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Commit 0b4f54fe authored by Ben Anderson's avatar Ben Anderson
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restructured headings slightly

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# Introduction
Now we add the basline water efficiency uptake to each of the two models
Now we add the baseline water efficiency uptake to each of the two models. This has to be done after the expansion to months as it is a monthly uptake model.
## Add baseline WE uptake
> Summary: add baseline WE uptake rates of dual flush WC & low flow shower head uptake and adjust l/day/hh for WCs and showers accordingly
This has to be done after the expansion to months as it is a monthly uptake model. how this works:
# Model WE uptake
* Add baseline WE uptake rates of dual flush WC & low flow shower head uptake and adjust l/day/hh for WCs and showers accordingly
## Backwards estimation of water efficiency uptake
### Backwards estimation of water efficiency uptake
The first step is to allocate households to dual-flush/no dual-flush status and low-flow/no low-flow shower heads for all years. The logic applied is as follows:
* dual-flush/no dual-flush WC:
......@@ -153,9 +152,17 @@ ggplot(dt, aes(x=obsDate, colour = factor(loFlowShower))) +
x = "Date")
```
## Update litres/day
Next we use the water use reduction values given at the start of the section to update the l/day/hh for those who have the dual flush WCs or low flow showers.
```{r update baseline water consumption}
# before
hhFinalDataComboExpandedDT[, .(mean_sumDaily = mean(sumDaily.baseline.madj, na.rm = TRUE),
mean_WC = mean(WC.baseline.madj, na.rm = TRUE),
mean_shower = mean(Shower.baseline.madj, na.rm = TRUE)
), keyby = .(model, metered)
]
# wc ----
hhFinalDataComboExpandedDT <- hhFinalDataComboExpandedDT[, WC.baseline.madj.we := ifelse(dualFlushWC == "Dual flush", WC.baseline.madj * dfWCReduction, WC.baseline.madj)
]
......@@ -174,10 +181,16 @@ hhFinalDataComboExpandedDT <- hhFinalDataComboExpandedDT[, sumDaily.baseline.mad
WC.baseline.madj.we +
WashingMachine.baseline.madj
]
# after
hhFinalDataComboExpandedDT[, .(mean_sumDaily = mean(sumDaily.baseline.madj.we, na.rm = TRUE),
mean_WC = mean(WC.baseline.madj.we, na.rm = TRUE),
mean_shower = mean(Shower.baseline.madj.we, na.rm = TRUE)
), keyby = .(model, metered)
]
```
### Compare effects
## Compare effects by model
Check the effects on each model
```{r plot we adjusted shower and WC use}
......@@ -211,6 +224,7 @@ ggplot(plotDT, aes(x = obsDate,)) +
Well that seems to have an effect!
# Baseline WE Results
## Model 1 (synthetic) results
Now re-draw final baseline charts for papers
......
......@@ -1054,12 +1054,12 @@ ggplot(droughtPhaseDT, aes(x = phase, y = V1, fill = phase)) +
ggsave(paste0("plots_v1/Fig4_5_DroughtPlotKey.pdf"), dpi = 400)
```
## Extract drought & WE-adjusted hot water volumes (for BECC 2017 paper)
# Enhanced WE model?
Use: hhFinalDataComboExpandedCEHDT
# Summary of final data
```{r summary of final WE data}
summary(hhFinalDataComboExpandedCEHDT)
skimr::skim(hhFinalDataComboExpandedCEHDT)
```
```{r Run drought model}
......
......@@ -66,6 +66,7 @@ reqLibsLocal <- c("data.table", # fast data manipulation
"plyr", # fast data munching & rename
"fGarch", # to create consumption distributions
#"doBy", # for summaryBy
"skimr", # for skim
"stargazer", # for neat summary & regression tables
"lubridate", # for date manipulation
"knitr" # for kable
......@@ -265,6 +266,7 @@ R packages used:
* dplyr - for rename [@dplyr]
* fGarch - to create skewed normal consumption distributions [@fgarch]
* lubridate - date manipulation [@lubridate]
* skimr - for skim [@skimr]
* knitr - to create this document [@knitr]
```{r run to here}
......
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