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Energy consumption in buildings is one of the top priorities in http://talk-tv.info/online-casino-no-download.php energy policies of many countries.

The main reason behind this lies in the significant increase in energy consumption in the building online casino slots malaysia. Energy consumption in buildings can be reduced by a number of energy efficiency measures, feniks bb just click for source most frequent being: What is common for all these measures is that they are implemented during feniks bb refurbishment of existing buildings or the construction of new buildings, and that in the majority of cases it is legally regulated [5].

The basis for this kind of research are the mathematicsl models of buildings and related systems. Modeling and prediction of performance focus on three categories [9]: Models of buildings and their accompanying systems for short-term forecasts feniks bb as follows: http://talk-tv.info/online-blackjack-guide.php models include physical characteristics and relations of buildings and their systems, and are incorporated in the best-known building dynamic simulation programs such as: The optimization process can be repeated time after time, resulting feniks bb the moving horizon optimization implemented in numerous studies on the topic of model predictive control [12—16].

This paper presents the possibility to feniks bb building energy consumption by optimizing the existing HVAC system operation modeled in EnergyPlus, while maintaining the occupant thermal comfort at the feniks bb time. The planning horizon is set to one day assuming a perfect weather forecast, and the optimization process is repeated day-by-day in the observed period. The optimization process is based on the combination of detailed hourly simulations of the building performed in EnergyPlus and the operation optimization of selected HVAC systems developed in the C programming language.

The optimization process follows a relatively simple iterative procedure described in [9]. The optimization problem is solved by using the parallel particle swarm optimization Feniks bb [17]. The program starts by loading the building model and weather file.

The building energy model created in EnergyPlus contains all the information on the analyzed building, and it is, basically, a text file with the values in particular lines which the optimization algorithm will replace with the values of selected decision variables. At the beginning of each iteration, the program randomly generates a population of decision vectors and creates as many text files as there are vectors.

The program feniks bb the simulation of all files related to a current PSO iteration, and all simulations are carried out simultaneously. After all the simulations have finished, the program reads the resulting output files and extracts the values required to calculate the objective function value s.

The objective function can be easily defined according to a particular interest. The process is then repeated in a new iteration, with a new population of decision variable vectors randomly generated around an optimal vector of the feniks bb completed iteration. This process repeats until the exit criteria is fulfilled. When the exit criteria is satisfied, http://talk-tv.info/top-online-casinos-in-uk.php optimization process is repeated for the next optimization period part of day, one day, several feniks bb, etc.

The building is a combination of the office and manufacturing type. Office part of the building. One part of the building, approximately a half, represents a manufacturing hall, while the other part is http://talk-tv.info/slots-play-free-online-slots-and-slot-machines.php into two stories.

The lower storey houses manufacturing feniks bb and warehouses, while the upper storey is where offices and manufacturing of electronic components are located. The AHU consists feniks bb the following sections: The air feniks bb system is designed in the classical manner to ensure the indoor temperature for a summer design day.

The operation of all secondary systems is controlled by PLCs. Gas-fired condensing boilers and air-to-water heat pump are used as the primary energy sources. Feniks bb rooms in the building were treated as separate thermal zones. To simulate the building, an appropriate weather file containing all boundary conditions was also needed. The offices were assumed to be occupied feniks bb weekdays from The aim is to maintain thermal comfort within the prescribed range by optimizing the HVAC system operation day by day.

As the indicator for thermal comfort the predicted mean vote PMV was used, and this value could be generated feniks bb the output from the simulations on an hourly basis for every modeled zone. PMV can have the value of 0. Typical number of occupants in offices during weekdays. The period starting on January 27th and ending on February 6th was selected to meet the needs of this paper and check the methodology. The optimization period of one day was feniks bb, and the optimization process itself was performed for each day of the stated period including weekends, which were treated as a single optimization period.

To perform the optimization task and calculate the objective function, decision variables should be defined first. Since the optimization goal is to achieve the minimum primary energy consumption while maintaining the thermal comfort for one day, having the simulation tool limitations in mind, variables were classified into two groups: Certain variables were further subdivided into three periods of day for each day in the observed period: To reduce the total number of deposit online casino list variables, only one decision variable for each of the unoccupied periods was allowed.

Furthermore, some decision variables were constrained by the fact that the system was already installed and there were feniks bb especially in terms of capacity and maximum flow rates. For feniks bb observed winter operation, the following variables were adopted:. Since two different HVAC systems radiator heating and air-conditioning system were served by the same heat source, using a built-in energy management system of EnergyPlus, a syntax was created according to which the heat source boiler was available whenever either of online gambling two systems was required.

In casino usa online objective function of the optimization problem is given in the form:.

In equation 3, i represents the zone identifier; feniks bb is the minimal value of PMV in the i-th zone; Ni is the number of occupants in the i-th zone; Ntot is the total number of occupants. For the PSO algorithm, the population size was set towhile the number feniks bb generations was set to The feniks bb criteria were not defined, meaning that all feniks bb, simulations were performed. The optimization process was run for every weekday of the observed period and also for the weekend but with less strict criteria for TCF.

The optimization lasted between 22 and 24 hours, meaning that there was enough time left to implement the optimal decision variables vector into the existing automatic regulation system, assuming that the weather forecast for that particular day was perfect. To compare the results obtained in the optimization, a baseline case was adopted. This case represented the usual operation of the existing HVAC systems.

The main differences between the baseline and the optimal model were:. In the baseline case, the primary energy consumption had the value of The PMV values read article the offices are feniks bb in Figure 2. For the optimized case the optimal values from each day joined feniks bb a single feniks bbthe primary energy consumption had the value of The increased energy consumption was due to the weekend operation of the systems and resulted in Thus increased energy consumption resulted in a much better occupant feniks bb comfort in all zones as shown in Figure 3.

PMV variation in offices - baseline case. PMV variation in offices - optimized operation. Feniks bb is interesting to note that for the optimal case no correlation could be made between the heating supply temperature and the outdoor temperature Figure 4which can potentially represent the material for future research with the aim of finding the heating curves with which system operators are familiar.

Heating supply temperature variation. The main goal of the paper was to show that with the existing HVAC system designed in the traditional manner, users or system operators can define in feniks bb the thermal comfort level which the system will try to feniks bb read more minimal energy consumption. The main advantage of this methodology is that it can be applied with relatively small modifications of the existing HVAC system.

Future research will be feniks bb to the moving horizon approach and the implementation of the obtained optimal values into a real system, as well as their experimental verification.

In addition, decision variables feniks bb objective function need to be feniks bb in order to generalize the application of the presented process.

Pout, A review feniks bb buildings energy consumption information, Energy and Buildings,Vol. Feniks bb, Model-based controllers for indoor climate control in office buildings— Complexity and performance evaluation, Energy and Buildings, feniks bb, Vol.

Kay, Optimisation of energy management in commercial buildings with weather forecasting inputs: Ibrahim, A review on optimized control systems for building energy and comfort management of smart sustainable buildings, Renewable and Sustainable Energy Feniks bb,Vol.

Wen, Review of building energy modeling for control and operation, Renewable and Sustainable Feniks bb Reviews,Vol. Xu, Demand reduction in building energy systems based on economic model predictive control, Chemical Engineering Science,Vol. Norford, Modeling environment for model predictive control of buildings, Energy and Buildings,Vol. About Us Who we are? Energy efficiency measures in existing buildings include improvements in heating, ventilation and air conditioning systems through systems renovation and components upgrade.

These measures target building energy consumption through improving the overall system efficiency, with the thermal comfort of occupants being observed through only feniks bb or two parameters.

Improvements in the existing system operation can lead to better energy efficiency as well, but with a possibility of maintaining the occupant thermal comfort in the desired range. This paper implements the feniks bb particle swarm optimization to determine the operation parameters of an existing HVAC system that corresponds to feniks bb minimal primary energy use while maintaining the desired occupant thermal comfort.

The moving horizon approach in near-real time feniks bb adopted. The focus in this paper is shifted from the minimal energy consumption to the minimal energy consumption for a desired thermal comfort level, without any renovation or upgrade of the system. Introduction Energy consumption in buildings is one of the top priorities in official energy policies of many countries.

Optimization process The optimization process is based on the combination of detailed hourly simulations of the building performed in EnergyPlus and the operation optimization of selected HVAC systems developed in the C programming language. Decision variables To perform the optimization task and calculate the objective function, feniks bb variables should be defined first. For feniks bb observed winter operation, the following variables were adopted: Objective function The objective function of the optimization problem is given in the form: The main differences between the baseline and the optimal model were: PMV variation in $1 casinos 2015 - baseline case Figure 3.


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