Built-In Functions[LINK]

Several useful, built-in functions are available for use in Erl programs. You cannot configure these; they are internal to the language processor inside EnergyPlus. They provide access to a subset of general service routines that are useful inside the main EnergyPlus program or are intrinsic functions available in the underlying Fortran language. The “@” character is used to signal to the language processor that the following character string defines a built-in function that is used to assign a result to an Erl variable. The characters appended to the “@” operator must be one of the predefined names listed in Table 4, Table 5, Table 6, or Table 7. The syntax of the function call will vary depending on the arguments required by the function, but the general structure is:

SET <variable> = @<function name> <argument1> <argument2> … <argumentN>

Where “argument” can be either an Erl variable or a numeric constant.

For example, the following two statements can be used to set the value of an Erl variable called mySupplyRH to have percent relative humidity.

SET mySupplyRH = @RhFnTdbWPb mySupplyDryblub mySupplyHumRat mySupplyPress

SET mySupplyRH = mySupplyRH * 100

Built-in Math Functions[LINK]

Table 4 lists the built-in functions for common mathematical functions. The numerical model for these functions is provided by the underlying Fortran language and the compiler.

Table 4. Built-in Math Functions for Erl

Function Name	Description	Number of Arguments
@Round	Decreases precision of real number argument to nearest whole number, remains a real number.	1
@Mod	Returns remainder after dividing the first argument by the second.	2
@Sin	Sine, returns sine of angle given in radians.	1
@Cos	Cosine, returns cosine of angle given in radians.	1
@ArcSin	Arcsine, returns angle in radians from sine of angle.	1
@ArcCos	ArcCosine, returns angle in radians from cosine of angle.	1
@DegToRad	Degrees to radians, returns radians from degrees.	1
@RadToDeg	Radians to degrees, returns degrees from radians.	1
@Exp	Exponential, ex, returns result.	1
@Ln	Natural log, loge(x), returns result.	1
@Max	Maximum, returns largest value of two arguments.	2
@Min	Minimum, returns smallest value of two arguments.	2
@Abs	Absolute value, returns positive magnitude of argument.	1
@RandomUniform	Pseudo-Random Number Generator, returns random number with uniform probability distribution across the range of values passed as the arguments, inclusive. Argument 1 is the lower limit. Argument 2 is the upper limit.	2
@RandomNormal	Pseudo-Random Number Generator, returns random number with normal (Gaussian) probability distribution as a function of the mean, standard deviation, and limits. Argument 1 is the mean. Argument 2 is the standard deviation. Argument 3 is the lower limit. Argument 4 is the upper limit.	4
@SeedRandom	Random Seed, controls the seed used in the random number generator for calls to @RandomUniform and @RandomNormal. Use is optional and provided for repeatable series of random numbers. The argument is rounded to the nearest whole number and then used to set the size and values of the seed for the number generator.	1

Built-In Simulation Management Functions[LINK]

Table 5. Built-in EnergyPlus Simulation Management Functions for Erl

Function Name	Description	Number of Arguments
@FatalHaltEp	Throws fatal error with time of occurrence and stops execution of current model. Argument passes a number that can be used as an error code.	1
@SevereWarnEp	Throws severe error with time of occurrence and continues execution. Argument passes a number that can be used as an error code.	1
@WarnEp	Throws warning error and continues execution. Argument passes a number that can be used as an error code.	1

Built-in Trend Variable Functions[LINK]

For control algorithms, you often need to be able put a sensor reading into some historical context. The trend variables are provided in Erl as a way to log the time history of data to use in control decisions. To use the trend variables in Erl programs, their values must be extracted and placed into normal Erl variables. Setting up an Erl variable as a trend variable requires an EnergyManagementSystem:TrendVariable input object. The access functions listed in Table 6 are used to obtain data from a trend variable during the execution of an Erl program. These functions act on trend variables and return values into the user’s Erl variables for subsequent use in calculations. Each trend function takes the name of the trend variable and an index that identifies how far back in time the function should be applied. Trend variable names are also Erl variables but with special pointers to another data structure with the time series data storage. The trend logs have a first-in, first-out storage array where only the most recent data are retained. Each element in the history corresponds to the result for that value over a zone timestep. The time difference between trend log items is the zone timestep in hours, so that the slope returned by @TrendDirection is in per-hour units.

Table 6. Built-in Functions for Trend Variables in Erl

Function Name	Description	Number of Arguments
@TrendValue	Returns history value for a particular number of timesteps into the past. Dereferences data stored in trend into another Erl variable. Takes trend variable name and the specific timestep into the past to return.	2
@TrendAverage	Returns historical average (mean) for values in trend variable. Takes trend variable name and number of steps into the past to analyze	2
@TrendMax	Returns historical maximum for values in trend variable. Takes trend variable name and number of steps into the past to analyze.	2
@TrendMin	Returns historical minimum for values in trend variable within the index. Takes trend variable name and number of steps into the past to analyze.	2
@TrendDirection	Returns slope of a linear least squares fit of trend data within the index. Positive if trend is increasing, negative if decreasing. Takes trend variable name and number of steps into the past to analyze.	2
@TrendSum	Returns sum of elements stored in trend. Takes trend variable name and number of steps into the past to analyze.	2

The trend functions all take as their second argument an array index. This number should be considered an integer for locating an array position. (It will be rounded down to the nearest integer using Fortran’s FLOOR intrinsic.) This index argument tells the trend functions how far back in time they should reach into the history log when they evaluate the function call. This enables you to compare long- and short-range trends.

Built-in Psychrometric Functions[LINK]

Building modeling often involves calculations related to moist air. A comprehensive set of built-in functions is available for psychrometric calculations. Table 7 lists the functions available for use in Erl programs that are related to moist air properties and some physical properties related to water. More discussion of the psychrometric functions is provided in the section “Pyschrometric services” in the Module Developer Guide.

Table 7. Built-in Psychrometric Functions for Erl

Function Name	Arguments	Description	Units
@RhoAirFnPbTdbW	Result	Density of moist air	kg/m<sup>3</sup>
	Input 1	Barometric pressure	Pa
	Input 2	Drybulb temperature	°C
	Input 3	Humidity ratio	kgWater/kgDryAir
@CpAirFnWTdb	Result	Heat capacity of moist air	J/kg-°C
	Input 1	Humidity ratio	kgWater/kgDryAir
	Input 2	Drybulb temperature	°C
@HfgAirFnWTdb`	Result	Heat of vaporization for vapor	J/kg
	Input 1	Humidity ratio	kgWater/kgDryAir
	Input 2	Drybulb temperature	°C
@HgAirFnWTdb	Result	Enthalpy of the gas
	Input 1	Humidity ratio	kgWater/kgDryAir
	Input 2	Drybulb temperature	°C
@TdpFnTdbTwbPb	Result	Dew-point temperature	°C
	Input 1	Drybulb temperature	°C
	Input 2	Wetbulb temperature	°C
	Input 3	Barometric pressure	Pa
@TdpFnWPb	Result	Dew-point temperature	°C
	Input 1	Humidity ratio	kgWater/kgDryAir
	Input 2	Barometric pressure	Pa
@HFnTdbW	Result	Enthalpy of moist air	J/kg
	Input 1	Drybulb temperature	°C
	Input 2	Humidity ratio	kgWater/kgDryAir
@HFnTdbRhPb	Result	Enthalpy of moist air	J/kg
	Input 1	Drybulb temperature	°C
	Input 2	Relative humidity	Fraction (0.0 .. 1)
	Input 3	Barometric pressure	Pa
@TdbFnHW	Result	Drybulb temperature	°C
	Input 1	Enthalpy of moist air	J/kg
	Input 2	Humidity ratio	kgWater/kgDryAir
@RhovFnTdbRh	Result	Vapor density in air	kg/m<sup>3</sup>
	Input 1	Drybulb temperature	°C
	Input 2	Relative humidity	Fraction (0.0 .. 1)
@RhovFnTdbWPb	Result	Vapor density in air	kg/m<sup>3</sup>
	Input 1	Drybulb temperature	°C
	Input 2	Humidity ratio	kgWater/kgDryAir
	Input 3	Barometric pressure	Pa
@RhFnTdbRhov	Result	Relative humidity	Fraction (0.0 .. 1)
	Input 1	Drybulb temperature	°C
	Input 2	Vapor density in air	kg/m<sup>3</sup>
@RhFnTdbWPb	Result	Relative humidity	Fraction (0.0 .. 1)
	Input 1	Drybulb temperature	°C
	Input 2	Humidity ratio	kgWater/kgDryAir
	Input 3	Barometric pressure	Pa
@TwbFnTdbWPb	Result	Wetbulb temperature	°C
	Input 1	Drybulb temperature	°C
	Input 2	Humidity ratio	kgWater/kgDryAir
	Input 3	Barometric pressure	Pa
@VFnTdbWPb	Result	Specific volume	m<sup>3</sup>/kg
	Input 1	Drybulb temperature	°C
	Input 2	Humidity ratio	kgWater/kgDryAir
	Input 3	Barometric pressure	Pa
@WFnTdpPb	Result	Humidity ratio	kgWater/kgDryAir
	Input 1	Dew-point temperature	°C
	Input 2	Barometric pressure	Pa
@WFnTdbH	Result	Humidity ratio	kgWater/kgDryAir
	Input 1	Drybulb temperature	°C
	Input 2	Enthalpy of moist air	J/kg
@WFnTdbTwbPb	Result	Humidity ratio	kgWater/kgDryAir
	Input 1	Drybulb temperature	°C
	Input 2	Wetbulb temperature	°C
	Input 3	Barometric pressure	Pa
@WFnTdbRhPb	Result	Humidity ratio	kgWater/kgDryAir
	Input 1	Drybulb temperature	°C
	Input 2	Relative humidity	Fraction (0.0 .. 1)
	Input 3	Barometric pressure	Pa
@PsatFnTemp	Result	Saturation pressure	Pa
	Input 1	Drybulb temperature	°C
@TsatFnHPb	Result	Saturation temperature	°C
	Input 1	Enthalpy of moist air	J/kg
	Input 2	Barometric pressure	Pa
@CpCW	Result	Heat capacity of water	J/kg
	Input 1	Temperature	°C
@CpHW	Result	Heat capacity of water	J/kg
	Input 1	Temperature	°C
@RhoH2O	Result	Density of water	kg/m<sup>3</sup>
	Input 1	Temperature	°C

Built-in Curve and Table Functions[LINK]

EnergyPlus has a number of different generic curve and table input objects that are used to describe the performance characteristics for various component models. Table 8 describes a built-in function called @CurveValue that is available for reusing those curve and table input objects in your Erl programs. Although the Erl language could be used to replicate the functionality, reusing those input objects can have advantages because the input may have already been developed for use in traditional component models or the limiting and interpolation methods are helpful. The @CurveValue function expects six arguments, although usually only a subset of them will be used depending on the number of independent variables involved with the curve or table. Because Erl does not support passing optional arguments, dummy variables do need to be included in the function call for all unused independent variables. For example, the Curve:Biquadratric object has only x and y independent variables, so input arguments 4, 5, and 6 will not be used when @CurveValue is evaluated:

Set MyCurveResult = @CurveValue myCurveIndex X1 Y1 dummy dummy dummy;

The first input argument is always an Erl variable that has been declared using an EnergyManagementSystem:CurveOrTableIndexVariable input object. This variable identifies the location of a specific curve or table in the program’s internal data structures. It is important that you do not inadvertently reassign the value held in this variable because it is only filled once at the beginning of the simulation.

Table 8. Built-in Function for Accessing Curves and Tables

Function Name	Arguments	Description	Notes
@CurveValue	Result	Result from evaluating the curve or table as a function of the input arguments
	Input 1	Index variable that “points” to a specific curve or table object defined elsewhere in the IDF.	This variable needs to be declared and filled using an EnergyManagementSystem:CurveOrTableIndexVariable object.
	Input 2	First independent variable	Typically the “X” input value, always used
	Input 3	Second independent variable	Typically the “Y” value, only used if curve/table has two or more independent variables
	Input 4	Third independent variable	Typically the “Z” value, only used if curve/table has three or more independent variables.
	Input 5	Fourth independent variable	Only used if table has four or more independent variables
	Input 6	Fifth independent variable	Only used if table has five independent variables