16-explore.Rmd

# Model Exploration {#explore}

```{r, include=FALSE}
source("_common.R", local = knitr::knit_global())
```

## Prerequisites

In this chapter we will explore the model to uncover energy characteristics and
create an overview of general trends. We will use eplusr to run the EnergyPlus
simulation and extract relevant model inputs/outputs, `{tidyverse}` for data
transformation, and `{ggplot2}` to visualize the extracted and subsequently
transformed data.

```{r, message=FALSE}
library(eplusr)
library(tidyverse)
library(lubridate)
library(ggplot2)
library(here)
```

We will be working with the IDF and EPW file that pertains to the U.S.
Department of Energy (DOE) Commercial Reference Building and Chicago's TMY3
respectively.

```{r, message=FALSE}
path_idf <- here("data", "idf", "RefBldgMediumOfficeNew2004_Chicago.idf")
model <- read_idf(path_idf)

path_epw <- here("data", "epw", "USA_IL_Chicago-OHare.Intl.AP.725300_TMY3.epw")
epw <- read_epw(path_epw)
```

## Extracting

Before carrying out any model exploration, you need to first specify the outputs
of interest. The code below preprocesses the model by adding the list of output
meters and variables to the model (See Chapter \@ref(output) for details).

```{r}
preprocess_idf <- function(idf, meters, variables) {
    # make sure weather file input is respected
    idf$SimulationControl$Run_Simulation_for_Weather_File_Run_Periods <- "Yes"

    # make sure simulation is not run for sizing periods
    idf$SimulationControl$Run_Simulation_for_Sizing_Periods <- "No"

    # make sure energy consumption is presented in kWh
    if (is.null(idf$OutputControl_Table_Style)) {
        idf$add(OutputControl_Table_Style = list("HTML", "JtoKWH"))
    } else {
        idf$OutputControl_Table_Style$Unit_Conversion <- "JtoKWH"
    }

    # remove all existing meter and variable outputs
    if (!is.null(idf$`Output:Meter`)) {
        idf$Output_Meter <- NULL
    }

    # remove all existing meter and variable outputs
    if (!is.null(idf$`Output:Table:Monthly`)) {
        idf$`Output:Table:Monthly` <- NULL
    }

    if (!is.null(idf$`Output:Variable`)) {
        idf$Output_Variable <- NULL
    }

    # add meter outputs to get hourly time-series energy consumption
    idf$add(Output_Meter := meters)

    # add variable outputs to get hourly zone air temperature
    idf$add(Output_Variable := variables)

    # make sure the modified model is returned
    return(idf)
}

meters <- list(
    key_name = c(
        "Cooling:Electricity",
        "Heating:NaturalGas",
        "Heating:Electricity",
        "InteriorLights:Electricity",
        "ExteriorLights:Electricity",
        "InteriorEquipment:Electricity",
        "Fans:Electricity",
        "Pumps:Electricity",
        "WaterSystems:NaturalGas"
    ),
    Reporting_Frequency = "Hourly"
)

variables <- list(
    key_value = "*",
    Variable_Name = c(
        "Site Outdoor Air Drybulb Temperature",
        "Site Outdoor Air Relative Humidity"
    ),
    Reporting_Frequency = "Hourly"
)

model <- preprocess_idf(model, meters, variables)
```

You can then run the simulation and it will be possible to extract the output of
interest from the model.

```{r, out.lines = 15}
model$save(here("data", "idf", "model_preprocessed.idf"), overwrite = TRUE)
job <- model$run(epw, dir = tempdir())
```

## Energy signature

```{r}
pt_of_interest <- c(
    "Site Outdoor Air Drybulb Temperature",
    "Cooling:Electricity",
    "Heating:NaturalGas"
)

report <- job$report_data(
        name = pt_of_interest, environment_name = "annual",
        all = TRUE, wide = TRUE
    ) %>%
    select(datetime, month, day, hour, day_type, contains(pt_of_interest)) %>%
    rename_with(~pt_of_interest, .cols = contains(pt_of_interest)) %>%
    pivot_longer(
        cols = c("Cooling:Electricity", "Heating:NaturalGas"),
        names_to = "type", values_to = "value"
    ) %>%
    mutate(
        value = value * 1e-6, # convert J to MJ
        month = ordered(month.abb[month], month.abb) # replace month index with month name
    )
```

```{r}
ggplot(report, aes(
    x = `Site Outdoor Air Drybulb Temperature`,
    y = value,
    color = type
)) +
    geom_point(shape = 1, alpha = 0.7) +
    facet_grid(rows = vars(type)) +
    scale_color_brewer(palette = "Set2") +
    xlab(expression("Outdoor drybulb temperature" ~ (degree * C))) +
    ylab("Energy consumption (MJ)") +
    theme(legend.position = "none")
```

```{r}
report_heating <- report %>%
    filter(type == "Heating:NaturalGas")

ggplot(report_heating, aes(
    x = day, y = hour,
    fill = value
)) +
    geom_tile() +
    scale_fill_viridis_c(
        name = "Heating\nEnergy (MJ)",
        option = "plasma"
    ) +
    facet_grid(cols = vars(month)) +
    ylab("Hour of the day") +
    xlab("Day of the week") +
    theme(legend.position = "bottom")
```