NEB Generation Projections (GWh)

NEB_generation <- transpose(read.csv("NEB_generation.csv", header = FALSE))

NEB_generation <- NEB_generation[-1,]

colnames(NEB_generation) <- c("Year","Hydro","Wind","Biomass_and_Geothermal","Solar","Uranium","Coal","Natural_Gas","Oil")

Start year is 2005

NEB_generation$Year <- NULL

NEB_generation$Hydro <- as.numeric(NEB_generation$Hydro)

NEB_generation$Wind <- as.numeric(NEB_generation$Wind)

NEB_generation$Biomass_and_Geothermal <- as.numeric(NEB_generation$Biomass_and_Geothermal)

NEB_generation$Solar <- as.numeric(NEB_generation$Solar)

NEB_generation$Uranium <- as.numeric(NEB_generation$Uranium)

NEB_generation$Coal <- as.numeric(NEB_generation$Coal)

NEB_generation$Natural_Gas <- as.numeric(NEB_generation$Natural_Gas)

NEB_generation$Oil <- as.numeric(NEB_generation$Oil)

NEB_generation$Total <- NEB_generation$Hydro+NEB_generation$Wind+NEB_generation$Biomass_and_Geothermal+NEB_generation$Solar+NEB_generation$Uranium+NEB_generation$Coal+NEB_generation$Natural_Gas+NEB_generation$Oil

NEB_generation.ts <- ts(NEB_generation, start = 2005, frequency = 1)

ggplot2::autoplot(NEB_generation.ts, ylab="GWh", main="Ontario Annual Electricity Generation Projections")

NEB Capacity Projections (MW)

NEB_capacity <- transpose(read.csv("NEB_capacity.csv", header = FALSE))

NEB_capacity <- NEB_capacity[-1,]

colnames(NEB_capacity) <- c("Year","Petroleum1","Petroleum2","Petroleum3","Coal", "Nuclear","Biomass_and_Geothermal","Solar","Wind","Hydro")

NEB_capacity$Year <- NULL

NEB_capacity$Petroleum <- as.numeric(NEB_capacity$Petroleum1) + as.numeric(NEB_capacity$Petroleum2) + as.numeric(NEB_capacity$Petroleum3)

NEB_capacity$Petroleum1 <- NULL

NEB_capacity$Petroleum2 <- NULL

NEB_capacity$Petroleum3 <- NULL

NEB_capacity$Coal <- as.numeric(NEB_capacity$Coal)

NEB_capacity$Nuclear <- as.numeric(NEB_capacity$Nuclear)

NEB_capacity$Biomass_and_Geothermal <- as.numeric(NEB_capacity$Biomass_and_Geothermal)

NEB_capacity$Solar <- as.numeric(NEB_capacity$Solar)

NEB_capacity$Wind <- as.numeric(NEB_capacity$Wind)

NEB_capacity$Hydro <- as.numeric(NEB_capacity$Hydro)

NEB_capacity$Total <- NEB_capacity$Petroleum+NEB_capacity$Coal+NEB_capacity$Nuclear+NEB_capacity$Biomass_and_Geothermal+NEB_capacity$Solar+NEB_capacity$Wind+NEB_capacity$Hydro

NEB_capacity.ts <- ts(NEB_capacity, start = 2005, frequency = 1)

ggplot2::autoplot(NEB_capacity.ts, ylab="MW", main="Ontario Annual Electricity Capacity Projections")

Hourly Generator Energy Output and Capability Report

Hourly Generator Energy Output and Capability Report

The Hourly Generator Energy Output and Capability Report presents the energy output and capability for generating facilities in the IESO-administered energy market with a maximum output capability of 20 MW or more.

http://reports.ieso.ca/docrefs/helpfile/GenOutputCapability_h4.pdf

Output is the actual energy production of the unit or facility. The hourly output is the facility’s five-minute outputs averaged over an hour.

Capability is the maximum potential output of the unit or facility under current conditions, which includes maximum unit derates and outages for that hour. In this report, Capability is provided for nuclear, hydro, gas, and biofuel generation (excluding wind and solar).

GOC-2010.xlsx - GOC-2019-Jan-April.xlsx -> IESO_output_2010.csv, IESO_capability_2010.csv - IESO_output_2019.csv, IESO_capability_2019.csv

observation for each hour, 2010 to April 2019

Robert J Hyman, Seasonal periods. forecast::msts

IESO_output_2010 <- fread("IESO_output_2010.csv")

IESO_output_2010$DATE <- NULL

IESO_output_2010$HOUR <- NULL

IESO_output_2010$TOTAL <- rowSums(IESO_output_2010, na.rm=TRUE)

IESO_output_2010 <- IESO_output_2010[,.(TOTAL)]

IESO_output_2011 <- fread("IESO_output_2011.csv")

IESO_output_2011$DATE <- NULL

IESO_output_2011$HOUR <- NULL

IESO_output_2011$TOTAL <- rowSums(IESO_output_2011, na.rm=TRUE)

IESO_output_2011 <- IESO_output_2011[,.(TOTAL)]

IESO_output_2012 <- fread("IESO_output_2012.csv")

IESO_output_2012$DATE <- NULL

IESO_output_2012$HOUR <- NULL

IESO_output_2012$TOTAL <- rowSums(IESO_output_2012, na.rm=TRUE)

IESO_output_2012 <- IESO_output_2012[,.(TOTAL)]

IESO_output_2012.ts <- msts(IESO_output_2012$TOTAL, seasonal.periods = c(24, 24*365.25), start=2012)

forecast::autoplot(IESO_output_2012.ts)

which(IESO_output_2012$TOTAL==0)

[1] 3947 7834

IESO_output_2012$TOTAL[3947]

[1] 0

IESO_output_2012$TOTAL[7834]

[1] 0

IESO_output_2012$TOTAL[3947] <- IESO_output_2012$TOTAL[3946]

IESO_output_2012$TOTAL[7834] <- IESO_output_2012$TOTAL[7833]

IESO_output_2012.ts <- msts(IESO_output_2012$TOTAL, seasonal.periods = c(24, 24*365.25), start=2012)

autoplot(IESO_output_2012.ts)

IESO_output_2013 <- fread("IESO_output_2013.csv")

IESO_output_2013$DATE <- NULL

IESO_output_2013$HOUR <- NULL

IESO_output_2013$TOTAL <- rowSums(IESO_output_2013, na.rm=TRUE)

IESO_output_2013 <- IESO_output_2013[,.(TOTAL)]

IESO_output_2014 <- fread("IESO_output_2014.csv")

IESO_output_2014$DATE <- NULL

IESO_output_2014$HOUR <- NULL

IESO_output_2014$TOTAL <- rowSums(IESO_output_2014, na.rm=TRUE)

IESO_output_2014 <- IESO_output_2014[,.(TOTAL)]

IESO_output_2015 <- fread("IESO_output_2015.csv")

IESO_output_2015 <- IESO_output_2015[,.(TOTAL)]

IESO_output_2016 <- fread("IESO_output_2016.csv")

IESO_output_2016 <- IESO_output_2016[,.(TOTAL)]

IESO_output_2016.ts <- msts(IESO_output_2015$TOTAL, seasonal.periods = c(24, 24*365.25), start=2016)

forecast::autoplot(IESO_output_2016.ts)

for (t in which(IESO_output_2016$TOTAL<4000)){
  IESO_output_2016$TOTAL[t] <- IESO_output_2016$TOTAL[t-1] 
}

IESO_output_2017 <- fread("IESO_output_2017.csv")

IESO_output_2017 <- IESO_output_2017[,.(TOTAL)]

IESO_output_2018 <- fread("IESO_output_2018.csv")

IESO_output_2018 <- IESO_output_2018[,.(TOTAL)]

IESO_output_2019 <- fread("IESO_output_2019.csv")

IESO_output_2019$DATE <- NULL

IESO_output_2019$HOUR <- NULL

IESO_output_2019$TOTAL <- rowSums(IESO_output_2019, na.rm=TRUE)

IESO_output_2019 <- IESO_output_2019[,.(TOTAL)]

IESO_output_2010_to_2019 <- do.call("rbind",list(IESO_output_2010,IESO_output_2011,IESO_output_2012,
                                           IESO_output_2013, IESO_output_2014, IESO_output_2015,
                                           IESO_output_2016, IESO_output_2017, IESO_output_2018,
                                           IESO_output_2019))

IESO_output_2010_to_2019.ts <- msts(IESO_output_2010_to_2019$TOTAL, seasonal.periods = c(24, 168, 8766), start=2010)

forecast::autoplot(IESO_output_2010_to_2019.ts)

IESO_output_2010_to_2019.ts.components <- mstl(IESO_output_2010_to_2019.ts)

forecast::autoplot(IESO_output_2010_to_2019.ts.components)

IESO_output_2010_to_2019.24.ts <- IESO_output_2010_to_2019.ts.components[, "Seasonal24"]

IESO_output_2010_to_2019.168.ts <- IESO_output_2010_to_2019.ts.components[, "Seasonal168"]

IESO_output_2010_to_2019.8766.ts <- IESO_output_2010_to_2019.ts.components[, "Seasonal8766"]

autoplot(subset(IESO_output_2010_to_2019.24.ts, end = 24*7*4))

autoplot(subset(IESO_output_2010_to_2019.168.ts, end = 24*7*4))

autoplot(subset(IESO_output_2010_to_2019.8766.ts, end = 24*7*52))

Realtime Constrained Totals Report (Ontario and Market Demand)

http://reports.ieso.ca/docrefs/helpfile/RealtimeConstTotals_h2.pdf

TOTAL ENERGY: Total energy dispatched into the IESO-controlled grid, calculated as Ontario generation plus imports

TOTAL LOSS: Total losses in the IESO-controlled grid, calculated by the load flow

TOTAL LOAD: Total system load, calculated as Total Energy - Total Loss

TOTAL DISP LOAD: Total MW withdrawn from the IESO-controlled grid by dispatchable load, where the value represents the system-wide amount of dispatchable load that was dispatched down. For example, if the load is bidding 100 MW and gets dispatched down to 90 MW, then the Total Disp Load quantity is 10 MW

ONTARIO DEMAND: Total Ontario electricity demand, calculated as:

Total Energy + Total Generation Without Offers - Total Exports + Total Off Market +/- Over/Under Generation

Hourly Demand Report

PUB_Demand_2002.csv - PUB_Demand_2019.csv (to July)

Demand_2010 <- fread("PUB_Demand_2010.csv")

Demand_2010 <- Demand_2010[,.(DEMAND = `Market Demand`)]

Demand_2011 <- fread("PUB_Demand_2011.csv")

Demand_2011 <- Demand_2011[,.(DEMAND = `Market Demand`)]

Demand_2012 <- fread("PUB_Demand_2012.csv")

Demand_2012 <- Demand_2012[,.(DEMAND = `Market Demand`)]

Demand_2013 <- fread("PUB_Demand_2013.csv")

Demand_2013 <- Demand_2013[,.(DEMAND = `Market Demand`)]

Demand_2014 <- fread("PUB_Demand_2014.csv")

Demand_2014 <- Demand_2014[,.(DEMAND = `Market Demand`)]

Demand_2015 <- fread("PUB_Demand_2015.csv")

Demand_2015 <- Demand_2015[,.(DEMAND = `Market Demand`)]

Demand_2016 <- fread("PUB_Demand_2016.csv")

Demand_2016 <- Demand_2016[,.(DEMAND = `Market Demand`)]

Demand_2017 <- fread("PUB_Demand_2017.csv")

Demand_2017 <- Demand_2017[,.(DEMAND = `Market Demand`)]

Demand_2018 <- fread("PUB_Demand_2018.csv")

Demand_2018 <- Demand_2018[,.(DEMAND = `Market Demand`)]

Demand_2019 <- fread("PUB_Demand_2019.csv")

Demand_2019 <- Demand_2019[,.(DEMAND = `Market Demand`)]

Demand_2010_to_2019 <- do.call("rbind",list(Demand_2010,Demand_2011,Demand_2012,
                                           Demand_2013, Demand_2014, Demand_2015,
                                           Demand_2016, Demand_2017, Demand_2018,
                                           Demand_2019))

Demand_2010_to_2019.ts <- msts(Demand_2010_to_2019$DEMAND, seasonal.periods = c(24, 168, 8766), start=2010)

Demand_2010_to_2019.ts.components <- mstl(Demand_2010_to_2019.ts)

forecast::autoplot(Demand_2010_to_2019.ts.components)

attributes(Demand_2010_to_2019.ts.components)

$dim
[1] 83496     6

$dimnames
$dimnames[[1]]
NULL

$dimnames[[2]]
[1] "Data"         "Trend"        "Seasonal24"   "Seasonal168"  "Seasonal8766" "Remainder"   


$tsp
[1] 2010.000 2019.525 8766.000

$class
[1] "mstl" "mts"  "msts" "ts"  

$seasonal.periods
[1]   24  168 8766

Demand_2010_to_2019.24.ts <- Demand_2010_to_2019.ts.components[, "Seasonal24"]

Demand_2010_to_2019.168.ts <- Demand_2010_to_2019.ts.components[, "Seasonal168"]

Demand_2010_to_2019.8766.ts <- Demand_2010_to_2019.ts.components[, "Seasonal8766"]

class(Demand_2010_to_2019.8766.ts)

[1] "msts" "ts"  

autoplot(subset(Demand_2010_to_2019.Seasonal24.ts, end = 24*7*4))

Error in subset(Demand_2010_to_2019.Seasonal24.ts, end = 24 * 7 * 4) : 
  object 'Demand_2010_to_2019.Seasonal24.ts' not found

autoplot(subset(Demand_2010_to_2019.168.ts, end = 24*7*4))

autoplot(subset(Demand_2010_to_2019.8766.ts, end = 24*7*365))

Hourly Ontario Energy Price (HOEP)

Yearly Hourly HOEP OR Predispatch Report

PUB_PriceHOEPPredispOR_2002.csv - PUB_PriceHOEPPredispOR_2019.csv (to July)

HOEP_2010 <- fread("PUB_PriceHOEPPredispOR_2010.csv")

HOEP_2010 <- HOEP_2010[,.(HOEP)]

HOEP_2011 <- fread("PUB_PriceHOEPPredispOR_2011.csv")

HOEP_2011 <- HOEP_2011[,.(HOEP)]

HOEP_2012 <- fread("PUB_PriceHOEPPredispOR_2012.csv")

HOEP_2012 <- HOEP_2012[,.(HOEP)]

HOEP_2013 <- fread("PUB_PriceHOEPPredispOR_2013.csv")

HOEP_2013 <- HOEP_2013[,.(HOEP)]

HOEP_2014 <- fread("PUB_PriceHOEPPredispOR_2014.csv")

HOEP_2014 <- HOEP_2014[,.(HOEP)]

HOEP_2015 <- fread("PUB_PriceHOEPPredispOR_2015.csv")

HOEP_2015 <- HOEP_2015[,.(HOEP)]

HOEP_2016 <- fread("PUB_PriceHOEPPredispOR_2016.csv")

HOEP_2016 <- HOEP_2016[,.(HOEP)]

HOEP_2017 <- fread("PUB_PriceHOEPPredispOR_2017.csv")

HOEP_2017 <- HOEP_2017[,.(HOEP)]

HOEP_2018 <- fread("PUB_PriceHOEPPredispOR_2018.csv")

HOEP_2018 <- HOEP_2018[,.(HOEP)]

HOEP_2018.ts <- ts(HOEP_2018$HOEP, frequency = 24)

HOEP_2018.ts.components <- decompose(HOEP_2018.ts)

forecast::autoplot(HOEP_2018.ts.components)

autoplot(subset(HOEP_2018.ts.components$seasonal, end = 24*7*4))

HOEP_2019 <- fread("PUB_PriceHOEPPredispOR_2019.csv")

HOEP_2019 <- HOEP_2019[,.(HOEP)]

HOEP_2010_to_2019 <- do.call("rbind",list(HOEP_2010,HOEP_2011,HOEP_2012,
                                           HOEP_2013, HOEP_2014, HOEP_2015,
                                           HOEP_2016, HOEP_2017, HOEP_2018,
                                           HOEP_2019))

HOEP_2010_to_2019.ts <- msts(HOEP_2010_to_2019$HOEP, seasonal.periods = c(24, 168, 8766), start=2010)

autoplot(HOEP_2010_to_2019.ts)

HOEP_2010_to_2019.ts.components <- mstl(HOEP_2010_to_2019.ts)

forecast::autoplot(HOEP_2010_to_2019.ts.components)

HOEP_2010_to_2019.24.ts <- HOEP_2010_to_2019.ts.components[, "Seasonal24"]

HOEP_2010_to_2019.168.ts <- HOEP_2010_to_2019.ts.components[, "Seasonal168"]

HOEP_2010_to_2019.8766.ts <- HOEP_2010_to_2019.ts.components[, "Seasonal8766"]

autoplot(subset(HOEP_2010_to_2019.24.ts, end = 24*7*4))

autoplot(subset(HOEP_2010_to_2019.168.ts, end = 24*7*4))

autoplot(subset(HOEP_2010_to_2019.8766.ts, end = 24*7*52))

autoplot(subset(HOEP_2010_to_2019.ts.components[,"Seasonal24"], end = 24*7*4))