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Commit c90866b1 authored by Ben Anderson's avatar Ben Anderson
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moved to SAVE

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# test SAVE SDRC 2.1 baseline IPF (based on time-use) against
# SDRC 2.2 based on observd kWh
# Libraries ----
library(data.table)
library(drake)
library(ggplot2)
library(here)
library(hms)
library(lubridate)
library(survey)
# Paramaters ----
pPath <- paste0(here::here(), "/plots/") # where to put plots
cPath <- "~/Data/UK_Census/Census_LUTS/2011/EngWales/"
# Rmd
rmdRoot <- paste0(here::here(), "/analysis/compareModels/")
# Load Census data (LSOAs) ----
# LSOA 2001 <-> LSOA 2011 LUT
lsoaLutDT <- data.table::fread(paste0(cPath,
"LSOA01_LSOA11_EW_LU.zip Folder/LSOA01_LSOA11_EW_LUv2.csv"))
sotonLSOA_01DT <- lsoaLutDT[LSOA01NM %like% "Southampton"]
sotonLSOA_11DT <- lsoaLutDT[LSOA11NM %like% "Southampton"]
# unchanged?
table(sotonLSOA_01DT$CHGIND)
table(sotonLSOA_11DT$CHGIND)
sotonLSOA_01DT[, zonecode := LSOA01CD] # set to 2001 LSOA for merge
setkey(sotonLSOA_01DT, zonecode)
sotonLSOA_11DT[, zonecode := LSOA11CD] # set to 2011 LSOA for merge
setkey(sotonLSOA_11DT, zonecode)
# Load Census data (OAs) ----
# OA 2001 <-> OA 2011 LUT
oa2001_2011LutDT <- data.table::fread(paste0(cPath,
"OA01_OA11_LAD11_EW_LU_csv/OA01_OA11_LAD11_EW_LU.csv"))
# not used - likange done at LSOA level
# Load Cesus data (OA <-> LSOA) ----
oa2011LutDT <- data.table::fread(paste0(cPath,
"OA11_LSOA11_MSOA11_LAD11_EW_LU.zip Folder/",
"OA11_LSOA11_MSOA11_LAD11_EW_LUv2.csv"))
sotonOA_11DT <- oa2011LutDT[LAD11NM %like% "Southampton"]
# Function: SDRC 2.1 Time Use model ----
getSDRC2.1 <- function(version){
message("Loading: ", version)
sdrc2.1tuPath <- "~/University of Southampton/SAVE - Documents/WP2-Customer-Model/SDRC-v2.1/results/ONS-TU-2000/"
sdrc2.1f <- paste0("ONS-TU-2000-SAVE-SDRC-v2.1-household-diary-file-weekdays-winter-",version,".dta")
# get the data
message("Loading: ", sdrc2.1f)
df <- paste0(sdrc2.1tuPath,sdrc2.1f)
dt <- data.table::as.data.table(foreign::read.dta(df))
# convert to kWh
dt[, power_sumkW := power_sum/1000]
dt[, power_sumkWh := power_sumkW/2] # cos it's half hours
setkey(dt, hhid_numeric)
uniqueN(dt$hhid_numeric)
# add weights
# weights files
sdrc2.1wPath <- "~/University of Southampton/SAVE - Documents/WP2-Customer-Model/SDRC-v2.1/results/ONS-TU-2000-sms/data/"
#sdrc2.1wf <- "output/ONS-TU-2000-Census-2001-wtdh_ug-Southampton-simoutput-weights.dta"
sdrc2.1wf <- "output/ONS-TU-2000-Census-2001-wtdh_ug-Southampton-v3m1_weekdays_winter_simoutput_weights.dta"
message("Loading weights: ", sdrc2.1wf)
df <- paste0(sdrc2.1wPath,sdrc2.1wf)
wdt <- data.table::as.data.table(foreign::read.dta(df))
setkey(wdt, hhid_numeric)
uniqueN(wdt$hhid_numeric)
# join them
jdt <- wdt[dt[, .(s_halfhour, ba_hrpindsurvey, hhid_numeric, power_sum, power_sumkWh)], allow.cartesian=TRUE]
summary(head(jdt))
# aggregate to LSOA (zonecode = LSOA)
# set survey design so we can do weighted stats
message("Setting svydesign ", version)
svy.jdt <- survey::svydesign(ids = ~hhid_numeric,
weights = ~weight,
data = jdt[!is.na(weight)])
message("Aggregating ", version)
svyt <- survey::svyby(~power_sumkWh, ~s_halfhour+zonecode, svy.jdt, # this is where you'd put the survey categories to average over
svymean,
keep.var = TRUE,
exclude=NULL,
na.action=na.pass
)
dt <- as.data.table(svyt)
dt[, dateTime := lubridate::as_datetime(s_halfhour, tz = "UTC")]
dt[, hms := hms::as.hms(dateTime)]
dt[, mean_powerkWh := power_sumkWh]
setkey(dt, zonecode)
setkey(sotonLSOA_01DT, zonecode)
# > plot LSOAs ----
plotDT <- dt[sotonLSOA_01DT, allow.cartesian=TRUE] # just Soton
p <- ggplot2::ggplot(plotDT,
aes(x = hms,
y = mean_powerkWh,
group = zonecode,
colour = zonecode)) +
geom_line()
p <- p + theme(legend.position = "None") + # no legend
labs(x = "Time of Day",
y = "Mean household kWh per half hour",
caption = paste0("2001 Census & Time Use Model (Model ", version," ): Winter weekdays\n",
uniqueN(plotDT$zonecode), " Southampton LSOAs")
)
ggplot2::ggsave(paste0(pPath,"lsoaProfiles_Winter-Weekdays-Time-Use-Model-", version, "-Sim.png"),
p)
return(dt)
}
# Function: SDRC 2.2 Event day ----
# Can load from OA file
# useful for spurious outier effects discussion
getSDRC2.2ED <- function(){
sdrc2.2Path <- "~/Data/SAVE/processed/"
f <- "SAVE_SDRC_2.2_OA_Event_Day_kWh_profiles_v1.csv.gz"
sdrc2.2_ED_OA_dt <- data.table::fread(paste0(sdrc2.2Path,
f))
# collapse to LSOAs
sdrc2.2_ED_OA_dt[, zonecode := oaCode]
setkey(sdrc2.2_ED_OA_dt, zonecode)
sotonOA_11DT[, zonecode := OA11CD]
setkey(sotonOA_11DT, zonecode)
sdrc2.2_ED_OA_dt <- sotonOA_11DT[sdrc2.2_ED_OA_dt]
dt <- sdrc2.2_ED_OA_dt[, .(meankWh = mean(meanHalfHourkWh)),
keyby = .(LSOA11CD, obsDateTime30m, obsDate)]
dt[, obsDateTime := lubridate::as_datetime(obsDateTime30m)]
dt[, hms := hms::as.hms(obsDateTime, tz = "UTC")] # just in case
dt[, halfHour := hms::trunc_hms(hms, 30*60)]
# > plot LSOAs ----
p <- ggplot2::ggplot(dt,
aes(x = halfHour,
y = meankWh,
group = LSOA11CD,
colour = LSOA11CD)) +
geom_line() +
facet_grid(obsDate ~ .)
p <- p + theme(legend.position = "None") + # no legend
labs(x = "Time of Day",
y = "Mean household kWh per half hour",
caption = paste0("2011 Census & Observed Event Day kWh\n",
uniqueN(dt$LSOA11CD), " Southampton LSOAs")
)
ggplot2::ggsave(paste0(pPath,"lsoaProfiles_Event-Day-Sim.png"),
p)
return(dt)
}
# Function: SDRC 2.2 Baseline kWh winter weekday ----
# Need to calculate from weights file etc
getSDRC2.2BL <- function(){
sdrc2.2Path <- "~/Data/SAVE/processed/"
f <- "SAVEipfHouseholdWeights_Baseline_v_sdrc2.2_all_hhs.csv.gz" # get weights file
sdrc2.2_wts_OA_dt <- data.table::fread(paste0(sdrc2.2Path,
f))
# these are OA level weights so we need to aggregate (sum) them to LSOA level
sdrc2.2_wts_OA_dt[, zonecode := oaCode]
setkey(sdrc2.2_wts_OA_dt, zonecode)
summary(sdrc2.2_wts_OA_dt)
# add LSOA codes
sotonOA_11DT[, zonecode := OA11CD]
setkey(sotonOA_11DT, zonecode)
sdrc2.2_wts_OA_dt <- sdrc2.2_wts_OA_dt[sotonOA_11DT]
sdrc2.2_wts_LSOA_dt <- sdrc2.2_wts_OA_dt[ipfWeight > 0, .(wgt = sum(ipfWeight), # ignore zeros
mean_wgt = mean(ipfWeight),
sd_wgt = sd(ipfWeight)),
keyby = .(LSOA11CD,bmg_id)]
summary(sdrc2.2_wts_LSOA_dt)
# load the baseline January 2017 kWh data
f <- "jan2017clamp15minuteData.csv.gz" # get Wh data file
sdrc2.2_Jan2017kWh_dt <- data.table::fread(paste0(sdrc2.2Path,
f))
sdrc2.2_Jan2017kWh_dt[, kWh := consumptionWh/1000]
sdrc2.2_Jan2017kWh_dt[, obsDateTime := lubridate::as_datetime(obsDateTime,
tz = "UTC")] # make sure
# now create per hh means by half hour by weekday vs weekend for the whole period
# and then use this as basis for merge to weights
sdrc2.2_Jan2017kWh_dt[, wday := lubridate::wday(obsDateTime, label = TRUE)]
sdrc2.2_Jan2017kWh_dt[, weekday := "Weekday"]
sdrc2.2_Jan2017kWh_dt[wday == "Sat" | wday == "Sun", weekday := "Weekend"]
sdrc2.2_Jan2017kWh_dt[, hms := hms::as.hms(obsDateTime, tz = "UTC")] # crucial if you're doing the analysis outsde UK!
sdrc2.2_Jan2017kWh_dt[, halfHour := hms::trunc_hms(hms, 30*60)] # half hours
# sum to half-hours
sdrc2.2_Jan2017kWh_dt <- sdrc2.2_Jan2017kWh_dt[, .(nObs = .N,
sumkWh = sum(kWh, na.rm = TRUE)), # add up the two 15 min values
keyby = .(halfHour, obsDate = lubridate::as_date(obsDateTime),
weekday, bmg_id )]
# mean for weekday & weekend
aggJan2017kWh_dt <- sdrc2.2_Jan2017kWh_dt[, .(nObs = .N,
meankWh = mean(sumkWh, na.rm = TRUE)), # add up the two 15 min values
keyby = .(halfHour, weekday, bmg_id )]
# test plot of weekend vs weekday profiles
summary(aggJan2017kWh_dt)
# remove the NAs as they are when a bmg_id starts so we have no consumption history
plotDT <- sdrc2.2_Jan2017kWh_dt[, .(meankWh = mean(sumkWh)),
keyby = .(halfHour, weekday)]
p <- ggplot2::ggplot(plotDT, aes(x = halfHour, y = meankWh,
colour = weekday, group = weekday)) +
geom_line()
ggplot2::ggsave(paste0(pPath, "saveProfiles_Winter-Obs-kWh.png"), p)
setkey(aggJan2017kWh_dt, bmg_id)
setkey(sdrc2.2_wts_LSOA_dt, bmg_id)
aggJan2017kWh_dt[,s_halfhour := as.character(halfHour)] # so srvy doesn't break
sdrc2.2_wts_LSOA_dt <- sdrc2.2_wts_LSOA_dt[wgt > 0] # remove 0s
# merge kWh and weights
janSimdt <- aggJan2017kWh_dt[sdrc2.2_wts_LSOA_dt, allow.cartesian=TRUE]
# keep only what we need for weekdays
janSimWDaydt <- janSimdt[weekday == "Weekday", .(bmg_id, wgt, LSOA11CD,s_halfhour, weekday, meankWh)]
janSimWDaydt$weekday <- NULL # save some memory
# make a svydes
svy.wd_dt <- survey::svydesign(ids = ~bmg_id,
weights = ~wgt,
data = janSimWDaydt)
svyt_wd <- survey::svyby(~meankWh, ~s_halfhour+LSOA11CD, svy.wd_dt, # this is where you'd put the survey categories to average over
svymean,
keep.var = TRUE,
exclude=NULL,
na.action=na.pass
)
dt <- as.data.table(svyt_wd) # convert to data.table
dt[, halfHour := hms::parse_hms(s_halfhour)]
dt[, halfHour := hms::as.hms(halfHour, tzone = "UTC")]
# > plot LSOAs ----
p <- ggplot2::ggplot(dt, aes(x = halfHour,
y = meankWh, colour = LSOA11CD)) +
geom_line() +
theme(legend.position = "None")
ggplot2::ggsave(paste0(pPath,"lsoaProfiles_Winter-Weekdays-Obs-kWh-Sim.png"),
p)
return(dt)
}
# drake plan ----
plan <- drake::drake_plan(
sdrc2.1v1 = getSDRC2.1("v1.0"),
sdrc2.1v2 = getSDRC2.1("v2.0"),
sdrc2.2_ED = getSDRC2.2ED(), # loads from OA results file
#sdrc2.2_BL = getSDRC2.2BL(), # requires 2 inputs - weights & kWh obs
report = rmarkdown::render(
knitr_in(paste0(rmdRoot, "compareModels_IMA2019.Rmd")),
output_file = file_out(paste0(rmdRoot, "compareModels_IMA2019.html")),
quiet = FALSE
)
)
# test it ----
plan
config <- drake_config(plan)
vis_drake_graph(config)
# do it ----
make(plan)
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---
title: "Comparing SAVE and GREEN Grid small area electricity demand models"
subtitle: "Analysis for paper presented at the IMA 7th World Congress, Galway June 19-21"
author: "Ben Anderson (University of Otago)"
date: 'Last run at: `r Sys.time()`'
output:
bookdown::html_document2:
code_folding: hide
fig_caption: yes
number_sections: yes
self_contained: no
toc: yes
toc_depth: 3
toc_float: yes
bibliography: '`r path.expand("~/bibliography.bib")`'
---
```{r setup, include=FALSE}
# This code should be called from the .R file in this directory to make sure all drake objects have already been created.
# change default
knitr::opts_chunk$set(echo = TRUE)
# Log compile time:
startTime <- proc.time()
# load packages we didn't load in the .R file
myPackages <- c(
"ggplot2", # plots
"kableExtra", # pretty tables
"skimr", # for skim
"viridis" # colour-blind friendly palettes
)
spatialec::loadLibraries(myPackages) # this will try to install packages it can't load
# ---- Local Settings ----
```
# Report Purpose
To compare SAVE small area models:
* SDRC2.1 v1.0: Time Use Model with all hot water = elec but not heat
* SDRC2.1 v2.0: Time Use Model with no hot water = elec and not heat
* SDRC2.2: Observed kWh Model
# Join models
Join SAVE models
```{r joinModels}
# Compare models ----
# join them at LSOA level
# v2.1 = 2001 v2,2 = 2011: some LSOAs may have changed
sdrc2.1v1_LSOA_dt <- drake::readd(sdrc2.1v1)
sdrc2.1v2_LSOA_dt <- drake::readd(sdrc2.1v2)
sdrc2.2_LSOA_dt <- drake::readd(sdrc2.2_BL)
sdrc2.1v1_LSOA_dt[, halfHour := hms] # mean weekday
sdrc2.1v1_LSOA_dt[, sdrc2.1v1meankWh := mean_powerkWh]
sdrc2.1v1_LSOA_dt <- sdrc2.1v1_LSOA_dt[,.(zonecode, halfHour, sdrc2.1v1meankWh)]
sdrc2.1v1_LSOA_dt[, halfHour := hms::trunc_hms(halfHour, 60*30)]
data.table::setkey(sdrc2.1v1_LSOA_dt, zonecode, halfHour)
sdrc2.1v2_LSOA_dt[, halfHour := hms] # mean weekday
sdrc2.1v2_LSOA_dt[, sdrc2.1v2meankWh := mean_powerkWh]
sdrc2.1v2_LSOA_dt <- sdrc2.1v2_LSOA_dt[,.(zonecode, halfHour, sdrc2.1v2meankWh)]
sdrc2.1v2_LSOA_dt[, halfHour := hms::trunc_hms(halfHour, 60*30)] # to remove the 1 sec (why is that there?)
data.table::setkey(sdrc2.1v2_LSOA_dt, zonecode, halfHour)
sdrc2.2_LSOA_dt[, sdrc2.2meankWh := meankWh] # mean weekday
sdrc2.2_LSOA_dt[, zonecode := LSOA11CD]
sdrc2.2_LSOA_dt <- sdrc2.2_LSOA_dt[,.(zonecode, halfHour, sdrc2.2meankWh)]
data.table::setkey(sdrc2.2_LSOA_dt, zonecode, halfHour)
# 2001 LSOAs:
data.table::uniqueN(sdrc2.1v1_LSOA_dt$zonecode)
data.table::uniqueN(sdrc2.1v2_LSOA_dt$zonecode)
# 2011 LSOAs:
data.table::uniqueN(sdrc2.2_LSOA_dt$zonecode)
# so we'll get some NAs through not matching
mergedLSOA <- sdrc2.1v1_LSOA_dt[sdrc2.1v2_LSOA_dt][sdrc2.2_LSOA_dt]
kableExtra::kable(head(mergedLSOA), caption = "Combined models data table: 10 rows") %>%
kable_styling()
t <- summary(mergedLSOA)
kableExtra::kable(t, caption = "Summary of combined models data table") %>%
kable_styling()
```
# Compare SDRC2.1 Model 1 with Model 2
At LSOA level...
```{r compare_v1_v2, fig.cap="Half-hourly mean kWh for model 1 vs model 2 for each LSOA"}
mergedLSOA[,peakFlag := "4. All other times"]
mergedLSOA[halfHour >= hms::as.hms("07:00:00") & halfHour < hms::as.hms("09:00:00"),
peakFlag := "1. Morning Peak"]
mergedLSOA[halfHour >= hms::as.hms("16:00:00") & halfHour < hms::as.hms("20:00:00"),
peakFlag := "3. Evening Peak"]
mergedLSOA[halfHour >= hms::as.hms("09:00:00") & halfHour <= hms::as.hms("16:00:00"),
peakFlag := "2. Day time"]
# plot LSOAs ----
p <- ggplot2::ggplot(mergedLSOA, aes(x = sdrc2.1v1meankWh,
y = sdrc2.1v2meankWh,
colour = zonecode)) +
geom_abline(intercept = 0, slope = 1) +
geom_point() +
facet_wrap(. ~ peakFlag) +
theme(legend.position = "None") +
labs(x = "mean kWh (Sim: Time Use model v1)",
y = "mean kWh (Sim: Time Use model v2)",
caption = paste0("Southampton: LSOAs\n",
"1:1 line shown"))
#xlim(0,0.8) +
#ylim(0,0.6) +
p
ggplot2::ggsave(paste0(pPath,"lsoaProfiles_Winter-Weekdays-Compare-SDCR2.1_v1_v2.png"), p)
peaks <- unique(mergedLSOA$peakFlag)
for(p in peaks){
message("Testing correlation for: ", p)
print(with(mergedLSOA[peakFlag == p], cor.test(sdrc2.1v1meankWh,
sdrc2.1v2meankWh,
na.action = "na.omit")
))
print(with(mergedLSOA[peakFlag == p], cor.test(sdrc2.1v1meankWh,
sdrc2.1v2meankWh,
na.action = "na.omit",
method = "spearman")
))
}
```
# Compare SDRC2.1 Model 1 with SDRC2.2 model
At LSOA level...
```{r compare_v1, fig.cap="Half-hourly mean kWh for model 1 vs observed for each LSOA"}
# plot LSOAs ----
p <- ggplot2::ggplot(mergedLSOA, aes(x = sdrc2.1v1meankWh,
y = sdrc2.2meankWh,
colour = zonecode)) +
geom_abline(intercept = 0, slope = 1) +
geom_point() +
facet_wrap(. ~ peakFlag) +
theme(legend.position = "None") +
labs(x = "mean kWh (Sim: Time Use model v1)",
y = "mean kWh (Sim: Observed kWh model)",
caption = paste0("Southampton: LSOAs\n",
"1:1 line shown"))
#xlim(0,0.8) +
#ylim(0,0.6) +
p
ggplot2::ggsave(paste0(pPath,"lsoaProfiles_Winter-Weekdays-Compare-SDCR2.1_v1_SDRC2.2.png"), p)
peaks <- unique(mergedLSOA$peakFlag)
for(p in peaks){
message("Testing correlation for: ", p)
print(with(mergedLSOA[peakFlag == p], cor.test(sdrc2.1v1meankWh,
sdrc2.2meankWh,
na.action = "na.omit")
))
print(with(mergedLSOA[peakFlag == p], cor.test(sdrc2.1v1meankWh,
sdrc2.2meankWh,
na.action = "na.omit",
method = "spearman")
))
}
```
# Compare SDRC2.1 Model 2 with SDRC2.2 model
At LSOA level...
```{r compare_v2, fig.cap="Half-hourly mean kWh for model 2 vs observed for each LSOA"}
# plot LSOAs ----
p <- ggplot2::ggplot(mergedLSOA, aes(x = sdrc2.1v2meankWh,
y = sdrc2.2meankWh,
colour = zonecode)) +
geom_abline(intercept = 0, slope = 1) +
geom_point() +
facet_wrap(. ~ peakFlag) +
theme(legend.position = "None") +
labs(x = "mean kWh (Sim: Time Use model v2)",
y = "mean kWh (Sim: Observed kWh model)",
caption = paste0("Southampton: LSOAs\n",
"1:1 line shown"))
#xlim(0,0.8) +
#ylim(0,0.6) +
p
ggplot2::ggsave(paste0(pPath,"lsoaProfiles_Winter-Weekdays-Compare-SDCR2.1_v2_SDRC2.2.png"), p)
peaks <- unique(mergedLSOA$peakFlag)
for(p in peaks){
message("Testing correlation for: ", p)
print(with(mergedLSOA[peakFlag == p], cor.test(sdrc2.1v2meankWh,
sdrc2.2meankWh,
na.action = "na.omit")
))
print(with(mergedLSOA[peakFlag == p], cor.test(sdrc2.1v2meankWh,
sdrc2.2meankWh,
na.action = "na.omit",
method = "spearman")
))
}
```
# Compare differences
```{r compare_delta, fig.cap="Half-hourly mean kWh for model 2 vs observed for each LSOA"}
mergedLSOA[, diffv1 := sdrc2.1v1meankWh - sdrc2.2meankWh]
mergedLSOA[, diffv2 := sdrc2.1v2meankWh - sdrc2.2meankWh]
t <- mergedLSOA[, .(meanDelta_v1 = mean(diffv1, na.rm = TRUE),
meanDelta_v2 = mean(diffv2, na.rm = TRUE)), keyby = .(peakFlag)]
kableExtra::kable(t, caption = "Summary of delta (+ve value means TU model higher)") %>%
kable_styling()
t1 <- mergedLSOA[, .(delta = diffv1, halfHour)]
t1[, label := "TU model 1: observed model"]
t2 <- mergedLSOA[, .(delta = diffv2, halfHour)]
t2[, label := "TU model 2 : observed model"]
dt <- rbind(t1,t2)
# plot LSOAs ----
p <- ggplot2::ggplot(dt, aes(x = halfHour, group = halfHour)) +
geom_boxplot(aes(y = delta)) +
facet_grid(label ~ .) +
labs(y = "delta mean kWh",
caption = paste0("Southampton: LSOAs"))
p
ggplot2::ggsave(paste0(pPath,"lsoaProfiles_Winter-Weekdays-Compare-delta_SDCR2.1_SDRC2.2.png"), p)
```
# Input data description
```{r v2.1v1}
skimr::skim(sdrc2.1v1_LSOA_dt)
```
```{r v2.1v2}
skimr::skim(sdrc2.1v2_LSOA_dt)
```
```{r v2.2}
skimr::skim(sdrc2.2_LSOA_dt)
```
# References
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