Assessing a Multi-Objective Genetic Algorithm with a Simulated Environment for Energy-Saving of Air Conditioning Systems with User Preferences (original) (raw)
Related papers
Multi-objective optimization of HVAC system with an evolutionary computation algorithm
A data-mining approach for the optimization of a HVAC (heating, ventilation, and air conditioning) system is presented. A predictive model of the HVAC system is derived by data-mining algorithms, using a dataset collected from an experiment conducted at a research facility. To minimize the energy while maintaining the corresponding IAQ (indoor air quality) within a user-defined range, a multi-objective optimization model is developed. The solutions of this model are set points of the control system derived with an evolutionary computation algorithm. The controllable input variables d supply air temperature and supply air duct static pressure set points d are generated to reduce the energy use. The results produced by the evolutionary computation algorithm show that the control strategy saves energy by optimizing operations of an HVAC system.
Evolutionary algorithms for multi-objective optimization in HVAC system control strategy
IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04., 2004
The supervisory control strategy set points for an existing HVAC system could be optimized using a two-objective evolutionary algorithm. The set points for the supply air temperature, the supply duct static pressure, the chilled water temperature, and the zone temperatures are the problem variables, while energy use and thermal comfort are the objective functions. Different evolutionary algorithm methods for two-objective optimization in HVAC systems are evaluated. It was concluded that controlled elitist non-dominated sorting genetic algorithms offer great potential for finding the Paretooptimal solutions of investigated problems. The results also showed that the on-line implementation of optimization process could save energy by 19.5%. The two-objective optimization could also help control daily energy use while bringing about further energy use savings as compared to a one-objective optimization.
A preference driven multi-criteria optimization tool for HVAC design and operation
Energy and Buildings, 2012
This paper discusses the issue of selecting the design solution that best accords with an articulated preference of multiple criteria with an acceptable performance band. The application of a newly developed multi-criteria decision-making tool called RR-PARETO2 is presented. An example of HVAC design is used to illustrate how solutions could be selected within a multi-criteria optimization framework. In this example, five criteria have been selected, namely, power consumption, thermal comfort, risk of airborne infection of influenza and tuberculosis and effective differential temperature ( t eq ) of body parts. The goal is to select the optimal air exchange rate that makes reasonable trade-offs among all the objectives. Two scenarios have been studied. In the first scenario, there is an influenza outbreak and the important objective is to prevent the spread of infection. In the second scenario, energy prices are high and the primary objective is to reduce energy. In both scenarios, RR-PARETO2 algorithm selects solutions that make reasonable trade-offs among conflicting objectives. The example illustrates how objectives such as reduction of airborne disease transmission and maximizing thermal comfort can be incorporated in the design of a practical, full-scale HVAC system.
Multi-criteria HVAC control optimization
2018 IEEE International Energy Conference (ENERGYCON), 2018
Heating, Ventilation and Air Conditioning (HVAC) systems consist one of the main elements that have a significant impact to both occupants' comfort and energy consumption in tertiary buildings, affecting therefore productivity and operational cost respectively. Energy consumption depends on occupant comfort through the calculation of the thermal comfort function, a function which derives from an extended analysis that takes into account various parameters such as temperature, humidity, air velocity, activity and clothing. This paper examines the implementation of a multi-criteria algorithm for optimizing operational control of HVAC systems, by integrating multiple real-time condition variables as input and targeting maximization of occupants' comfort satisfaction and minimization of energy consumption as output. As added value, the proposed framework allows its users to configure themselves the balance between comfort and consumption by adjusting specific thresholds. The effectiveness of the optimization algorithm is presented through the real-time operation of an HVAC system in an office building. Experimental results are presented and conclusions regarding the value of the HVAC optimal operational control are derived.
Multicriteria Genetic Tuning for the Optimization and Control of HVAC Systems
Applied Decision Support with Soft Computing, 2003
This work presents the use of genetic algorithms for the optimization and control of Heating, Ventilating and Air Conditioning (HVAC) systems developing smartly tuned fuzzy logic controllers for energy e ciency and overall performance of these systems. An optimum operation of the HVAC systems is a necessary condition for minimizing energy consumptions and optimizing indoor comfort in buildings. This problem has some speci c restrictions that make i t v ery particular and complex because of the large time requirements existing due to the need of considering multiple criteria (which enlarges the solution search space) and to the long computation time models require to assess the accuracy of each individual. To solve these problems, three e cient genetic tuning strategies, considering di erent m ulticriteria approaches, have been presented and tested in two r e a l t e s t sites (buildings) obtaining satisfactory results.
Energies
Refrigeration systems based on cooling towers and chillers are widely used equipment in industrial buildings, such as shopping centers, gas and oil refineries and power plants, among many others. Cooling towers are used to recover the heat rejected by the refrigeration system. In this work, the refrigeration is composed of cooling towers dotted with ventilators and compression chillers. The growing environmental concerns and the current scenario of scarce water and energy resources have lead to the adoption of actions to obtain the maximum energy efficiency in such refrigeration equipment. This backs up the application of computational intelligence to optimize the operating conditions of the involved equipment and cooling processes. In this context, we utilize multi-objective optimization algorithms to determine the optimal operational setpoints of the cooling system regarding the cooling towers, its fans and the included chillers. We use evolutionary multi-objective optimization to...
IEEE PES Innovative Smart Grid Technologies, Europe, 2014
the integration of renewable energy sources (RES) in the electrical system can be achieved on all voltage levels using both large power plants and individual/local small distributed renewable generation units. The present paper focuses on a low voltage consumer: a house with a dedicated photovoltaic system which supplies energy to an air conditioning unit. However the problem is the variability of this energy during the day. The solution proposed, in this paper, is the development of a home energy management system (HEMS), in which the air conditioning unit is controlled according to the solar irradiation during the day and the desirable inside temperature of the house between the hours of 9:00 to 18:00. The algorithm considers the thermodynamic model of the house and the photovoltaic panels' model to maximize the use of solar energy during the day. This optimization is implemented in CPLEX and the complete algorithm and a graphical user's interface (GUI) is developed in JAVA. At the end, the test simulations shows that the inside temperature during the day is closed to the desirable temperature in which the air conditioning unit is on according the algorithm developed especially on those moments with high irradiation. In the study, a comparison between the normal control of an air conditioning unit and the HEMS is developed.
Energies, 2020
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)...
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
The current investigation is focused on the analysis of indoor air quality characteristics of an air-conditioned car using multi-objective genetic algorithm (MOGA) approach. The conditioned space was selected, and the experiments were planned as per design of experiments to study the effect of human load, fresh air supply and air velocity on the human comfort conditions. The nonlinear regression models were developed to predict the comfort conditions, namely temperature, CO2 level and relative humidity over a specified range of input conditions. The optimum values of indoor air quality responses like Carbon dioxide level below 1,000 ppm, temperature less than 26°C and relative humidity below that of 60% were predicted for input conditions using MOGA in this present investigation.