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+# Combined models
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+# Continuation of clusterviz.R and weathmod.R
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+
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+library(TSA)
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+library(caTools)
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+library(dplyr)
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+library(ggplot2)
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+library(reticulate)
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+library(tidyr)
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+library(MASS)
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+theme_set(theme_bw())
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+use_virtualenv("../venv/")
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+
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+p <- import("pandas")
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+sns <- import("seaborn")
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+aggdf <- p$read_pickle("../data/9-clusters.agg.pkl")
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+aggdf$cluster <- factor(aggdf$cluster)
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+clusters <- levels(aggdf$cluster)
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+str(aggdf)
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+mtempdf <- read.csv("../data/weatherharm.csv", stringsAsFactors = FALSE) %>%
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+ mutate(x = as.POSIXct(x, tz = "UTC")) %>%
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+ rename(read_time = x, rollingmin = y, fitmin = f, resmin = r)
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+str(mtempdf)
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+
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+ntps <- length(unique(aggdf$read_time))
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+
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+clus = "9"
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+
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+
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+harm.y <- ts(1:ntps, frequency = floor(48 * 365.25)) %>% harmonic(2)
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+harm.w <- ts(1:ntps, frequency = floor(48 * 7)) %>% harmonic(3)
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+harm.d <- ts(1:ntps, frequency = floor(48)) %>% harmonic(3)
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+colnames(harm.y) <- sprintf("%s.%s.%s", "year", rep(c("cos", "sin"), each = ncol(harm.y)/2), rep(1:(ncol(harm.y)/2), times = 2))
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+colnames(harm.w) <- sprintf("%s.%s.%s", "week", rep(c("cos", "sin"), each = ncol(harm.w)/2), rep(1:(ncol(harm.w)/2), times = 2))
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+colnames(harm.d) <- sprintf("%s.%s.%s", "day", rep(c("cos", "sin"), each = ncol(harm.d)/2), rep(1:(ncol(harm.d)/2), times = 2))
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+
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+clusdf <- filter(aggdf, cluster == clus) %>%
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+ dplyr::select(read_time, kwh = kwh_tot_mean) %>%
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+ left_join(mtempdf, by = "read_time") %>% cbind(harm.y, harm.w, harm.d)
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+str(clusdf)
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+
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+ycols <- paste(colnames(harm.y), collapse = " + ")
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+wcols <- paste(colnames(harm.w), collapse = " + ")
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+dcols <- paste(colnames(harm.d), collapse = " + ")
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+
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+nform.full <- sprintf("kwh ~ %s + %s + %s + (%s):(%s) + (%s):(%s) + (%s):(%s) + resmin + resmin:(%s) + resmin:(%s) + resmin:(%s)",
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+ ycols, wcols, dcols, ycols, wcols, ycols, dcols, wcols, dcols, ycols, wcols, dcols) %>% formula()
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+nform.comp <- sprintf("kwh ~ %s + %s + %s + (%s):(%s) + (%s):(%s) + resmin + resmin:(%s) + resmin:(%s) + resmin:(%s)",
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+ ycols, wcols, dcols, ycols, dcols, wcols, dcols, ycols, wcols, dcols) %>% formula()
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+nform.now <- sprintf("kwh ~ %s + %s + %s + (%s):(%s) + (%s):(%s)",
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+ ycols, wcols, dcols, ycols, dcols, wcols, dcols) %>% formula()
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+nform.min <- formula("kwh ~ 1")
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+nform.start <- sprintf("kwh ~ %s + %s + %s + resmin",
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+ ycols, wcols, dcols) %>% formula()
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+
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+# charmmod <- lm(kwh ~ resmin + harm.y * harm.w * harm.d + resmin:harm.y, data = clusdf)
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+charmmod <- lm(nform.comp, data = clusdf)
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+# charmmod <- lm(nform.full, data = clusdf)
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+# charmmod <- lm(kwh ~ ., data = clusdf)
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+summary(charmmod)
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+
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+mean(abs(lm(nform.now, data = clusdf)$residuals))
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+mean(abs(lm(nform.comp, data = clusdf)$residuals))
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+mean(abs(lm(nform.full, data = clusdf)$residuals))
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+sd(lm(nform.now, data = clusdf)$residuals)
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+sd(lm(nform.comp, data = clusdf)$residuals)
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+sd(lm(nform.full, data = clusdf)$residuals)
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+
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+cmdf <- data.frame(x = clusdf$read_time, y = clusdf$kwh, f = fitted(charmmod), r = resid(charmmod))
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+cmplot <-ggplot(cmdf, aes(x = x, y = y)) + geom_line(aes(y = f), color = "blue", size = 2) + geom_point() +
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+ geom_point(aes(y = r), color = "darkgreen")
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+
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+cmplot
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+
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+cmplot + coord_cartesian(xlim = c(as.POSIXct("2017-08-01", tz = "UTC"), as.POSIXct("2017-09-01", tz = "UTC")))
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+
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+# sres <- stepAIC(charmmod, scope = list(upper = nform.full, lower = nform.min),
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+# direction = "both", steps = 300)
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+
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+
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