payback period of 3.8 years.     

Grey Predictor reference model for assisting particle swarm optimization for wind turbine control

This paper proposes an approach of forming the average performance by Grey Modeling, and use an average performance as reference model for performing evolutionary computation with error type control performance index. The idea of the approach is to construct the reference model based on the performance of unknown systems when users apply evolutionary computation to fine-tune the control systems with error type performance index. We apply this approach to particle swarm optimization for searching the optimal gains of baseline PI controller of wind turbines operating at the certain set point in Region 3. In the numerical simulation part, the corresponding results demonstrate the effectiveness of Grey Modeling.