hongyuanjia / epluspar Goto Github PK

eplusr will report an " invalid character" error while changing the temperature of schedule in epScan.

Evaluate improvement in code efficiency for the following

• pyMC3
• cov_exp_quad function in Stan
• multi_gp function in Stan

Hi @hongyuanjia,
I am trying to perform energy vs cost-optimization using Epluspar.
Considering the limitations of energyplus in cost analysis, I decided to have my cost variables assigned in an R data frame. My first objective to minimize energy seems ok because it is dependent on Idf file. However, my second objective cost minimization kept prompting the following error:
Error: Objective function 'global_cost' must have an parameter named 'idf'.
In addition: Warning message:
In formals(fun) : argument is not a function

Please I need suggestions to overcome this error.

Hello @hongyuanjia,

Before any comments, I want to thank you and your collaborators for your great job with the packages eplusr and epluspar. These tools are very useful.

I am trying to carry out a Bayesian calibration with epluspar. However, I am facing a problem with adding the following custom meter in $output() :

Meter:Custom,
CustomLights, !- Name
Electricity, !- Resource Type
Depto201_8376d7ca, !- Key Name 1
Zone Lights Electricity Energy, !- Output Variable or Meter Name 1
Depto202_065db6d5, !- Key Name 2
Zone Lights Electricity Energy, !- Output Variable or Meter Name 2
Depto203_ee2eff98, !- Key Name 3
Zone Lights Electricity Energy, !- Output Variable or Meter Name 3
Depto204_b3ba733f, !- Key Name 4
Zone Lights Electricity Energy, !- Output Variable or Meter Name 4
Depto205_ca4768b6, !- Key Name 5
Zone Lights Electricity Energy, !- Output Variable or Meter Name 5
Depto206_2149faff, !- Key Name 6
Zone Lights Electricity Energy; !- Output Variable or Meter Name 6

When I run a simulation with the seed .idf file the custom meter is present in the mdd file :

══ EnergyPlus Meter Data Dictionary File ════════════════════════════════════════════════════════════════════════════════════════════════════════

• EnergyPlus version: NA
• Simulation started: 2022-11-03 16:09:00

── Details ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
index reported_time_step report_type variable units
1: 623 Zone Meter CUSTOMLIGHTS

However, when I try to add it with $output() I have the following error :

Error: Invaid variable name found in input 'key_value': 'CUSTOMLIGHTS'.

The custom does not appear in the data frame mdd <- bc$read_mdd().

Custom meters is very useful to calibrate the model using measured data from submetering data.

Best regards

Fabien

In sensitivity::morris(), there is a scale parameter to scale the input design of experiments before computing the elementary effects so that all factors vary within the range [0,1].

This argument should be added and be set to TRUE by default.

The morris class in python SALib package does this by default in its sample() method.

Choose a proper name for this package. Possibles:

• epcalib = ep + calib
• epluscalib = eplus + calib
• epstats = ep + stats
• epanalysis = ep + analysis
• epluspar = eplus + par (parameter OR parametric analysis in R)

Using sensitivity::morrisMultOut() to support multi-dimension outputs.

Since this package is public, we can now build a public package site using pkgdown.

Good day Sir @hongyuanjia,
Please I have been trying to store certain design parameters from my optimization process so I can access them afterwards. All my efforts have not been successful. Below is a sample of my code. Kindly assist me.

iteration <-  0
wall_insulation <- 0
roof_insulation <- 0
win_frame <- "" 
total_investment_opaque <- 0

#create a data frame
hold <- data.frame(iteration, wall_insulation, 
                             roof_insulation, win_frame,
                             total_investment_opaque) 

#-------------------------------------------------------------------------------
# combine all measures into one
design_options <- function(idf, wall_insulation_thickness,
                           roof_insulation_thickness,
                           window_frame,
                           ) {
  
  # get results
  types <- set_type (idf, window_frame)
  
  
  set_insulation_wall(idf, wall_insulation_thickness)
  set_insulation_roof(idf, roof_insulation_thickness)
  
   #set vital variables for data frame hold
  hold$wall_insulation <- wall_insulation_thickness
  hold$roof_insulation <- roof_insulation_thickness
  hold$win_frame <- window_frame
    
  idf
}
#-------------------------------------------------------------------------------
add_init_cost_to_db <- function(idf, param) {
initial_investment <- sum(initial_investment_envelope, 
                            initial_investment_floor)
  
  #set data frame hold
  hold$total_investment_opaque<- initial_investment
}

    wall_insulation <- hold$wall_insulation
    roof_insulation <- hold$roof_insulation
    win_frame <- hold$win_frame
     total_investment_opaque<- hold$total_investment_opaque
 
    
    
    iteration <<- iteration + 1
    
    vitalsimulationresults <- rbind( vitalsimulationresults, data.frame
                                                    (iteration, wall_insulation,
                                                     roof_insulation, win_frame, 
                                                     total_investment_opaque))
    

Good day Hongyuanjia,
please I need help with functions in epluspar.
If
choice_space is used to specify optimization parameter of character type
float_space is used to specify optimization parameter of float type in a sequential manner
integer_spaceis used to specify optimization parameter of integer type in a sequential manner
what functions can I use to specify float and integer parameters in an orderly manner I wish them to be respectively ?
For example, to enter parameters: 20,40,50,60,90

Good sir,
I have been trying to add an object to my IDF file using eplusr, but I kept encountering an error. Below are my codes and the error.
mywork<-(WindowMaterial:Glazing = list(Name = "clear glass 4 mm",
Optical_Data_Type = "SpectralAverage",
SpectralAverage =0.0039,
Solar_Transmittance_at_Normal_Incidence =0.812 ,
Front_Side_Solar_Reflectance_at_Normal_Incidence =0.076 ,
Back_Side_Solar_Reflectance_at_Normal_Incidence =0.075,
Visible_Transmittance_at_Normal_Incidence =0.893,
Front_Side_Visible_Reflectance_at_Normal_Incidence =0.084,
Back_Side_Visible_Reflectance_at_Normal_Incidence =0.084,
Infrared_Transmittance_at_Normal_Incidence =0,
Front_Side_Infrared_Hemispherical_Emissivity =0.840,
Back_Side_Infrared_Hemispherical_Emissivity =0.840,
Conductivity = 0.9))
idf$add(mywork)

Error in WindowMaterial:Glazing = list(Name = "clear glass 4 mm", Optical_Data_Type = "SpectralAverage", :
object 'WindowMaterial' not found

Good day, I have been trying to install epluspar package, but I kept receiving the following error message.

Error: Failed to install 'epluspar' from GitHub:
(converted from warning) installation of package ‘C:/Users/Toshiba/AppData/Local/Temp/RtmpcN6aMt/file225c784e4a27/epluspar_0.0.0.9000.tar.gz’ had non-zero exit status
In addition: Warning messages:
1: In untar2(tarfile, files, list, exdir) :
skipping pax global extended headers
2: In untar2(tarfile, files, list, exdir) :
skipping pax global extended headers

cmdstanr provides a clean interface to Stan services with minimal dependencies.

There is user observing significant time saving:

Simple linear regression with 5 observations:

system.time for rstan was (85.876   5.269  47.514).
Same model in CmdStanR (compilation + sampling): 
system.time ( 1.522   0.321   2.435 )

Current way to specify parameters in SensitivityJob class is using $param() which only makes it possible to specify a single field value range.

sen <- Sensitivity$new(path_idf, path_epw)
sen$param(
    # Syntax: Object Name = list(Field = c(Min, Max, Levels))
    GP01 = list(Thickness = c(min = 0.01, max = 1, levels = 5)),
    `Supply Fan 1` = list(Fan_Total_Efficiency = c(0.1, 1.0, 5)),
    # See `r` and `grid_jump` in `sensitivity::morris`
    .rep = 12, .grid_jump = 4
)

There are cases when the user may want to modify the same field in all related objects, e.g. change the fan efficiency for all fans. Below are several possible interfaces that came into my mind:

• (A) Use a special notation := (borrowed from data.table package) in $param() to indicate that input field range should be applied to all objects in that class. For example:

  sen$param(`Fan:VariableVolume` := list(Fan_Total_Efficiency = c(0.1, 1.0, 5)))

  • Pros: Maybe more intuitive to new users.
  • Cons: Have to implement additional functions to manipulate R's language objects to make this work.

• (B) Since SensitivityJob class inherits from ParametricJob class in current implementation, the user always can use $apply_measure() to apply complex modifications on the seed mode. Changing the example in README to modify the efficiency in all fans is equivalent to:

  my_actions <- function (idf, thickness, fan_efficiency) {
      # change the thickness of a single material
      idf$set(GP01 = list(Thickness = thickness))
  
      # change the efficiency of all fans
      fan <- idf$to_table(class = "Fan:VariableVolume", all = TRUE)
      fan[field == "Fan Total Efficiency", value := as.character(fan_efficiency)]
      idf$update(fan)
  
      idf
  }
  
  sen$apply_measure(my_action,
      thickness = c(min = 0.01, max = 1, levels = 5),
      fan_efficiency = c(0.1, 1.0, 5)
  )

  • Pros: Using existing API. Less code work. Much more flexible. The arugments in the function can also be used as a factor name in sensitivity::morris().
  • Cons: Some new users may treat this way as too complicated for them.

It may be better to do both ways to provide more options for different level users.

Good day Sir.
The code bellow, has been working perfectly for sometime.
But after an update to my packages yesterday, It refused to work again.

#load packages
library(eplusr)
library(tidyverse)
library(epluspar)
library(openxlsx)
library(ggplot2)
library(formattable)

# read base case file
idf <- read_idf(system.file("extdata/1D 2F base_case.idf", package = "eplusr"))
# read weather file path
path_epw <- file.path(eplus_config(8.9)$dir, "WeatherData/Nicosia_hour.epw")
# run case simulation
job <- idf$run(path_epw)

#Extract tabular data
primary_energy <- as.double(idf$last_job()$tabular_data( 
  report_name = "AnnualBuildingUtilityPerformanceSummary",
  table_name = "Site and Source Energy",
  row_name = "Total Site Energy",
  column_name = "Total Energy")
  $value)

> Error in l$contains : $ operator is invalid for atomic vectors

Hello @hongyuanjia
I have read your publication titled: eplusr: A framework for integrating building energy simulation and data-driven analytics.
I tried to run a simply optimization problem to help me understand Epluspar R package much better, but I kept encountering an error.
I selected only one parameter for my optimization, which is the wall insulation, to be varied from 1cm to 6cm.
My objective function is to minimize Energy Per Conditioned Building Area [kWh/m2].

Here is the modified listing I tried to run:

#load packages
library(eplusr)
library(tidyverse) # for data-driven analysis
library(epluspar)

# read IDF
idf <- read_idf(system.file("extdata/1D 2F.idf", package = "eplusr"))

# read EPW
path_epw <- file.path(eplus_config(8.9)$dir, "WeatherData/Nicosia_hour.epw")
epw <- read_epw(path_epw)

# create a GA optimization job
ga <- gaoptim_job(idf, epw)

#Optimization variables#
# define a measure to change the insulation thickness of the exterior wall
set_insulation <- function (idf, thickness) {
  idf$set(`polystyrene eps (5 cm)` = list(thickness = thickness))
  idf
}

# combine measures into one
design_options <- function (idf,insulation_thickness) { 
  idf <- set_insulation(idf, insulation_thickness) 
  idf
}    

# specify design space of parameters 
ga$apply_measure(design_options,insulation_thickness = float_space(0.01, 0.06,0.01))

# define an objective function to  energy per conditioned building area
primary_energy <- function (idf) {
  as.double(idf$last_job()$tabular_data( 
    report_name = "Site and Source Energy", 
    row_name = "Total Site Energy",
    column_name = "Energy Per Conditioned Building Area [kWh/m2]" )
    $value) 
}

# set optimization objectives 
ga$objective(primary_energy,.n=NULL, .dir = "min")

# specify how to mix solutions 
ga$recombinator()
# specify how to change parts of one solution randomly 
ga$mutator()
# specify how to select best solutions 
ga$selector()
# specify the conditions when to terminate the computation 
ga$terminator(max_gen = 100L)

# run optimization 
ga$run(mu = 20)

# get all population 
population <- ga$population()
# get Pareto set 
pareto <- ga$pareto_set()


Here is the error I kept encountered when I tried to run it:
 
> # run optimization 
> ga$run(mu = 20)
-- Initialization --------------------------------------------------------------
  * Create initial population
Checking if parameter(s) has been set ...
Checking if objective(s) has been set ...
Validating parameter function 'design_options' ...
Validating objective function(s)...
Error: Idf has been modified since read or last saved. Please save it using `$save()` before running.
>

Once #1 is closed, prepare to submit epluspar to CRAN. Also related to ideas-lab-nus/eplusr-paper#2

@hongyuanjia, greetings of the day.

Bellow is an objective function code, that is meant to evaluate discomfort hours, by extracting data from the table displayed above.

# Evaluate discomfort hours
  discomfort_hours <- function(idf) {

  # Local definition of time (hours) not meeting adaptive comfort model
 discomfort_time <- as.double(idf$last_job()$tabular_data(
   report_name = "AdaptiveComfortSummary",
   table_name = "Time Not Meeting the Adaptive Comfort Models during Occupied Hours",
   row_name = "OCCUPANTS",
   column_name = "ASHRAE55 80% Acceptability Limits"
  )
  $value)

# Evaluate the discomfort hours
 discomfort_time<-(discomfort_time/8760)*100

 # return discomfort hours
 discomfort_time
 
}

However, I encountered the following error when I tried to run the code. Please, can you help me resolve the problem, sir?

-- Initialization --------------------------------------------------------------------------------------
  * Create initial population
Checking if parameter(s) has been set ...
Checking if objective(s) has been set ...
Validating parameter function 'design_options' ...
Validating objective function(s)...
  [1] 'combined_primary_energy' --> OK
  [2] 'combined_global_cost' --> OK
Error: Objective function 'discomfort_hours' should return at least a length-one vector instead of a 0-length one.