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MSBA Sustainability Comp (1).Rmd
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---
title: "R Notebook"
output: html_notebook
---
This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code.
Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl+Shift+Enter*.
```{r}
library(fpp2)
library(tidyverse)
library(readxl)
library(forecast)
library(ggplot2)
library(dplyr)
library(reshape2)
library(janitor)
library(tibble)
```
```{r}
#set wd
getwd()
setwd('C:/Users/kesha/Desktop/MSBA Keshav/Case Competitions/MSBA Case Competition')
```
```{r}
#read electricity data for proof of concept section
elec = read_excel('UPC Utility Data 2018-2021.xlsx', sheet="Electricity", range = "A6:AY232")
elec_ts_clean = data.frame(t(elec[c(-1,-224,-225),-1:-2]))
head(elec_ts_clean)
```
```{r}
#make field numeric and only select relevant columns with data
elec_ts_TOT = elec_ts_clean[-1,223]
elec_ts_TOT = as.numeric(elec_ts_TOT)
```
```{r}
# Create a time series object for electricity totals data
elec_ts_TOT.ts = ts(elec_ts_TOT,start=c(2018,1),frequency=12)
```
```{r}
#moving average example of elec trend - proof of concept
elec.trend = ma(elec_ts_TOT.ts, order = 12)
autoplot(elec_ts_TOT.ts) + autolayer(elec.trend) + ylab("Total Elec Usage") +
xlab("Time")
```
```{r}
#Example forecast fit to show COVID sharp adjustment and Trend Ramp
# Divide data into training and testing sets and call y.train and y.test
y.train = window(elec_ts_TOT.ts, start=c(2020,3),end=c(2021,8))
y.train
y.test = window(elec_ts_TOT.ts, start=c(2021,9))
y.test
length(y.test)
# Build a model on training set and predict on the testing set
M1 = auto.arima(y.train,lambda='auto')
M1F = forecast(M1, h = length(y.test))
accuracy(M1F) #MAPE ~47.5
M1
#Plot existing data, fitted values against training window, and forecast for test window
autoplot(elec_ts_TOT.ts,size=1) + geom_point(size=2) + theme_bw() +
autolayer(M1F$fitted,size=2,series="Fitted values") +
autolayer(M1F$mean,size=2,series="Test Forecast")
# Plot all data (train + test) and overlay fitted values and forecasts
autoplot(M1F$residuals)
#autoplot(elec_ts_TOT.ts) + autolayer(M1$fitted) + autolayer(M1$mean,size=2)+
# theme_bw()
```
```{r}
#example linear model showing proof of concept on one building
elec_ts_clean = janitor::row_to_names(elec_ts_clean, row_number=1) %>% clean_names()
elec_ts_clean$adm <- as.numeric(elec_ts_clean$adm)
#create factors
elec_ts_clean = elec_ts_clean %>% mutate(Ramp=c(rep(0,27),1:21))
elec_ts_clean = elec_ts_clean %>% mutate(Bump=c(rep(0,27),rep(1,21)))
elec_ts_clean$Month = 1:(dim(elec_ts_clean)[1])
elec_ts_clean = elec_ts_clean %>% mutate(MonthShort=c('Jan','Feb','March','April','May','June','July','Aug','Sep','Oct','Nov','Dec',
'Jan','Feb','March','April','May','June','July','Aug','Sep','Oct','Nov','Dec',
'Jan','Feb','March','April','May','June','July','Aug','Sep','Oct','Nov','Dec',
'Jan','Feb','March','April','May','June','July','Aug','Sep','Oct','Nov','Dec'))
#create lag1
elec_ts_clean$adm_Lag1 <- elec_ts_clean$adm %>% lag(1)
#M11 creation - example to make loop
xLabels = c('','','','Feb 2020','Feb 2021','')
M11=lm(adm ~ Bump + Ramp + Month + MonthShort + adm_Lag1,data=elec_ts_clean)
elec_ts_clean$M11ADM = c(0,M11$fitted.values)
elec_ts_clean$M11ADM
dim(elec_ts_clean)
elec_ts_clean[2:48,] %>% ggplot(mapping=aes(x=Month,y=adm)) + geom_point() + theme_bw()+
geom_line(aes(x=Month,y=adm)) +
geom_line(mapping=aes(x=Month,y=M11ADM),col="red",lwd=1)+
labs(y= "Electricty Usage (ADM Building)", x = "Month")+
scale_x_continuous(breaks = c(0,10,20,30,40,50),labels = xLabels)
#scale_x_discrete(breaks=c("0.5","1","2"),labels=c("Dose 0.5", "Dose 1", "Dose 2"))
#M11 test creation end - commented out section
summary(M11)
M11$coefficients[3]
```
```{r}
########
# Electricity Section - create output table of means and coefficients
########
elec = read_excel('UPC Utility Data 2018-2021.xlsx', sheet="Electricity", range = "A6:AY232")
elec_clean = data.frame(t(elec[c(-1,-224,-225),-1:-2]))
elec_clean = janitor::row_to_names(elec_clean, row_number=1) %>% clean_names()
elec_clean[1:223]
#create data frame to store results, row names are the bldg codes and coefficients are the post-covid trend coefficient (Ramp)
elec_results = data.frame(colnames(elec_clean[1:223]),coefficient_value=0,row.names = colnames(elec_clean[1:223]))
elec_results = elec_results[-1]
#colnames(elec_results)
elec_results
elec_clean = elec_clean %>% mutate(Ramp=c(rep(0,27),1:21))
elec_clean = elec_clean %>% mutate(Bump=c(rep(0,27),rep(1,21)))
elec_clean$Month = 1:(dim(elec_clean)[1])
elec_clean = elec_clean %>% mutate(MonthShort=c('Jan','Feb','March','April','May','June','July','Aug','Sep','Oct','Nov','Dec',
'Jan','Feb','March','April','May','June','July','Aug','Sep','Oct','Nov','Dec',
'Jan','Feb','March','April','May','June','July','Aug','Sep','Oct','Nov','Dec',
'Jan','Feb','March','April','May','June','July','Aug','Sep','Oct','Nov','Dec'))
#elec_clean$adm_Lag1 <- elec_clean$adm %>% lag(1)
for (i in colnames(elec_clean)[1:223]) {
elec_clean[i] <- as.numeric(unlist(elec_clean[i]))
M_i = lm(unlist(elec_clean[i]) ~ Ramp + Bump + Month + MonthShort,data=elec_clean)
elec_results[i,"coefficient_value"] = M_i$coefficients[3]
elec_results[i,"mean"] = mean(unlist(elec_clean[i]))
}
elec_results
write.csv(elec_results,"electricity_mean_trend.csv")
#write.csv(elec_results,"electricity_mean_trend.csv")
```
```{r}
################
# nat_gas section start
################
nat_gas = read_excel('UPC Utility Data 2018-2021.xlsx', sheet="Natural Gas", range = "A3:AY181")
nat_gas_clean = data.frame(t(nat_gas[c(-179,-180),-1:-2]))
#janitor to get rid of auto generated column names and put proper column names on
nat_gas_clean = janitor::row_to_names(nat_gas_clean, row_number=1) %>% clean_names()
dim(nat_gas_clean)
nat_gas_clean = nat_gas_clean[c(-176,-177,-178)]
nat_gas_clean
nat_gas_clean = nat_gas_clean %>% mutate(Ramp=c(rep(0,27),1:21))
nat_gas_clean = nat_gas_clean %>% mutate(Bump=c(rep(0,27),rep(1,21)))
nat_gas_clean$Month = 1:(dim(nat_gas_clean)[1])
nat_gas_clean = nat_gas_clean %>% mutate(MonthShort=c('Jan','Feb','March','April','May','June','July','Aug','Sep','Oct','Nov','Dec',
'Jan','Feb','March','April','May','June','July','Aug','Sep','Oct','Nov','Dec',
'Jan','Feb','March','April','May','June','July','Aug','Sep','Oct','Nov','Dec',
'Jan','Feb','March','April','May','June','July','Aug','Sep','Oct','Nov','Dec'))
#create data frame to store results, row names are the bldg codes and coefficients are the post-covid trend coefficient (Ramp)
nat_gas_results = data.frame(colnames(nat_gas_clean[1:175]),coefficient_value=0,row.names = colnames(nat_gas_clean[1:175]))
nat_gas_results = nat_gas_results[-1]
#colnames(elec_results)
nat_gas_results
for (i in colnames(nat_gas_clean)[1:175]) {
nat_gas_clean[i] <- as.numeric(unlist(nat_gas_clean[i]))
M_i = lm(unlist(nat_gas_clean[i]) ~ Ramp + Bump + Month + MonthShort,data=nat_gas_clean)
nat_gas_results[i,"coefficient_value"] = M_i$coefficients[3]
nat_gas_results[i,"mean"] = mean(unlist(nat_gas_clean[i]))
}
nat_gas_results
write.csv(nat_gas_results,"nat_gas_mean_trend.csv")
###############################
# Nat Gas Section End
###############################
```
```{r}
################
# water section start
################
water = read_excel('UPC Utility Data 2018-2021.xlsx', sheet="Water", range = "A5:AY193") #this read cuts off the totals row which we don't happen to use and two houses without acronyms
water_clean = data.frame(t(water[c(-1),c(-1,-3)]))
head(water_clean)
dim(water_clean)
water_clean[1:187]
#janitor to get rid of auto generated column names and put proper column names on
water_clean = janitor::row_to_names(water_clean, row_number=1) %>% clean_names()
dim(water_clean)
#nat_gas_clean = nat_gas_clean[c(-176,-177,-178)]
#nat_gas_clean
water_clean = water_clean %>% mutate(Ramp=c(rep(0,27),1:21))
water_clean = water_clean %>% mutate(Bump=c(rep(0,27),rep(1,21)))
water_clean$Month = 1:(dim(water_clean)[1])
water_clean = water_clean %>% mutate(MonthShort=c('Jan','Feb','March','April','May','June','July','Aug','Sep','Oct','Nov','Dec',
'Jan','Feb','March','April','May','June','July','Aug','Sep','Oct','Nov','Dec',
'Jan','Feb','March','April','May','June','July','Aug','Sep','Oct','Nov','Dec',
'Jan','Feb','March','April','May','June','July','Aug','Sep','Oct','Nov','Dec'))
#create data frame to store results, row names are the bldg codes and coefficients are the post-covid trend coefficient (Ramp)
water_results = data.frame(colnames(water_clean[1:187]),coefficient_value=0,row.names = colnames(water_clean[1:187])) #187 columns to avoid selecting factor columns
water_results = water_results[-1]
#colnames(elec_results)
water_results
for (i in colnames(water_clean)[1:187]) {
water_clean[i] <- as.numeric(unlist(water_clean[i]))
M_i = lm(unlist(water_clean[i]) ~ Ramp + Bump + Month + MonthShort,data=water_clean)
water_results[i,"coefficient_value"] = M_i$coefficients[3]
water_results[i,"mean"] = mean(unlist(water_clean[i]))
}
water_results
write.csv(water_results,"water_mean_trend.csv")
###############################
# Water Section End
###############################
```
```{r}
###############################
# Chilled Water Section Start
###############################
chill_water = read_excel('UPC Utility Data 2018-2021.xlsx', sheet="Chilled Water", range = "A5:AX76")
chill_water_clean = data.frame(t(chill_water[c(-69,-70,-71),c(-1)]))
head(chill_water_clean)
dim(chill_water_clean)
chill_water_clean
#chill_water_clean = chill_water_clean[,-24] #drop empty irc_k column with too many NAs
sum(is.na(chill_water_clean))
#chill_water_clean[!complete.cases(chill_water_clean), ]
#janitor to get rid of auto generated column names and put proper column names on
chill_water_clean = janitor::row_to_names(chill_water_clean, row_number=1) %>% clean_names()
dim(chill_water_clean) #should be 48 rows
#nat_gas_clean = nat_gas_clean[c(-176,-177,-178)]
#nat_gas_clean
chill_water_clean = chill_water_clean %>% mutate(Ramp=c(rep(0,27),1:21))
chill_water_clean = chill_water_clean %>% mutate(Bump=c(rep(0,27),rep(1,21)))
chill_water_clean$Month = 1:(dim(chill_water_clean)[1])
chill_water_clean = chill_water_clean %>% mutate(MonthShort=c('Jan','Feb','March','April','May','June','July','Aug','Sep','Oct','Nov','Dec',
'Jan','Feb','March','April','May','June','July','Aug','Sep','Oct','Nov','Dec',
'Jan','Feb','March','April','May','June','July','Aug','Sep','Oct','Nov','Dec',
'Jan','Feb','March','April','May','June','July','Aug','Sep','Oct','Nov','Dec'))
#create data frame to store results, row names are the bldg codes and coefficients are the post-covid trend coefficient (Ramp)
chill_water_results = data.frame(colnames(chill_water_clean[1:67]),coefficient_value=0,row.names = colnames(chill_water_clean[1:67])) #67 to avoid selecting factor columns
chill_water_results = chill_water_results[-1]
#colnames(elec_results)
chill_water_results
colnames(chill_water_clean)
for (i in colnames(chill_water_clean[1:67])){ #1:67 to avoid selecting factor columns
chill_water_clean[i] <- as.numeric(unlist(chill_water_clean[i]))
M_i = lm(unlist(chill_water_clean[i]) ~ Ramp + Bump + Month + MonthShort,data=chill_water_clean)
chill_water_results[i,"coefficient_value"] = M_i$coefficients[3]
chill_water_results[i,"mean"] = mean(unlist(chill_water_clean[i]))
}
chill_water_results
write.csv(water_results,"chill_water_mean_trend.csv")
###############################
# Chilled Water Section End
###############################
```