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main_preprocessed_data.R
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# Paty, May 2017.
# Reserve's project (Claire Tomlin and Duncan Callaway)
#
# Description: use machine learning (regression) to identify potential features that can predict ramping (reserves) needs
# Potential features: amount of wind and solar in each hour of day, day of month, subarea of transmission network, load,
# previous three hours of wind and solar
# Training set: half? of days available in 2016, Test prediction of ramp (reserves) needed
# Data: real time dispatch per hour for 2016 http://oasis.caiso.com
rm(list = ls())
#setwd("C:/CAISO_OASIS/wind_solar_relational")
setwd("/Users/paty/Documents/Research/Load_following/CAISO_OASIS")
library("ggplot2")
library(data.table)
library(gbm)
source("filter_and_check.R")
source("process_load.R")
source("calculate_ramps.R")
source("data_cleaning.R")
# wind+solar data import
wind_solar_01_2016 <- read.csv("./wind_solar_relational/01_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
wind_solar_02_2016 <- read.csv("./wind_solar_relational/02_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
wind_solar_03_2016 <- read.csv("./wind_solar_relational/03_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
wind_solar_04_2016 <- read.csv("./wind_solar_relational/04_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
wind_solar_05_2016 <- read.csv("./wind_solar_relational/05_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
wind_solar_06_2016 <- read.csv("./wind_solar_relational/06_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
wind_solar_07_2016 <- read.csv("./wind_solar_relational/07_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
wind_solar_08_2016 <- read.csv("./wind_solar_relational/08_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
wind_solar_09_2016 <- read.csv("./wind_solar_relational/09_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
wind_solar_10_2016 <- read.csv("./wind_solar_relational/10_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
wind_solar_11_2016 <- read.csv("./wind_solar_relational/11_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
wind_solar_12_2016 <- read.csv("./wind_solar_relational/12_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
# wind+solar data processing
rtm_wind_solar_01_2016 <- filter_and_check(wind_solar_01_2016)
rtm_wind_solar_02_2016 <- filter_and_check(wind_solar_02_2016)
rtm_wind_solar_03_2016 <- filter_and_check(wind_solar_03_2016)
rtm_wind_solar_04_2016 <- filter_and_check(wind_solar_04_2016)
rtm_wind_solar_05_2016 <- filter_and_check(wind_solar_05_2016)
rtm_wind_solar_06_2016 <- filter_and_check(wind_solar_06_2016)
rtm_wind_solar_07_2016 <- filter_and_check(wind_solar_07_2016)
rtm_wind_solar_08_2016 <- filter_and_check(wind_solar_08_2016)
rtm_wind_solar_09_2016 <- filter_and_check(wind_solar_09_2016)
rtm_wind_solar_10_2016 <- filter_and_check(wind_solar_10_2016)
rtm_wind_solar_11_2016 <- filter_and_check(wind_solar_11_2016)
rtm_wind_solar_12_2016 <- filter_and_check(wind_solar_12_2016)
# load data import
gen_01_2016 <- read.csv("./generation/01_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
gen_02_2016 <- read.csv("./generation/02_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
gen_03_2016 <- read.csv("./generation/03_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
gen_04_2016 <- read.csv("./generation/04_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
gen_05_2016 <- read.csv("./generation/05_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
gen_06_2016 <- read.csv("./generation/06_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
gen_07_2016 <- read.csv("./generation/07_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
gen_08_2016 <- read.csv("./generation/08_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
gen_09_2016 <- read.csv("./generation/09_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
gen_10_2016 <- read.csv("./generation/10_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
gen_11_2016 <- read.csv("./generation/11_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
gen_12_2016 <- read.csv("./generation/12_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
# process load
net_load_hourly_01_2016 <- process_load(gen_01_2016)
net_load_hourly_02_2016 <- process_load(gen_02_2016)
net_load_hourly_03_2016 <- process_load(gen_03_2016)
net_load_hourly_04_2016 <- process_load(gen_04_2016)
net_load_hourly_05_2016 <- process_load(gen_05_2016)
net_load_hourly_06_2016 <- process_load(gen_06_2016)
net_load_hourly_07_2016 <- process_load(gen_07_2016)
net_load_hourly_08_2016 <- process_load(gen_08_2016)
net_load_hourly_09_2016 <- process_load(gen_09_2016)
net_load_hourly_10_2016 <- process_load(gen_10_2016)
net_load_hourly_11_2016 <- process_load(gen_11_2016)
net_load_hourly_12_2016 <- process_load(gen_12_2016)
# merge load and wind+solar data
hourly_01_2016 <- merge(net_load_hourly_01_2016, rtm_wind_solar_01_2016, by=c('year', 'month', 'day', 'hour'))
hourly_02_2016 <- merge(net_load_hourly_02_2016, rtm_wind_solar_02_2016, by=c('year', 'month', 'day', 'hour'))
hourly_03_2016 <- merge(net_load_hourly_03_2016, rtm_wind_solar_03_2016, by=c('year', 'month', 'day', 'hour'))
hourly_04_2016 <- merge(net_load_hourly_04_2016, rtm_wind_solar_04_2016, by=c('year', 'month', 'day', 'hour'))
hourly_05_2016 <- merge(net_load_hourly_05_2016, rtm_wind_solar_05_2016, by=c('year', 'month', 'day', 'hour'))
hourly_06_2016 <- merge(net_load_hourly_06_2016, rtm_wind_solar_06_2016, by=c('year', 'month', 'day', 'hour'))
hourly_07_2016 <- merge(net_load_hourly_07_2016, rtm_wind_solar_07_2016, by=c('year', 'month', 'day', 'hour'))
hourly_08_2016 <- merge(net_load_hourly_08_2016, rtm_wind_solar_08_2016, by=c('year', 'month', 'day', 'hour'))
hourly_09_2016 <- merge(net_load_hourly_09_2016, rtm_wind_solar_09_2016, by=c('year', 'month', 'day', 'hour'))
hourly_10_2016 <- merge(net_load_hourly_10_2016, rtm_wind_solar_10_2016, by=c('year', 'month', 'day', 'hour'))
hourly_11_2016 <- merge(net_load_hourly_11_2016, rtm_wind_solar_11_2016, by=c('year', 'month', 'day', 'hour'))
hourly_12_2016 <- merge(net_load_hourly_12_2016, rtm_wind_solar_12_2016, by=c('year', 'month', 'day', 'hour'))
# append all months to have only one set for the year
hourly_2016 <- rbind(hourly_01_2016,
hourly_02_2016,
hourly_03_2016,
hourly_04_2016,
hourly_05_2016,
hourly_06_2016,
hourly_07_2016,
hourly_08_2016,
hourly_09_2016,
hourly_10_2016,
hourly_11_2016,
hourly_12_2016)
# order by year, month, day, and hour
hourly_2016<- hourly_2016[order(hourly_2016$year,
hourly_2016$month,
hourly_2016$day,
hourly_2016$hour),]
# calculatin load and wind+solar ramps
hourly_2016 <- calculate_ramps(hourly_2016)
# data cleaning (replacing defective data for the average of the ramp from t-1 and t+1)
source("data_cleaning.R")
source("data_cleaning_2.R")
hourly_2016 <- data_cleaning(hourly_2016)
hourly_2016 <- data_cleaning_2(hourly_2016)
rownames(hourly_2016) <- NULL # renumbering rows
# dropping the last rows by hand:
hourly_2016 <- hourly_2016[-c(6629, 6629+1, 6629+2, 6629+3, 6629+4, 6629+5), ]
rownames(hourly_2016) <- NULL # renumbering rows
# picking an arbitrary training set
training_2016 <- subset(hourly_2016, day==4 | day==5 | day==6 | day==7 | day==8 | day==9 | day==10)
test_set_2016 <- subset(hourly_2016, day!=4 & day!=5 & day!=6 & day!=7 & day!=8 & day!=9 & day!=10)
# Ordinary least Squares #######################################################################################
# [Notes] Should we scale and center the data? scale(cars$speed, center=TRUE, scale=FALSE)
# OLS with hour, month and weekday as factors (dummy variables)
# OLS with 3 weeks or training set and 1 week for predictions (per month for 2016)
# picking an arbitrary training set
test_set_2016_v2 <- subset(hourly_2016, day==4 | day==5 | day==6 | day==7 | day==8 | day==9 | day==10)
training_set_2016_v2 <- subset(hourly_2016, day!=4 & day!=5 & day!=6 & day!=7 & day!=8 & day!=9 & day!=10)
# OLS with 6 hours of past features ##################################################################################
ols_2016_discrete_v3 <- lm(formula = duck_ramp_t_MWh ~ factor(hour)
+ factor(weekday)
+ factor(month)
+ net_load_t_minus_1_MWh
+ net_load_t_minus_2_MWh
+ net_load_t_minus_3_MWh
+ net_load_t_minus_4_MWh
+ net_load_t_minus_5_MWh
+ net_load_t_minus_6_MWh
+ wind_solar_t_minus_1_MWh
+ wind_solar_t_minus_2_MWh
+ wind_solar_t_minus_3_MWh
+ wind_solar_t_minus_4_MWh
+ wind_solar_t_minus_5_MWh
+ wind_solar_t_minus_6_MWh,
data = training_set_2016_v2
)
summary(ols_2016_discrete_v3)
test_set_2016_v2$ramp_prediction_dummy_6hrs <- predict(ols_2016_discrete_v3, test_set_2016_v2)
plot_this <- subset(test_set_2016_v2, select=c('timestamp.x',
'duck_ramp_t_MWh',
'ramp_prediction_dummy_6hrs'))
#write.csv(plot_this, "OLS_duck_ramp.csv")
# Lasso (1-norm) ######################################################################################################
# install.packages("glmnet", repos = "http://cran.us.r-project.org")
library(glmnet)
# creating dataframe for function glmnet:
hourly_2016_dummies <- subset(hourly_2016, select=c('month', 'day', 'hour', 'weekday',
'net_load_MWh',
'duck_ramp_t_MWh',
'net_load_t_minus_1_MWh',
'net_load_t_minus_2_MWh',
'net_load_t_minus_3_MWh',
'net_load_t_minus_4_MWh',
'net_load_t_minus_5_MWh',
'net_load_t_minus_6_MWh',
'wind_solar_t_minus_1_MWh',
'wind_solar_t_minus_2_MWh',
'wind_solar_t_minus_3_MWh',
'wind_solar_t_minus_4_MWh',
'wind_solar_t_minus_5_MWh',
'wind_solar_t_minus_6_MWh'))
#continue here
hourly_2016_dummies <- data.frame(
factor(hourly_2016_dummies$month),
factor(hourly_2016_dummies$hour),
factor(hourly_2016_dummies$weekday),
hourly_2016_dummies$day,
hourly_2016_dummies$duck_ramp_t_MWh,
hourly_2016_dummies$net_load_t_minus_1_MWh,
hourly_2016_dummies$net_load_t_minus_2_MWh,
hourly_2016_dummies$net_load_t_minus_3_MWh,
hourly_2016_dummies$net_load_t_minus_4_MWh,
hourly_2016_dummies$net_load_t_minus_5_MWh,
hourly_2016_dummies$net_load_t_minus_6_MWh,
hourly_2016_dummies$wind_solar_t_minus_1_MWh,
hourly_2016_dummies$wind_solar_t_minus_2_MWh,
hourly_2016_dummies$wind_solar_t_minus_3_MWh,
hourly_2016_dummies$wind_solar_t_minus_4_MWh,
hourly_2016_dummies$wind_solar_t_minus_5_MWh,
hourly_2016_dummies$wind_solar_t_minus_6_MWh
)
names(hourly_2016_dummies)[1]<-'month'
names(hourly_2016_dummies)[2]<-'hour'
names(hourly_2016_dummies)[3]<-'weekday'
names(hourly_2016_dummies)[4]<-'day'
names(hourly_2016_dummies)[5]<-'duck_ramp_t_MWh'
names(hourly_2016_dummies)[6]<-'net_load_t_minus_1_MWh'
names(hourly_2016_dummies)[7]<-'net_load_t_minus_2_MWh'
names(hourly_2016_dummies)[8]<-'net_load_t_minus_3_MWh'
names(hourly_2016_dummies)[9]<-'net_load_t_minus_4_MWh'
names(hourly_2016_dummies)[10]<-'net_load_t_minus_5_MWh'
names(hourly_2016_dummies)[11]<-'net_load_t_minus_6_MWh'
names(hourly_2016_dummies)[12]<-'wind_solar_t_minus_1_MWh'
names(hourly_2016_dummies)[13]<-'wind_solar_t_minus_2_MWh'
names(hourly_2016_dummies)[14]<-'wind_solar_t_minus_3_MWh'
names(hourly_2016_dummies)[15]<-'wind_solar_t_minus_4_MWh'
names(hourly_2016_dummies)[16]<-'wind_solar_t_minus_5_MWh'
names(hourly_2016_dummies)[17]<-'wind_solar_t_minus_6_MWh'
# picking an arbitrary training set
test_set <- subset(hourly_2016_dummies, day==4 | day==5 | day==6 | day==7 | day==8 | day==9 | day==10)
training <- subset(hourly_2016_dummies, day!=4 & day!=5 & day!=6 & day!=7 & day!=8 & day!=9 & day!=10)
training <- model.matrix( ~ .-1, training)
test_set <- model.matrix( ~ .-1, test_set)
# Lasso and Ridge cross validated ############
# Lasso
lasso_2016 = cv.glmnet(training[,c("month1",
"month2",
"month3",
"month4",
"month5",
"month6",
"month7",
"month8",
"month9",
"month10",
"month11",
"month12",
"hour1", "hour2", "hour3", "hour4", "hour5",
"hour6", "hour7", "hour8", "hour9", "hour10", "hour11", "hour12",
"hour13", "hour14", "hour15", "hour16", "hour17", "hour18", "hour19",
"hour20", "hour21", "hour22", "hour23",
"weekdayMonday", "weekdaySaturday", "weekdaySunday", "weekdayThursday",
"weekdayTuesday","weekdayWednesday",
"net_load_t_minus_1_MWh", "net_load_t_minus_2_MWh", "net_load_t_minus_3_MWh",
"net_load_t_minus_4_MWh", "net_load_t_minus_5_MWh", "net_load_t_minus_6_MWh",
"wind_solar_t_minus_1_MWh", "wind_solar_t_minus_2_MWh", "wind_solar_t_minus_3_MWh",
"wind_solar_t_minus_4_MWh", "wind_solar_t_minus_5_MWh", "wind_solar_t_minus_6_MWh")],
training[,c("duck_ramp_t_MWh")], alpha=1)
# good figure on selection of lambda:
plot(lasso_2016)
lasso_2016$lambda.min
# lasso prediction
prediction_lasso <- predict(lasso_2016, test_set[,c("month1",
"month2",
"month3",
"month4",
"month5",
"month6",
"month7",
"month8",
"month9",
"month10",
"month11",
"month12",
"hour1", "hour2", "hour3", "hour4", "hour5",
"hour6", "hour7", "hour8", "hour9", "hour10", "hour11", "hour12",
"hour13", "hour14", "hour15", "hour16", "hour17", "hour18", "hour19",
"hour20", "hour21", "hour22", "hour23",
"weekdayMonday", "weekdaySaturday", "weekdaySunday", "weekdayThursday",
"weekdayTuesday","weekdayWednesday",
"net_load_t_minus_1_MWh", "net_load_t_minus_2_MWh", "net_load_t_minus_3_MWh",
"net_load_t_minus_4_MWh", "net_load_t_minus_5_MWh", "net_load_t_minus_6_MWh",
"wind_solar_t_minus_1_MWh", "wind_solar_t_minus_2_MWh", "wind_solar_t_minus_3_MWh",
"wind_solar_t_minus_4_MWh", "wind_solar_t_minus_5_MWh", "wind_solar_t_minus_6_MWh")],
s="lambda.min")
# Ridge
ridge_2016 = cv.glmnet(training[,c("month1",
"month2",
"month3",
"month4",
"month5",
"month6",
"month7",
"month8",
"month9",
"month10",
"month11",
"month12",
"hour1", "hour2", "hour3", "hour4", "hour5",
"hour6", "hour7", "hour8", "hour9", "hour10", "hour11", "hour12",
"hour13", "hour14", "hour15", "hour16", "hour17", "hour18", "hour19",
"hour20", "hour21", "hour22", "hour23",
"weekdayMonday", "weekdaySaturday", "weekdaySunday", "weekdayThursday",
"weekdayTuesday","weekdayWednesday",
"net_load_t_minus_1_MWh", "net_load_t_minus_2_MWh", "net_load_t_minus_3_MWh",
"net_load_t_minus_4_MWh", "net_load_t_minus_5_MWh", "net_load_t_minus_6_MWh",
"wind_solar_t_minus_1_MWh", "wind_solar_t_minus_2_MWh", "wind_solar_t_minus_3_MWh",
"wind_solar_t_minus_4_MWh", "wind_solar_t_minus_5_MWh", "wind_solar_t_minus_6_MWh")],
training[,c("duck_ramp_t_MWh")], alpha=0)
plot(ridge_2016)
# ridge prediction
prediction_ridge <- predict(ridge_2016, test_set[,c("month1",
"month2",
"month3",
"month4",
"month5",
"month6",
"month7",
"month8",
"month9",
"month10",
"month11",
"month12",
"hour1", "hour2", "hour3", "hour4", "hour5",
"hour6", "hour7", "hour8", "hour9", "hour10", "hour11", "hour12",
"hour13", "hour14", "hour15", "hour16", "hour17", "hour18", "hour19",
"hour20", "hour21", "hour22", "hour23",
"weekdayMonday", "weekdaySaturday", "weekdaySunday", "weekdayThursday",
"weekdayTuesday","weekdayWednesday",
"net_load_t_minus_1_MWh", "net_load_t_minus_2_MWh", "net_load_t_minus_3_MWh",
"net_load_t_minus_4_MWh", "net_load_t_minus_5_MWh", "net_load_t_minus_6_MWh",
"wind_solar_t_minus_1_MWh", "wind_solar_t_minus_2_MWh", "wind_solar_t_minus_3_MWh",
"wind_solar_t_minus_4_MWh", "wind_solar_t_minus_5_MWh", "wind_solar_t_minus_6_MWh")],
s="lambda.min")
# Least Squares (lambda=0)
least_2016 = cv.glmnet(training[,c("month1",
"month2",
"month3",
"month4",
"month5",
"month6",
"month7",
"month8",
"month9",
"month10",
"month11",
"month12",
"hour1", "hour2", "hour3", "hour4", "hour5",
"hour6", "hour7", "hour8", "hour9", "hour10", "hour11", "hour12",
"hour13", "hour14", "hour15", "hour16", "hour17", "hour18", "hour19",
"hour20", "hour21", "hour22", "hour23",
"weekdayMonday", "weekdaySaturday", "weekdaySunday", "weekdayThursday",
"weekdayTuesday","weekdayWednesday",
"net_load_t_minus_1_MWh", "net_load_t_minus_2_MWh", "net_load_t_minus_3_MWh",
"net_load_t_minus_4_MWh", "net_load_t_minus_5_MWh", "net_load_t_minus_6_MWh",
"wind_solar_t_minus_1_MWh", "wind_solar_t_minus_2_MWh", "wind_solar_t_minus_3_MWh",
"wind_solar_t_minus_4_MWh", "wind_solar_t_minus_5_MWh", "wind_solar_t_minus_6_MWh")],
training[,c("duck_ramp_t_MWh")], alpha=0)
plot(least_2016)
# least prediction
prediction_least <- predict(least_2016, test_set[,c("month1",
"month2",
"month3",
"month4",
"month5",
"month6",
"month7",
"month8",
"month9",
"month10",
"month11",
"month12",
"hour1", "hour2", "hour3", "hour4", "hour5",
"hour6", "hour7", "hour8", "hour9", "hour10", "hour11", "hour12",
"hour13", "hour14", "hour15", "hour16", "hour17", "hour18", "hour19",
"hour20", "hour21", "hour22", "hour23",
"weekdayMonday", "weekdaySaturday", "weekdaySunday", "weekdayThursday",
"weekdayTuesday","weekdayWednesday",
"net_load_t_minus_1_MWh", "net_load_t_minus_2_MWh", "net_load_t_minus_3_MWh",
"net_load_t_minus_4_MWh", "net_load_t_minus_5_MWh", "net_load_t_minus_6_MWh",
"wind_solar_t_minus_1_MWh", "wind_solar_t_minus_2_MWh", "wind_solar_t_minus_3_MWh",
"wind_solar_t_minus_4_MWh", "wind_solar_t_minus_5_MWh", "wind_solar_t_minus_6_MWh")],
s=0)
# Save to csv to plot ####################
plot_this <- cbind(prediction_ridge, prediction_lasso, prediction_least, test_set[,c("duck_ramp_t_MWh")])
names(plot_this)[1] <- 'ridge'
names(plot_this)[2] <- "lasso"
names(plot_this)[3] <- "least squares"
names(plot_this)[4] <- "actual_ramp"
#write.csv(plot_this, "duck_filtered_ridge_lasso_leastsquares_optimal_lambda_v2.csv")
# for another time: compare least squares from glmnet() and lm()
# Calculating prediction errors (in MWh) for ridge, lasso and OLS #####
test_set_with_errors<- cbind(test_set, prediction_ridge, prediction_lasso, prediction_least)
test_set_with_errors <- as.data.frame(test_set_with_errors)
names(test_set_with_errors)[56] <- "prediction_ridge"
names(test_set_with_errors)[57] <- "prediction_lasso"
names(test_set_with_errors)[58] <- "prediction_least"
test_set_with_errors$Ridge <- (abs(test_set_with_errors$prediction_ridge) - abs(test_set_with_errors$duck_ramp_t_MWh))
test_set_with_errors$Lasso <- (abs(test_set_with_errors$prediction_lasso) - abs(test_set_with_errors$duck_ramp_t_MWh))
test_set_with_errors$OLS <- (abs(test_set_with_errors$prediction_least) - abs(test_set_with_errors$duck_ramp_t_MWh))
#calculating max (caiso's benchmark)
benchmark <- as.data.frame(training)
benchmark_month1 <- max(abs(benchmark[which(benchmark$month1==1),c("duck_ramp_t_MWh")]))
benchmark_month2 <- max(abs(benchmark[which(benchmark$month2==1),c("duck_ramp_t_MWh")]))
benchmark_month3 <- max(abs(benchmark[which(benchmark$month3==1),c("duck_ramp_t_MWh")]))
benchmark_month4 <- max(abs(benchmark[which(benchmark$month4==1),c("duck_ramp_t_MWh")]))
benchmark_month5 <- max(abs(benchmark[which(benchmark$month5==1),c("duck_ramp_t_MWh")]))
benchmark_month6 <- max(abs(benchmark[which(benchmark$month6==1),c("duck_ramp_t_MWh")]))
benchmark_month7 <- max(abs(benchmark[which(benchmark$month7==1),c("duck_ramp_t_MWh")]))
benchmark_month8 <- max(abs(benchmark[which(benchmark$month8==1),c("duck_ramp_t_MWh")]))
benchmark_month9 <- max(abs(benchmark[which(benchmark$month9==1),c("duck_ramp_t_MWh")]))
benchmark_month10 <- max(abs(benchmark[which(benchmark$month10==1),c("duck_ramp_t_MWh")]))
benchmark_month11 <- max(abs(benchmark[which(benchmark$month11==1),c("duck_ramp_t_MWh")]))
benchmark_month12 <- max(abs(benchmark[which(benchmark$month12==1),c("duck_ramp_t_MWh")]))
#calulating benchmark's error
test_set_with_errors$prediction_benchmark <- ifelse(test_set_with_errors$month1 == 1, benchmark_month1,
ifelse(test_set_with_errors$month2 == 1, benchmark_month2,
ifelse(test_set_with_errors$month3 == 1, benchmark_month3,
ifelse(test_set_with_errors$month4 == 1, benchmark_month4,
ifelse(test_set_with_errors$month5 == 1, benchmark_month5,
ifelse(test_set_with_errors$month6 == 1, benchmark_month6,
ifelse(test_set_with_errors$month7 == 1, benchmark_month7,
ifelse(test_set_with_errors$month8 == 1, benchmark_month8,
ifelse(test_set_with_errors$month9 == 1, benchmark_month9,
ifelse(test_set_with_errors$month10 == 1, benchmark_month10,
ifelse(test_set_with_errors$month11 == 1, benchmark_month11,
ifelse(test_set_with_errors$month12 == 1, benchmark_month12,0))))))))))))
test_set_with_errors$Benchmark <- (abs(test_set_with_errors$prediction_benchmark) - abs(test_set_with_errors$duck_ramp_t_MWh))
library(extrafont)
ggplot(stack(test_set_with_errors[,c("Ridge", "Lasso", "OLS", "Benchmark")]), aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10)
#savingthe figure
ggsave("fig_1.png", dpi=300, dev='png', height=3, width=4, units="in") # 2 2, 4 5, 3 4
# DECISION TREE (BOOSTED) #############################################################################################
# for reproducability:
set.seed(1)
# decision tree with cross validation #############
# data sets have to be dataframes
training_dt <- as.data.frame(training)
test_set_dt <- as.data.frame(test_set)
decision_tree_cv = gbm(formula = duck_ramp_t_MWh ~ factor(month1) + factor(month2) + factor(month3)
+ factor(month4) + factor(month5) + factor(month6)
+ factor(month7) + factor(month8) + factor(month9) + factor(month10) + factor(month11) + factor(month12)
+ factor(hour1) + factor(hour2) + factor(hour3) + factor(hour4) + factor(hour5) + factor(hour6)
+ factor(hour7) + factor(hour8) + factor(hour9) + factor(hour10) + factor(hour11) + factor(hour12)
+ factor(hour13) + factor(hour14) + factor(hour15) + factor(hour16) + factor(hour17) + factor(hour18)
+ factor(hour19) + factor(hour20) + factor(hour21) + factor(hour22) + factor(hour23)
+ factor(weekdayMonday) + factor(weekdaySaturday) + factor(weekdaySunday) + factor(weekdayThursday)
+ factor(weekdayTuesday) + factor(weekdayWednesday)
+ net_load_t_minus_1_MWh + net_load_t_minus_2_MWh + net_load_t_minus_3_MWh
+ net_load_t_minus_4_MWh + net_load_t_minus_5_MWh + net_load_t_minus_6_MWh
+ wind_solar_t_minus_1_MWh + wind_solar_t_minus_2_MWh + wind_solar_t_minus_3_MWh
+ wind_solar_t_minus_4_MWh + wind_solar_t_minus_5_MWh + wind_solar_t_minus_6_MWh,
# distribution = "bernoulli",
data = training_dt,
n.trees = 4000, #4000 was good #3000 #2000
shrinkage = .1,
n.minobsinnode = 20, # 200
cv.folds = 5,
n.cores = 1)
# optimal number of trees
bestTreeForPrediction = gbm.perf(decision_tree_cv)
decision_tree_cv_optimal = gbm(formula = duck_ramp_t_MWh ~ factor(month1) + factor(month2) + factor(month3)
+ factor(month4) + factor(month5) + factor(month6)
+ factor(month7) + factor(month8) + factor(month9) + factor(month10) + factor(month11) + factor(month12)
+ factor(hour1) + factor(hour2) + factor(hour3) + factor(hour4) + factor(hour5) + factor(hour6)
+ factor(hour7) + factor(hour8) + factor(hour9) + factor(hour10) + factor(hour11) + factor(hour12)
+ factor(hour13) + factor(hour14) + factor(hour15) + factor(hour16) + factor(hour17) + factor(hour18)
+ factor(hour19) + factor(hour20) + factor(hour21) + factor(hour22) + factor(hour23)
+ factor(weekdayMonday) + factor(weekdaySaturday) + factor(weekdaySunday) + factor(weekdayThursday)
+ factor(weekdayTuesday) + factor(weekdayWednesday)
+ net_load_t_minus_1_MWh + net_load_t_minus_2_MWh + net_load_t_minus_3_MWh
+ net_load_t_minus_4_MWh + net_load_t_minus_5_MWh + net_load_t_minus_6_MWh
+ wind_solar_t_minus_1_MWh + wind_solar_t_minus_2_MWh + wind_solar_t_minus_3_MWh
+ wind_solar_t_minus_4_MWh + wind_solar_t_minus_5_MWh + wind_solar_t_minus_6_MWh,
# distribution = "bernoulli",
data = training_dt,
n.trees = bestTreeForPrediction,
shrinkage = .1, # (tree in the expansion) Also known as the learning rate or step-size reduction.
n.minobsinnode = 20, #200 # minimum number of observation per node
cv.folds = 5,
n.cores = 1)
# prediction
test_set_dt$prediction_decision_tree <- predict(object = decision_tree_cv_optimal,
newdata = subset(test_set_dt,
select = c(month1, month2, month3, month4, month5, month6, month7,
month8, month9, month10, month11, month12,
hour1, hour2, hour3, hour4, hour5, hour6, hour7, hour8, hour9,
hour10, hour11, hour12, hour13, hour14, hour15, hour16, hour17,
hour18, hour19, hour20, hour21, hour22, hour23,
weekdayMonday, weekdaySaturday, weekdaySunday, weekdayThursday,
weekdayTuesday, weekdayWednesday,
net_load_t_minus_1_MWh, net_load_t_minus_2_MWh,
net_load_t_minus_3_MWh, net_load_t_minus_4_MWh,
net_load_t_minus_5_MWh, net_load_t_minus_6_MWh,
wind_solar_t_minus_1_MWh, wind_solar_t_minus_2_MWh,
wind_solar_t_minus_3_MWh, wind_solar_t_minus_4_MWh,
wind_solar_t_minus_5_MWh, wind_solar_t_minus_6_MWh)),
n.trees = bestTreeForPrediction,
type = "response")
plot_this <- subset(test_set_dt, select=c('duck_ramp_t_MWh', 'prediction_decision_tree'))
#write.csv(plot_this, "duck_filtered_decision_tree.csv")
# Calculating prediction errors for decision tree ####
test_set_dt$error_tree <- abs(test_set_dt$prediction_decision_tree) - abs(test_set_dt$duck_ramp_t_MWh)
test_set_with_errors$prediction_decision_tree <- test_set_dt$prediction_decision_tree
test_set_with_errors$DT <- (abs(test_set_dt$prediction_decision_tree) - abs(test_set_with_errors$duck_ramp_t_MWh))
# Boxplot for all ML algorithms ##################################################################
# Distributions of errors ######
ggplot(stack(test_set_with_errors[,c("Ridge", "Lasso", "OLS", "DT","Benchmark")]), aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Algorithm") + labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000))
#savingthe figure
ggsave("fig_errors.png", dpi=300, dev='png', height=3, width=4, units="in") # 2 2, 4 5, 3 4
# Table for mean abs(error) and standard dev. #########
mean_error_ridge <- mean(abs(test_set_with_errors$Ridge))
mean_error_lasso <- mean(abs(test_set_with_errors$Lasso))
mean_error_OLS <- mean(abs(test_set_with_errors$OLS))
mean_error_DT <- mean(abs(test_set_with_errors$DT))
mean_error_benchmark <- mean(abs(test_set_with_errors$Benchmark))
sd_error_ridge <- sd(abs(test_set_with_errors$Ridge))
sd_error_lasso <- sd(abs(test_set_with_errors$Lasso))
sd_error_OLS <- sd(abs(test_set_with_errors$OLS))
sd_error_DT <- sd(abs(test_set_with_errors$DT))
sd_error_benchmark <- sd(abs(test_set_with_errors$Benchmark))
# Distributions of predicted ramps #######
plot_ramps <- test_set_with_errors
plot_ramps$Ridge <- NULL
plot_ramps$Lasso <- NULL
plot_ramps$OLS <- NULL
plot_ramps$DT <- NULL
plot_ramps$Benchmark <- NULL
names(plot_ramps)[56] <- "Ridge"
names(plot_ramps)[57] <- "Lasso"
names(plot_ramps)[58] <- "OLS"
names(plot_ramps)[59] <- "Benchmark"
names(plot_ramps)[60] <- "DT"
ggplot(stack(plot_ramps[,c("Ridge", "Lasso", "OLS", "DT","Benchmark")]), aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Algorithm") + labs(y = "Prediction (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000))
#savingthe figure
ggsave("fig_predicted_ramps.png", dpi=300, dev='png', height=3, width=4, units="in") # 2 2, 4 5, 3 4
# same plot in .tiff
tiff("fig_predicted_ramps_tiff.tiff", width = 3, height = 3, units = 'in', res = 300)
ggplot(stack(plot_ramps[,c("Ridge", "Lasso", "OLS", "DT","Benchmark")]), aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Algorithm") + labs(y = "Prediction (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000))
dev.off()
# Distributions of actual ramps in test set #######
ggplot(data=plot_ramps, aes(plot_ramps$duck_ramp_t_MWh)) +
geom_histogram(breaks=seq(-5000, 5000, by = 1000),
col="black",
fill="gray",
alpha = .2) +
labs(x="Ramp (MWh)", y="Count") +
theme_bw(base_family = "serif", base_size = 10)
# xlim(c(18,52)) +
# ylim(c(0,30)) +
# labs(title="Histogram for Age")
ggsave("fig_test_set_actual_ramps.png", dpi=300, dev='png', height=3, width=4, units="in") # 2 2, 4 5, 3 4
# Distributions of predictions per month per algorithm (one figure per alg with 12 boxplots) ######
# try 1 #####
library(plotly)
jan <- ggplot(stack(test_set_with_errors[which(test_set_with_errors$month1==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + #labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="January")
feb <- ggplot(stack(test_set_with_errors[which(test_set_with_errors$month2==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + #labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="February")
mar <- ggplot(stack(test_set_with_errors[which(test_set_with_errors$month3==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + #labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="March")
apr <- ggplot(stack(test_set_with_errors[which(test_set_with_errors$month4==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + #labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="April")
may <- ggplot(stack(test_set_with_errors[which(test_set_with_errors$month5==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + #labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="May")
jun <- ggplot(stack(test_set_with_errors[which(test_set_with_errors$month6==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + #labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="June")
jul <- ggplot(stack(test_set_with_errors[which(test_set_with_errors$month7==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + #labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="July")
aug <- ggplot(stack(test_set_with_errors[which(test_set_with_errors$month8==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="August")
sep <- ggplot(stack(test_set_with_errors[which(test_set_with_errors$month9==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + #labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="September")
oct <- ggplot(stack(test_set_with_errors[which(test_set_with_errors$month10==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + #labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="October")
nov <- ggplot(stack(test_set_with_errors[which(test_set_with_errors$month11==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + #labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="November")
dec <- ggplot(stack(test_set_with_errors[which(test_set_with_errors$month12==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="December")
pdf('rplot.pdf')
subplot(jan, feb, mar, apr,
may, jun, jul, aug,
sep, oct, nov, dec,
shareY = TRUE, shareX = TRUE, nrows = 3, titleY = TRUE)
dev.off()
ggsave("fig_month_error.png", dpi=300, dev='png', height=6, width=9, units="in") # 2 2, 4 5, 3 4
# try 2 #######
old.par <- par(mfrow=c(4, 3))
ggplot(stack(test_set_with_errors[which(test_set_with_errors$month1==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="January")
ggplot(stack(test_set_with_errors[which(test_set_with_errors$month2==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="February")
ggplot(stack(test_set_with_errors[which(test_set_with_errors$month3==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="March")
ggplot(stack(test_set_with_errors[which(test_set_with_errors$month4==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="April")
ggplot(stack(test_set_with_errors[which(test_set_with_errors$month5==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="May")
ggplot(stack(test_set_with_errors[which(test_set_with_errors$month6==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="June")
ggplot(stack(test_set_with_errors[which(test_set_with_errors$month7==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="July")
ggplot(stack(test_set_with_errors[which(test_set_with_errors$month8==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="August")
ggplot(stack(test_set_with_errors[which(test_set_with_errors$month9==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="September")
ggplot(stack(test_set_with_errors[which(test_set_with_errors$month10==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="October")
ggplot(stack(test_set_with_errors[which(test_set_with_errors$month11==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="November")
ggplot(stack(test_set_with_errors[which(test_set_with_errors$month12==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="December")
par(old.par)
#p <- subplot(jan, feb, mar, apr,
# may, jun, jul, aug,
# sep, oct, nov, dec,
# shareY = TRUE, shareX = TRUE, nrows = 4)
ggsave("fig_month_error.png", dpi=300, dev='png', height=6, width=8, units="in") # 2 2, 4 5, 3 4
# try 3 #####
par(mfrow=c(3,4))
ggplot(stack(test_set_with_errors[which(test_set_with_errors$month1==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + #labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="January")
ggplot(stack(test_set_with_errors[which(test_set_with_errors$month2==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + #labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="February")
ggplot(stack(test_set_with_errors[which(test_set_with_errors$month3==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + #labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="March")
ggplot(stack(test_set_with_errors[which(test_set_with_errors$month4==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + #labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="April")
ggplot(stack(test_set_with_errors[which(test_set_with_errors$month5==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + #labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="May")
ggplot(stack(test_set_with_errors[which(test_set_with_errors$month6==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + #labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="June")
ggplot(stack(test_set_with_errors[which(test_set_with_errors$month7==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + #labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="July")
ggplot(stack(test_set_with_errors[which(test_set_with_errors$month8==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="August")
ggplot(stack(test_set_with_errors[which(test_set_with_errors$month9==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + #labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="September")
ggplot(stack(test_set_with_errors[which(test_set_with_errors$month10==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + #labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="October")
ggplot(stack(test_set_with_errors[which(test_set_with_errors$month11==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + #labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="November")
ggplot(stack(test_set_with_errors[which(test_set_with_errors$month12==1),c("Ridge", "Lasso", "OLS", "DT","Benchmark")]),
aes(x = ind, y = values)) +
geom_boxplot() + labs(x = "Machine Learning algorithm") + labs(y = "Prediction error (MWh)") +
theme_bw(base_family = "serif", base_size = 10) + ylim(c(-5000,5000)) + labs(title="December")
# continue here: this one worked the best (but couldn't save it or size it well)
# Economic impact ##################################################################################
# import data
price_01_2016p1 <- read.csv("./as_prices/01_2016_p1.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
price_01_2016p2 <- read.csv("./as_prices/01_2016_p2.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
price_02_2016 <- read.csv("./as_prices/02_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
price_03_2016 <- read.csv("./as_prices/03_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
price_04_2016 <- read.csv("./as_prices/04_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
price_05_2016p1 <- read.csv("./as_prices/05_2016_p1.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
price_05_2016p2 <- read.csv("./as_prices/05_2016_p2.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
price_06_2016 <- read.csv("./as_prices/06_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
price_07_2016 <- read.csv("./as_prices/07_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
price_08_2016 <- read.csv("./as_prices/08_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
price_09_2016 <- read.csv("./as_prices/09_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
price_10_2016 <- read.csv("./as_prices/10_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
price_11_2016 <- read.csv("./as_prices/11_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
price_12_2016 <- read.csv("./as_prices/12_2016.csv",stringsAsFactors=F, header = T , fill = TRUE, sep = ",", quote = "\"", dec = ".")
# procesing prices
source("process_prices.R")
sr_price_01_2016p1 <- process_prices(price_01_2016p1)
sr_price_01_2016p2 <- process_prices(price_01_2016p2)
sr_price_02_2016 <- process_prices(price_02_2016)
sr_price_03_2016 <- process_prices(price_03_2016)
sr_price_04_2016 <- process_prices(price_04_2016)
sr_price_05_2016p1 <- process_prices(price_05_2016p1)
sr_price_05_2016p2 <- process_prices(price_05_2016p2)
sr_price_06_2016 <- process_prices(price_06_2016)
sr_price_07_2016 <- process_prices(price_07_2016)
sr_price_08_2016 <- process_prices(price_08_2016)
sr_price_09_2016 <- process_prices(price_09_2016)
sr_price_10_2016 <- process_prices(price_10_2016)
sr_price_11_2016 <- process_prices(price_11_2016)
sr_price_12_2016 <- process_prices(price_12_2016)
# append all months to have only one set for the year
price_hourly_2016 <- rbind(sr_price_01_2016p1,
sr_price_01_2016p2,
sr_price_02_2016,
sr_price_03_2016,
sr_price_04_2016,
sr_price_05_2016p1,
sr_price_05_2016p2,
sr_price_06_2016,
sr_price_07_2016,
sr_price_08_2016,
sr_price_09_2016,
sr_price_10_2016,
sr_price_11_2016,
sr_price_12_2016)
# order by year, month, day, and hour
price_hourly_2016<- price_hourly_2016[order(price_hourly_2016$year,
price_hourly_2016$month,
price_hourly_2016$day,
price_hourly_2016$hour),]
# just to look at duplicates
price_hourly_2016[duplicated(price_hourly_2016), ]
# dropped duplicates
price_hourly_2016 <- unique(price_hourly_2016)
# converting ramps (actual and predicted) dataframe into a dataframe without categorical vars.
ramp <- plot_ramps
ramp$month <- ifelse(ramp$month1==1, 1,
ifelse(ramp$month2==1, 2,
ifelse(ramp$month3==1, 3,
ifelse(ramp$month4==1, 4,
ifelse(ramp$month5==1, 5,
ifelse(ramp$month6==1, 6,
ifelse(ramp$month7==1, 7,
ifelse(ramp$month8==1, 8,
ifelse(ramp$month9==1, 9,
ifelse(ramp$month10==1, 10,
ifelse(ramp$month11==1, 11,
ifelse(ramp$month12==1, 12, 0))))))))))))
ramp$hour <- ifelse(ramp$hour1==1, 1,
ifelse(ramp$hour2==1, 2,
ifelse(ramp$hour3==1, 3,
ifelse(ramp$hour4==1, 4,
ifelse(ramp$hour5==1, 5,
ifelse(ramp$hour6==1, 6,
ifelse(ramp$hour7==1, 7,
ifelse(ramp$hour8==1, 8,
ifelse(ramp$hour9==1, 9,
ifelse(ramp$hour10==1, 10,
ifelse(ramp$hour11==1, 11,
ifelse(ramp$hour12==1, 12,
ifelse(ramp$hour13==1, 13,
ifelse(ramp$hour14==1, 14,
ifelse(ramp$hour15==1, 15,
ifelse(ramp$hour16==1, 16,
ifelse(ramp$hour17==1, 17,
ifelse(ramp$hour18==1, 18,
ifelse(ramp$hour19==1, 19,
ifelse(ramp$hour20==1, 20,
ifelse(ramp$hour21==1, 21,
ifelse(ramp$hour22==1, 22,
ifelse(ramp$hour23==1, 23, 0)))))))))))))))))))))))
ramp$year <- 2016
# merging ramps and price dataframes:
ramps_and_prices <- merge(ramp, price_hourly_2016,by=c("year","month", "day", "hour"))
# order by year, month, day, and hour
ramps_and_prices<- ramps_and_prices[order(ramps_and_prices$year,
ramps_and_prices$month,
ramps_and_prices$day,
ramps_and_prices$hour),]
# drop redundant columns
ramps_and_prices$month2 <- NULL
ramps_and_prices$month3 <- NULL
ramps_and_prices$month4 <- NULL
ramps_and_prices$month5 <- NULL
ramps_and_prices$month6 <- NULL
ramps_and_prices$month7 <- NULL
ramps_and_prices$month8 <- NULL
ramps_and_prices$month9 <- NULL
ramps_and_prices$month10 <- NULL
ramps_and_prices$month11 <- NULL
ramps_and_prices$month12 <- NULL
ramps_and_prices$hour1 <- NULL
ramps_and_prices$hour2 <- NULL
ramps_and_prices$hour3 <- NULL
ramps_and_prices$hour4 <- NULL
ramps_and_prices$hour5 <- NULL
ramps_and_prices$hour6 <- NULL
ramps_and_prices$hour7 <- NULL
ramps_and_prices$hour8 <- NULL
ramps_and_prices$hour9 <- NULL
ramps_and_prices$hour10 <- NULL
ramps_and_prices$hour11 <- NULL
ramps_and_prices$hour12 <- NULL
ramps_and_prices$hour13 <- NULL
ramps_and_prices$hour14 <- NULL
ramps_and_prices$hour15 <- NULL
ramps_and_prices$hour16 <- NULL
ramps_and_prices$hour17 <- NULL
ramps_and_prices$hour18 <- NULL
ramps_and_prices$hour19 <- NULL
ramps_and_prices$hour20 <- NULL
ramps_and_prices$hour21 <- NULL
ramps_and_prices$hour22 <- NULL
ramps_and_prices$hour23 <- NULL
# CALCULATING costs
ramps_and_prices$Cost_Ridge <- abs(ramps_and_prices$Ridge*ramps_and_prices$Price_dollars_per_MWh)
ramps_and_prices$Cost_Lasso <- abs(ramps_and_prices$Lasso*ramps_and_prices$Price_dollars_per_MWh)
ramps_and_prices$Cost_OLS <- abs(ramps_and_prices$OLS*ramps_and_prices$Price_dollars_per_MWh)
ramps_and_prices$Cost_DT <- abs(ramps_and_prices$DT*ramps_and_prices$Price_dollars_per_MWh)
ramps_and_prices$Cost_Benchmark <- abs(ramps_and_prices$Benchmark*ramps_and_prices$Price_dollars_per_MWh)
# adding costs across all timepoints in the test set
Ridge <- sum(ramps_and_prices$Cost_Ridge)
Lasso <- sum(ramps_and_prices$Cost_Lasso)
OLS <- sum(ramps_and_prices$Cost_OLS)
DT <- sum(ramps_and_prices$Cost_DT)
Benchmark <- sum(ramps_and_prices$Cost_Benchmark)
Ridge <- Ridge/Benchmark*100 # 30%
Lasso <- Lasso/Benchmark*100 # 33%
OLS <- OLS/Benchmark*100 # 30%
DT <- DT/Benchmark*100 # 34%