WO2018203862A1

WO2018203862A1 - Integrated building operation, design, optimization method

Info

Publication number: WO2018203862A1
Application number: PCT/TR2017/050632
Authority: WO
Inventors: Onur DURSUN; Emrah Biyik; Levent Bi̇li̇r; Nurdan Yildirim; İlker Kahraman; Arif HEPBAŞLI
Original assignee: Yaşar Üni̇versi̇tesi̇
Priority date: 2016-12-29
Filing date: 2017-12-06
Publication date: 2018-11-08

Abstract

The present invention relates to an integrated operation, design and optimization method which enables any type of new building project to be designed in an energy efficient manner with a cost-effective net zero energy goal.

Description

I NT EG RAT ED BUI LDI NG OPERATI ON, DESI GN AND OPTI Ml ZATI ON METHOD

Technical Field of I nvention

The present invention relates to an integrated building operation, design and optimization method.

The present invention particularly relates to an integrated optimization method which enables any type of new building project to be designed in an energy efficient manner with a cost effective, net zero energy goal. As per the decision of the European Union Energy Directive dated 1 9 May 2010, the integrated method proposed by the invention requires the data coming from various disciplines to be simultaneously used as design input, starting from the early design stages of new building projects. I n this sense, the present invention covers the technical fields of architecture, control engineering and mechanical engineering.

Prior Art

Nowadays, there are numerous studies conducted in various disciplines which are aimed at reducing the energy demand of buildings. I n such studies, it is possible to see different research and analysis methods. Nevertheless, the present invention relates to a proposal for an innovative optimization method. Therefore, the general features of the studies utilizing the optimization method of the present invention are summarized. Figures A and B demonstrate the applications of the known art. Additionally, the integrated optimization method proposed by the invention aims at replacing the existing technique used in the process of building design.

Built environment (building sciences) optimizations;

The studies in the first group are carried out by building scientists (architects - building environment control experts) and focus on the optimization of the design parameters affecting the building energy demand (for example: window gaps - outer wall ratio) . These studies optimize the selected design parameters so as to achieve design solutions that will minimize the building energy demand.

I n an embodiment, the design parameters affecting the building energy demand are defined as decision variables. These decision variables are connected to the energy simulation program . The output of the simulation is the building energy demand calculated for the given time period (usually, annual) . I n this embodiment, the output of the simulation is defined as the target function of the optimization. The optimization target can merely be the minimization of the building energy demand or multiple targets (such as maximizing the building sunlight utilization, minimization of the building costs, etc.) can be present. Optimization is performed under the defined limitations. A small change in the decision variables requires the whole simulation to be run again. This leads to high costs of calculation. During optimization, computer-based optimization is performed. I n the course of computational optimization, the correct method is selected among optimization algorithms, recursive methods, collective convergence and heuristic algorithms.

I n another embodiment, building equivalent model is implemented in order to reduce the high costs of calculation experienced in the previous embodiment. Building equivalent model is the name of the mathematical model developed to estimate the energy demand of the building. A data set is required to develop this estimation model. I n order to obtain the required data set, random values are assigned, in the required numbers, to the predefined ranges of the design parameters affecting the energy demand. Thus, a spreadsheet with a certain number of rows is obtained. I n other words, a random sample is created from the main mass of building design alternatives. An energy simulation is performed for each observation in this sample. The outputs of the performed simulations are recorded onto the same spreadsheet. Thus, the creation of the data set required for training the building equivalent models is complete.

I n this embodiment, data set statistical modeling, machine learning techniques or any appropriate method is selected to be converted in the building equivalent models. Thus, the development of the "building equivalent model", which estimates the energy consumption of the building as the design parameters affecting the energy demand change, is finalized. I n the last step, the design parameters affecting the energy demand are defined as the decision variables of optimization (8) . The energy demand, which is the output of the building equivalent model, is converted into the optimization target. As is the case with the previous embodiment, during optimization, computer-based optimization is performed. I n the course of computational optimization, the correct method is selected among optimization algorithms, recursive methods, collective convergence and heuristic algorithms.

I n both embodiments, the energy demands of the alternatives having different design parameters are estimated by utilizing a third party energy simulation engine (such as EnergyPlus) . These simulation engines carry out calculations based on constant acceptances through average values over the operation period of the building. For example, in a location assigned with an office function, the number of people per square meter and the presence of such people in said room with which percentages and with which time periods throughout the year are defined with constant scenarios. I n other words, the dynamic effects subjected to the building during the operation period thereof are not taken into consideration.

Control (engineering) optimizations;

The studies in the second group are mostly carried out by engineers (control and mechanical) and aim at operating the existing building installations (such as heating, cooling, ventilation and lighting) under optimal conditions. Physical building models and the parameters of said models are among the most important inputs of the optimization process of such studies. The studies in this group consider the dynamic effects which might be present in the operation period. However, they focus on improving the operation conditions of a constant design. One of the most important disadvantage of the existing method is that it considers the design parameters, and therefore the physical building model, as constants.

As a conclusion ;

The physical building model is a function of the design parameters. Changes in the design parameters will lead to changes in the physical building model.

The physical building model is the most important input of the building energy demand estimation. The variability in the design parameters will have a direct effect on the building energy demand. The estimated energy demand of the building is a function of the scenarios to which the building is subjected to during the operation period. Changes in the previously mentioned scenarios will lead to changes in the building energy demand estimation.

As summarized above, control optimization where the variability during the operation period is a topic that is being studied. Optimization based optimal control approach is much more advantageous compared to the basic control approaches and predefined set-point planning, which are widely used, in terms of modeling the dynamic relations among the parts of a given system and the optimization of the performance of the whole system.

Design optimization where the variability of the building parameters are addressed, is a topic that is also being frequently studied. Nevertheless, a method where the building parameters and the variables during the operation period are addressed is yet to be developed. The disadvantage of such a situation is that it provides design solutions that are far from the actual values obtained through simplified acceptances.

Objective of the I nvention

I n order to eliminate the disadvantages present in the known art, the present invention aims at developing a building energy management system (BEMS) which can simultaneously respond to the dynamic effects to be experienced during the life time of the building.

Another objective of the present invention is to propose an innovative optimization system which runs the developed BEMS in integration with the techniques utilized for determining the design parameters which form the basis of the building's energy consumption.

Yet another objective of the present invention is to ensure that the complicated scenarios, which the building will face during the operation period, are taken into consideration as of the conceptual design stage in line with the goal of an energy efficient building design.

Still another objective of the present invention is to perform energy demand estimations which are more realistic compared to the existing static simulation based optimization methods. Another objective of the present invention is to reduce the building energy demand below the benchmark, at a rate that is greater than the existing methods in the literature.

Yet another objective of the invention is to provide automation to the design solutions, which can be accessed (probably) by the project stakeholders after they convene during the design of the building and work on said solutions in detail for a long time. Overhead costs are reduced for the users of the invention and rapid access to energy efficient design solutions is provided.

Yet another objective of the present invention concerns the global economy and the environment. The energy demand of the buildings adds up 40% of the total global energy demand. The invention aims at dramatically reducing this rate for the new building projects to be built. On the other hand, the present invention also aims at contributing to the reduction of C02 emissions which are in correlation with energy demand.

The present invention also proposes a control methodology that optimizes building energy performance. This control methodology achieves energy savings and high system performance in the building through the real time control of the heating, cooling, ventilation and lighting systems in the building. For this purpose, dynamic models of the building and utilized equipment are created, the thermal and electrical condition of the system is dynamically estimated via these models within a certain estimation horizon and the control signals optimizing the defined performance criteria are calculated inside the BEMS.

Description of the Drawings

Figure 1 is the flow diagram of the method of the present invention. Description of the Reference Nu

Detailed Description of the I nvention

The present invention is an integrated building operation, design and optimization method. Thanks to the method of the present invention, the BEMS control optimization (B) utilized for the building operation period can be nested inside the optimization process of the design parameters affecting the building energy demand (A) which is used during the building design process, and the two can be used together. The control optimization step (B) demonstrates the real time control optimization process which is used under the scope of the building energy management systems. BEMS, which is summarized under the control optimization step (B) , aims at simultaneously optimizing the building within the framework of the criteria and performance benchmarks defined by the building administrator and/or users and operating the heating and lighting installations in the building at the best set-points, by utilizing the measurement values collected from the building and the building energy model.

The method of the present invention is an integrated building operation, design and optimization method comprising the optimization process of the design parameters affecting the building energy demand (A) which includes the creation of a precursor reference model (1 ) , the building energy model process step (2) , the simulation process (3) , the power demand step (4) and the energy efficient building design solution clustering (6) ; the real time control optimization (B) which includes the building energy model process step (2) , the obtaining information about the system status (8) and the power demand step (4) ;

the integrated building design-operation optimization method built in line with the energy efficient design objective (C) which includes the creation of a precursor reference model (1 ) , the assigning random values in required numbers to the predefined ranges of the design parameters (9) , the building energy model process step (2) , the simulation process (3) ; and it includes the process step of ensuring the integration of the said building design-operation optimization (C) with the control and design optimization through the system diagnostics process (10) .

The building energy model process step (2) demonstrates the mathematical model stating the dynamic relations among the outdoor air temperature, the building ambient temperature, the illumination values, the status of utilization and the heat provided to or taken out of the building.

As shown in the step of obtaining information about the system status (7) , information (ambient temperature, outdoor air temperature, flow rate, lux, electrical and thermal load, etc.) regarding the system status in each control step is collected from the measurement devices present in the building and the final condition of the system dynamics is obtained by utilizing state estimating algorithms for non-measurable variables. I n the step of obtaining information about the system status (7) ; thanks to the building energy model of the building energy model process (2) , the ambient temperature goals, the lighting requirements and the values of the limitations are calculated.

I n the optimization step (8) ; the limited optimization algorithm is solved by using the obtained information about the system and the set-points, which keep system parameters such as temperature, humidity, intensity of illumination, building heating and cooling load within the projected limits and which also optimize the defined system performance parameters, are calculated and sent to the equipment control devices. Moreover, at the end of this optimization process, the energy and power demand of the building under the best operation conditions are calculated at the power demand step (4) .

The integrated design-operation optimization (C) , which covers real time control optimization (B) , consists of the following sub-steps:

A precursor reference model defining the physical building model is created at the step of creating a precursor reference model (1 ) .

Random values are assigned in the required numbers to the predefined ranges of the design parameters affecting the energy demand in order to obtain the required data set at the step of assigning random values (9) . Thus, a spreadsheet with a certain number of rows is obtained. I n other words, a random sample is created from main mass of the building design alternatives.

At the building energy model process step (2) ; an energy model is created for each observation in the sample created in the step of assigning random values (9) .

An energy simulation of each energy model created in the step (2) is performed at the simulation process (3) . The sub step is named as diagnostics in the system diagnostics process step (10) and it play a crucial role in the integration of the BEMS model and the design optimization. Furthermore, the present invention is a method that has never been used before. The simulation outputs from the simulation process step (3) are defined as building R, C, Lighting, and Equipment and model parameters by properly converting them , instead of a simple energy demand value. Thus, the physical building model parameters corresponding to the random parameters in the spreadsheet, which is prepared at the step of assigning random values (9) , are determined, thereby completing the spreadsheet. Utilizing the sample the variable values of which are recorded in said spreadsheet at the step of developing estimation models (1 1 ) , the equivalent models which are to be used for the design space estimation are developed by using one of the suitable methods.

The process of the real time control and optimization process of the building operation (8) is the integration step with the BEMS. According to this process, building R, C, Lighting, Equipment and other model parameters, which are the model outputs developed in the step of developing estimation models, are defined as the decision variables of the control optimization (8) to be carried out. This optimization is performed with the objective of minimizing the power/energy demand (4) by utilizing the suitable method.

I n the process of the optimization of design parameters affecting the building energy demand (5) ; the energy demand value which is obtained as the outcome of the control and optimization process (8) is defined as the target function of the optimization of the design parameters affecting the building energy demand. The decision variables are input of the building equivalent model created in the step of developing estimation models (1 1 ) . When the optimization runs in the process of the optimization of design parameters (5) , respectively:

• The inputs of the model developed in the step of developing estimation models (1 1 ) change,

· Accordingly, the output of the building equivalent models are estimated based on the changing input values and recorded as output,

• These outputs become the decision variables (inputs) of the control optimization (8) ,

• The building energy demand value, which is the outcome of the control optimization, is calculated as the decision variable values change,

· The cycle continues until convergence to the design parameter cluster which minimizes said value is achieved.

Claims

CLAI MS

The invention is an integrated building operation, design and optimization method comprising; the optimization process of the design parameters affecting the building energy demand (A) which includes the creation of a precursor reference model ( 1 ) ,the building energy model process step (2) , the simulation process (3) , the power demand step (4) and the energy efficient building design solution clustering (6) ; the real time control optimization (B) which includes the building energy model process step (2) , the obtaining information about the system status (8) and the power demand step (4) ;

the integrated building design-operation optimization method built in line with the energy efficient design objective (C) which includes the creation of a precursor reference model ( 1 ) , the assigning random values in required numbers to the predefined ranges of the design parameters (9) , the building energy model process step (2) , the simulation process (3) ; characterized in that, it comprises the process step of ensuring the integration of the said building design-operation optimization (C) with the control and design optimization through the system diagnostics process ( 10) .

A method according to Claim 1 , characterized in that said process of ensuring control and design optimization integration comprises the steps of developing estimation models (1 1 ) , real time control and optimization of the building operation (8) , the power demand step (4) , the process of the optimization of the design parameters affecting the building energy demand (5) and energy efficient building design solution clustering (6) .

A method according to Claim 1 , characterized in that, in order to minimize the power/ energy demand (4) in the step of developing estimation models (1 1 ) , the R, C, Lighting, Equipment and other model parameters, which are the outputs of the developed models, are defined as the decision variables of the control optimization (8) to be performed. A method according to Claim 1 , characterized in that, in the said process of the optimization of design parameters affecting the building energy demand (5) ; the energy demand value which is obtained as the outcome of the control and optimization process (8) is defined as the target function of the optimization of the design parameters affecting the building energy demand.

A method according to Claims 1 and 4, characterized in that the said decision variables are the inputs of the building equivalent model created in the step of developing estimation models (1 1 ) .

A method according to claim 1 , characterized in that it comprises the following steps respectively when the optimization runs in the said process of the optimization of design parameters (5) : changing of the inputs of the model developed in the step of developing estimation models (1 1 ) , accordingly, estimating the output of the building equivalent models based on the changing input values and recording them as output, said outputs becoming the decision variables (inputs) of the control optimization (8) , calculating the building energy demand value, which is the outcome of the control optimization, as the decision variable values change,

the cycle continuing until the convergence to the design parameter cluster which minimizes said value is achieved.