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read_solardcell_data.R
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read_solardcell_data.R
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#load solarzellen data
library(readr)
library(dplyr)
library(ggplot2)
library(lubridate)
source("summary_se.R")
source("solar_auswertung.R")
dir_name = "solarzellen_data"
solarcell_files_names = list.files(path = dir_name, pattern="/*.csv")
solarcell_files_names<- paste(dir_name, solarcell_files_names, sep = "/")
solarcell_tbl <- data_frame("datum"=character(0), "zaehlerstand_morgens"= numeric(0), "zaehlerstand_abends" = numeric(0),"produktion"= numeric(0),"eigen_verbrauch"= numeric(0),"eingespeist"= numeric(0))
solarcell_tbl = lapply(solarcell_files_names, read_csv2) %>% bind_rows()
#using lubridate to make the Datum column more usefull
solarcell_tbl$Datum <- dmy(solarcell_tbl$Datum)
solarcell_tbl <- summarySE(solarcell_tbl, measurevar="Produktion", groupvars=c("YEAR","MONTH"))
solarcell_tbl$rating <- sapply(solarcell_tbl$Produktion, wetter)
y <- solarcell_tbl$Produktion
t <- 1:2167
x <- solarcell_tbl$Datum
# sinus fit
#1
ssp <- spectrum(y)
per <- 1/ssp$freq[ssp$spec==max(ssp$spec)]
reslm <- lm(y ~ sin(2*pi/per*t)+cos(2*pi/per*t))
summary(reslm)
rg <- diff(range(y))
plot(y~t,ylim=c(min(y)-0.1*rg,max(y)+0.1*rg))
lines(fitted(reslm)~t,col=4,lty=1) # dashed blue line is sin fit
# including 2nd harmonic really improves the fit
reslm2 <- lm(y ~ sin(2*pi/per*t)+cos(2*pi/per*t)+sin(4*pi/per*t)+cos(4*pi/per*t))
summary(reslm2)
lines(fitted(reslm2)~t,col=3)
#2 fft
raw.fft = fft(y)
ggplot(solarcell_tbl, aes(x = Datum, y = Produktion)) +
geom_point(size=2, shape=23) +
geom_smooth(linetype="dashed")
ggplot(data = solarcell_tbl, aes(x = Datum, y = Produktion)) +
geom_point() +
geom_smooth(se=FALSE, method="lm", formula = y ~ sin(2*pi/per*x)+cos(2*pi/per*x))
#including second harmonic
ggplot(data = solarcell_tbl, aes(x = Datum, y = Produktion)) +
geom_point() +
geom_smooth(se=FALSE, method="lm", formula = y ~ sin(2*pi/per*x)+cos(2*pi/per*x)+sin(4*pi/per*x)+cos(4*pi/per*x))
#manage the dates
solarcell_tbl$YEAR <- year(solarcell_tbl$Datum)
solarcell_tbl$MONTH <- month(solarcell_tbl$Datum)
solarcell_tbl$DAY <- day(solarcell_tbl$Datum)
ggplot(data = solarcell_tbl, aes(x = MONTH, y = Produktion )) +
geom_point() +
facet_wrap(~YEAR) +
geom_smooth(se=FALSE, method="lm", formula = y ~ sin(2*pi/per*x)+cos(2*pi/per*x)+sin(4*pi/per*x)+cos(4*pi/per*x))
ggplot(data = solarcell_tbl, aes(x = MONTH, y = Produktion )) +
geom_bar(aes(fill=YEAR), position=position_dodge(.9), stat="identity") +
facet_wrap(~YEAR) +
geom_errorbar(aes(ymin=Produktion-se, ymax=Produktion+se),
width=.2, # Width of the error bars
position=position_dodge(.9))
# monthly plot
nr <- solarcell_tbl %>%
group_by(YEAR, MONTH) %>%
summarise(solarcell_tbl$Produktion) %>% nrow()
solar_monthly <- data.frame(matrix(NA, nrow = nr, ncol = 1))
solar_monthly <- solarcell_tbl %>%
group_by(YEAR, MONTH) %>%
summarise(Produktion)
x <-1:nrow(solar_monthly)
ggplot(solar_monthly, aes(x=x, y=solar_monthly$Produktion)) + geom_bar(stat = "identity", width = 0.5)
#summary over for months
solarcell_tbl %>%
group_by( MONTH) %>%
summarise_at(vars(Produktion), funs(mean,sd))
#summary over for year
solarcell_tbl %>%
group_by(YEAR) %>%
summarise_at(vars(Produktion), funs(mean,sd))
# for(file in solarcell_files_names) {
# tmp <- read_csv2(file)
# bind_rows(solarcell_tbl,tmp)
# }
#
# tmp <-read_csv2(solarcell_files_names[4], skip=1)
# rbind(solarcell_tbl, tmp)
# solarcell_files_names[4]
# Step 2: drop anything past the N/2 - 1th element.
# This has something to do with the Nyquist-shannon limit, I believe
# (https://en.wikipedia.org/wiki/Nyquist%E2%80%93Shannon_sampling_theorem)
truncated.fft = raw.fft[seq(1, length(y)/2 - 1)]
# Step 3: drop the first element. It doesn't contain frequency information.
truncated.fft[1] = 0
# Step 4: the importance of each frequency corresponds to the absolute value of the FFT.
# The 2, pi, and length(y) ensure that omega is on the correct scale relative to t.
# Here, I set omega based on the largest value using which.max().
omega = which.max(abs(truncated.fft)) * 2 * pi / length(y)
#3 tidyverse test
tt <- solarcell_tbl %>%
group_by(YEAR, MONTH) %>%
summarise(Produktion)
str(tt)
x <-1:nrow(tt)
ggplot(tt, aes(x=x, y=P)) + geom_bar(stat = "identity", width = 0.5)