1、19.1CHAPTER 19ENERGY ESTIMATING AND MODELING METHODSGENERAL CONSIDERATIONS 19.1Models and Approaches. 19.1Characteristics of Models 19.2Choosing an Analysis Method . 19.3COMPONENT MODELING AND LOADS . 19.4Calculating Space Sensible Loads. 19.4Secondary System Components 19.8Primary System Components
2、 . 19.11SYSTEM MODELING 19.15Overall Modeling Strategies 19.15Degree-Day and Bin Methods 19.16Correlation Methods 19.20Simulating Secondary and Primary Systems 19.20Modeling of System Controls . 19.21Integration of System Models . 19.21DATA-DRIVEN MODELING . 19.22Categories of Data-Driven Methods 19
3、.22Types of Data-Driven Models 19.23Examples Using Data-Driven Methods . 19.27Model Selection 19.29MODEL VALIDATION AND TESTING. 19.29Methodological Basis. 19.30NERGY requirements of HVAC systems directly affect a build-E ings operating cost and indirectly affect the environment. Thischapter discuss
4、es methods for estimating energy use for two pur-poses: modeling for building and HVAC system design and associ-ated design optimization (forward modeling), and modelingenergy use of existing buildings for establishing baselines, calculat-ing retrofit savings, and implementing model predictive contr
5、ol(data-driven modeling) (Armstrong et al. 2006a; Gayeski et al.2012; Krarti 2010).GENERAL CONSIDERATIONSMODELS AND APPROACHESA mathematical model is a description of the behavior of a sys-tem. It is made up of three components (Beck and Arnold 1977):1. Input variables (statisticians call these regr
6、essor variables,whereas physicists call them forcing variables), which act on thesystem. There are two types: controllable by the experimenter(e.g., internal gains, thermostat settings), and uncontrollable (e.g.,climate).2. System structure and parameters/properties, which providethe necessary physi
7、cal description of the system (e.g., thermalmass or mechanical properties of the elements).3. Output (response, or dependent) variables, which describe thereaction of the system to the input variables. Energy use is oftena response variable.The science of mathematical modeling as applied to physical
8、 sys-tems involves determining the third component of a system when theother two components are given or specified. There are two broadbut distinct approaches to modeling; which to use is dictated by theobjective or purpose of the investigation (Rabl 1988).Forward (Classical) Approach. The objective
9、 is to predict theoutput variables of a specified model with known structure andknown parameters when subject to specified input variables. To en-sure accuracy, models have tended to become increasingly detailed,especially with the advent of inexpensive, powerful computing. Thisapproach presumes kno
10、wledge not only of the various natural phe-nomena affecting system behavior but also of the magnitude of var-ious interactions (e.g., effective thermal mass, heat and mass transfercoefficients). The main advantage of this approach is that the systemneed not be physically built to predict its behavio
11、r. Thus, the for-ward-modeling approach is ideal in the preliminary design and anal-ysis stage and is most often used then.Forward modeling of building energy use begins with a physicaldescription of the building system or component of interest. Forexample, building geometry, geographical location,
12、physical charac-teristics (e.g., wall material and thickness), type of equipment andoperating schedules, type of HVAC system, building operatingschedules, plant equipment, etc., are specified. The peak and averageenergy use of such a building can then be predicted or simulated bythe forward-simulati
13、on model. The primary benefits of this methodare that it is based on sound engineering principles usually taught incolleges and universities, and consequently has gained widespreadacceptance by the design and professional community. Major simu-lation codes, such as TRNSYS, DOE-2, EnergyPlus, and ESP
14、-r, arebased on forward-simulation models.Figure 1 illustrates the analysis steps typically included in abuilding energy simulation program. Previously, the steps were per-formed independently: each step was completed for the entire yearThe preparation of this chapter is assigned to TC 4.7, Energy C
15、alculations.Fig. 1 Flow Chart for Building Energy Simulation Program(Ayres and Stamper 1995)19.2 2013 ASHRAE HandbookFundamentalsand hourly results were passed to the next step. With the increasedcomputing resources now available, current codes usually performall steps at each time interval, allowin
16、g effects such as insufficientplant capacity to be reflected in room conditions.Data-Driven (Inverse) Approach. In this case, input and outputvariables are known and measured, and the objective is to determinea mathematical description of the system and to estimate systemparameters. In contrast to t
17、he forward approach, the data-drivenapproach is relevant only when the system has already been builtand actual performance data are available for model developmentand/or identification. Two types of performance data can be used:nonintrusive and intrusive. Intrusive data are gathered under con-dition
18、s of predetermined or planned experiments on the system toelicit system response under a wider range of system performancethan would occur under normal system operation to allow moreaccurate model identification. When constraints on system opera-tion do not allow such tests to be performed, the mode
19、l must beidentified from nonintrusive data obtained under normal opera-tion.Data-driven modeling often allows identification of system mod-els that not only are simpler to use but also are more accurate pre-dictors of future system performance than forward models. Thedata-driven approach arises in m
20、any fields, such as physics, biology,engineering, and economics. Although several monographs, text-books, and even specialized technical journals are available in thisarea, the approach has not yet been widely adopted in energy-relatedcurricula and by the building professional community.CHARACTERIST
21、ICS OF MODELSForward ModelsAlthough procedures for estimating energy requirements varyconsiderably in their degree of complexity, they all have three com-mon elements: calculation of (1) space load, (2) secondary equip-ment load, and (3) primary equipment energy requirements. Here,secondary refers t
22、o equipment that distributes the heating, cooling,or ventilating medium to conditioned spaces, whereas primaryrefers to central plant equipment that converts fuel or electric energyto heating or cooling effect.The first step in calculating energy requirements is to determinethe space load, which is
23、the amount of energy that must be added toor extracted from a space to maintain thermal comfort. The simplestprocedures assume that the energy required to maintain comfort isonly a function of the outdoor dry-bulb temperature. More detailedmethods consider humidity, solar effects, internal gains, he
24、at andmoisture storage in walls and interiors, and effects of wind on bothbuilding envelope heat transfer and infiltration. Chapters 17 and 18discuss load calculation in detail.Although energy calculations are similar to the heating and cool-ing design load calculations used to size equipment, they
25、are not thesame. Energy calculations are based on average use and typicalweather conditions rather than on maximum use and worst-caseweather. Currently, the most sophisticated procedures are based onhourly profiles for climatic conditions and operational characteris-tics for a number of typical days
26、 of the year or on 8760 h of operationper year.The second step translates the space load to a load on the sec-ondary equipment. This can be a simple estimate of duct or pipinglosses or gains or a complex hour-by-hour simulation of an airsystem, such as variable-air-volume with outdoor-air cooling. T
27、hisstep must include calculation of all forms of energy required by thesecondary system (e.g., electrical energy to operate fans and/orpumps, as well as energy in the form of heated or chilled water).The third step calculates the fuel and energy required by the pri-mary equipment to meet these loads
28、 and the peak demand on theutility system. It considers equipment efficiencies and part-loadcharacteristics. It is often necessary to keep track of the differentforms of energy, such as electrical, natural gas, or oil. In some cases,where calculations are required to ensure compliance with codes ors
29、tandards, these energies must be converted to source energy orresource consumed, as opposed to energy delivered to the buildingboundary.Often, energy calculations lead to an economic analysis to estab-lish the cost-effectiveness of efficiency measures (ASHRAE Stan-dard 90.1). Thus, thorough energy a
30、nalysis provides intermediatedata, such as time of energy usage and maximum demand, so utilitycharges can be accurately estimated. Although not part of the energycalculations, capital equipment costs should also be estimated toassess the life-cycle costs of alternative efficiency measures.Complex an
31、d often unexpected interactions can occur betweensystems or between various modes of heat transfer. For example,radiant heating panels affect space loads by raising the mean radianttemperature in the space (Howell and Suryanarayana 1990). As aresult, air temperature can be lowered while maintaining
32、comfort.Compared to a conventional heated-air system, radiant panels maycreate a greater temperature difference from the indoor surface tothe outdoor air. Thus, conduction losses through the walls and roofincrease because the indoor surface temperatures are greater. At thesame time, the heating load
33、 caused by infiltration or ventilationdecreases because of the reduced indoor-to-outdoor-air temperaturedifference. The infiltration rate may also decrease because thereduced air temperature difference reduces the stack effect.Data-Driven ModelsThe data-driven model has to meet requirements very dif
34、ferentfrom the forward model. The data-driven model can only contain arelatively small number of parameters because of the limited andoften repetitive information contained in the performance data. (Forexample, building operation from one day to the next is fairly repet-itive.) It is thus a much sim
35、pler model that contains fewer terms rep-resentative of aggregated or macroscopic parameters (e.g., overallbuilding heat loss coefficient and time constants). Because modelparameters are deduced from actual building performance, it is muchmore likely to accurately capture as-built system performance
36、, thusallowing more accurate prediction of future system behavior undercertain specific circumstances. Performance data collection andmodel formulation need to be appropriately tailored for the specificcircumstance, which often requires a higher level of user skill andexpertise. In general, data-dri
37、ven models are less flexible than for-ward models in evaluating energy implications of different designand operational alternatives, and so are not substitutes in this regard.To better understand the uses of data-driven models, considersome of the questions that a building professional may ask about
38、 anexisting building with known energy consumption (Rabl 1988):How does energy consumption compare with design predictions(and are any discrepancies caused by anomalous weather, unin-tended building operation, improper operation, as-built deficiency,etc.)?How would consumption change if thermostat s
39、ettings, ventilationrates, or indoor lighting levels were changed?How much energy could be saved by retrofits to the building shell,changes to air handler operation from constant volume (CV) tovariable air volume (VAV), or changes in the various control set-tings?If retrofits are implemented, can on
40、e verify that the savings aredue to the retrofit and not to other causes (e.g., weather)?How can one detect faults in HVAC equipment and optimize con-trol and operation?All these questions are better addressed by the data-drivenapproach. The forward approach could also be used, for example, bygoing
41、back to the blueprints of the building and of the HVAC system,Energy Estimating and Modeling Methods 19.3and repeating the analysis performed at the design stage using actualbuilding schedules and operating modes, but this is tedious andlabor-intensive, and materials and equipment often perform diff
42、er-ently in reality than as specified. Tuning the forward-simulationmodel is often problematic, although it is an option (see the sectionon Calibrated Simulation Approach).CHOOSING AN ANALYSIS METHODThe most important step in selecting an energy analysis methodis matching method capabilities with pr
43、oject requirements. Themethod must be capable of evaluating all design options with suffi-cient accuracy to make correct choices. The following factors applygenerally (Sonderegger 1985):Accuracy. The method should be sufficiently accurate to allowcorrect choices. Because of the many parameters invol
44、ved inenergy estimation, absolutely accurate energy prediction is notpossible (Waltz 1992). ANSI/ASHRAE Standard 140 was devel-oped to identify and diagnose differences in predictions that maybe caused by algorithmic differences, modeling limitations, cod-ing errors, or input errors. More informatio
45、n on model validationand testing can be found in the Model Validation and Testing sec-tion of this chapter and in ANSI/ASHRAE Standard 140.Sensitivity. The method should be sensitive to the design optionsbeing considered. The difference in energy use between twochoices should be adequately reflected
46、.Versatility. The method should allow analysis of all options underconsideration. When different methods must be used to considerdifferent options, an accurate estimate of the differential energyuse cannot be made.Speed and cost. The total time (gathering data, preparing input,calculations, and anal
47、ysis of output) to make an analysis shouldbe appropriate to the potential benefits gained. With greaterspeed, more options can be considered in a given time. The costof analysis is largely determined by the total time of analysis.Reproducibility. The method should not allow so many vaguelydefined ch
48、oices that different analysts would get completely dif-ferent results (Corson 1992).Ease of use. This affects both the economics of analysis (speed)and the reproducibility of results.Selecting Energy Analysis Computer ProgramsSelecting a building energy analysis program depends on its ap-plication,
49、number of times it will be used, experience of the user, andhardware available to run it. The first criterion is the ability of theprogram to deal with the application. For example, if the effect of ashading device is to be analyzed on a building that is also shaded byother buildings part of the time, the ability to analyze detached shad-ing is an absolute requirement, regardless of any other factors.Because almost all manual methods are now implemented on acomputer, selection of an energy analysis method is the selection ofa computer program. The U.S. Department of Energy Building En-e
