The autoCalibrate package is a collection of functions useful for calibrating EnergyPlus simulations.
Time series data often needs additional columns to label various time periods. This function adds useful columns for the calibration process. Columns include:
These labels are used to filter results, "Summer weekday afternoons", for example.
This function calculates coefficient of variation of the root mean square error. Metrics for checking model calibration. From ASHRAE Guideline 14, pg 15, equations 5.4 and 5.5 http://www.eeperformance.org/uploads/8/6/5/0/8650231/ashrae_guideline_14-2002_measurement_of_energy_and_demand_saving.pdf For hourly analyses the suggested heuristics are < 30% CVRSME.
This function calculates normalized mean bias error. Metrics for checking model calibration. From ASHRAE Guideline 14, pg 15, equations 5.4 and 5.5 http://www.eeperformance.org/uploads/8/6/5/0/8650231/ashrae_guideline_14-2002_measurement_of_energy_and_demand_saving.pdf
The billing data for a given buiding type and location is actually an aggregation of several building types (such as heating fuel and vintage). This function combines several EnergyPlus outputs into one, using a weighted average. The weights of each variation is determined from survey data, and is included as a function input.
This function searches for fields in a dataframe representing an EnergyPlus output for units of [J] and then converts them into units of [kW].
This function reads the EnergyPlus output file(s) into R and reformats them for use in the autocalibration alogorithms. It cleans up the date format, deletes extraneous "design day" rows at the beginning of the output file, renames columns, and calculates the energy use index, EUI, which is power used per unit area.
This function reads the SMUD billing data into R and cleans it. Similar functions are required for each set of utility data. The functions select, rename, and filter columns as needed for compatibility with the analysis functions. It also converts the date to a standard format.
This function processes input files, calls EnergyPlus to simulate the representative set of buildings, and conbines them in a weighted average.
Takes a list of input file names, combines the files, and runs the parametric pre-processor. The parametric pre-processor is a tool included with energy plus, so one of the function inputs is the address of this executable.
The ADM billing data pre-processor determines some schedules for the EnergyPlus simulations. This function transfers scheduling information from the pre-processor into the EnergyPlus schedule file.
Vignettes are long form documentation commonly included in packages. Because they are part of the distribution of the package, they need to be as compact as possible. The html_vignette
output type provides a custom style sheet (and tweaks some options) to ensure that the resulting html is as small as possible. The html_vignette
format:
Note the various macros within the vignette
section of the metadata block above. These are required in order to instruct R how to build the vignette. Note that you should change the title
field and the \VignetteIndexEntry
to match the title of your vignette.
The html_vignette
template includes a basic CSS theme. To override this theme you can specify your own CSS in the document metadata as follows:
output: rmarkdown::html_vignette: css: mystyles.css
The figure sizes have been customised so that you can easily put two images side-by-side.
plot(1:10) plot(10:1)
You can enable figure captions by fig_caption: yes
in YAML:
output: rmarkdown::html_vignette: fig_caption: yes
Then you can use the chunk option fig.cap = "Your figure caption."
in knitr.
You can write math expressions, e.g. $Y = X\beta + \epsilon$, footnotes^[A footnote here.], and tables, e.g. using knitr::kable()
.
knitr::kable(head(mtcars, 10))
Also a quote using >
:
"He who gives up [code] safety for [code] speed deserves neither." (via)
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