R Notebook
# Import library yang dibutuhkan
library(readr) # untuk membaca csv
library(dplyr) # untuk manipulasi data##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2) # untuk visualisasi
library(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionlibrary(reshape2)## Warning: package 'reshape2' was built under R version 4.5.1library(forecast)## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoolibrary(tseries)Load Data
Load data wifi
# Baca dataset wifi
wifi_df <- read_csv("wifi.csv")## Rows: 1883844 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): Event Time, Uni, Building, Floor
## dbl (2): Associated Client Count, Authenticated Client Count
## dttm (1): time
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.head(wifi_df)## # A tibble: 6 × 7
## time `Event Time` Associated Client Co…¹ Authenticated Client…²
## <dttm> <chr> <dbl> <dbl>
## 1 2020-02-01 00:02:12 Sat Feb 01 … 184 182
## 2 2020-02-01 00:02:12 Sat Feb 01 … 6 6
## 3 2020-02-01 00:02:12 Sat Feb 01 … 18 18
## 4 2020-02-01 00:02:12 Sat Feb 01 … 23 23
## 5 2020-02-01 00:02:12 Sat Feb 01 … 45 45
## 6 2020-02-01 00:02:12 Sat Feb 01 … 16 16
## # ℹ abbreviated names: ¹​`Associated Client Count`,
## # ²​`Authenticated Client Count`
## # ℹ 3 more variables: Uni <chr>, Building <chr>, Floor <chr> wifi_library <- wifi_df %>%
filter(trimws(Building) == "Library") %>%
mutate(time = ymd_hms(time))
wifi_resampled <- wifi_library %>%
mutate(time=floor_date(time,"10 minutes")) %>%
group_by(time) %>%
summarise(`Associated Client Count`=mean(`Associated Client Count`,na.rm=TRUE))
wifi_resampled <- wifi_resampled %>%
rename(occupancy = `Associated Client Count`)
head(wifi_resampled)## # A tibble: 6 × 2
## time occupancy
## <dttm> <dbl>
## 1 2020-02-01 00:00:00 22.8
## 2 2020-02-01 00:10:00 20.6
## 3 2020-02-01 00:20:00 17.2
## 4 2020-02-01 00:30:00 15.4
## 5 2020-02-01 00:40:00 12.4
## 6 2020-02-01 00:50:00 10.5Load data library
lib1 <- read_csv("library1.csv")## Rows: 18864 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): name, units
## dbl (3): reading, cumulative, rate
## dttm (1): ts
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.lib2 <- read_csv("library2.csv")## Rows: 18864 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): name, units
## dbl (3): reading, cumulative, rate
## dttm (1): ts
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.lib3 <- read_csv("library3.csv")## Rows: 18864 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): name, units
## dbl (3): reading, cumulative, rate
## dttm (1): ts
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.head(lib1)## # A tibble: 6 × 6
## ts name reading units cumulative rate
## <dttm> <chr> <dbl> <chr> <dbl> <dbl>
## 1 2020-01-01 00:00:00 MC065-L01/M9R2048 1489442 KWh 1489442 NA
## 2 2020-01-01 00:10:00 MC065-L01/M9R2048 1489449 KWh 1489449 7
## 3 2020-01-01 00:20:00 MC065-L01/M9R2048 1489456 KWh 1489456 7
## 4 2020-01-01 00:30:00 MC065-L01/M9R2048 1489464 KWh 1489464 8
## 5 2020-01-01 00:40:00 MC065-L01/M9R2048 1489471 KWh 1489471 7
## 6 2020-01-01 00:50:00 MC065-L01/M9R2048 1489479 KWh 1489479 8head(lib2)## # A tibble: 6 × 6
## ts name reading units cumulative rate
## <dttm> <chr> <dbl> <chr> <dbl> <dbl>
## 1 2020-01-01 00:00:00 MC065-L01/M11R2056 2129016 KWh 2129016 NA
## 2 2020-01-01 00:10:00 MC065-L01/M11R2056 2129034 KWh 2129034 18
## 3 2020-01-01 00:20:00 MC065-L01/M11R2056 2129054 KWh 2129054 20
## 4 2020-01-01 00:30:00 MC065-L01/M11R2056 2129071 KWh 2129071 17
## 5 2020-01-01 00:40:00 MC065-L01/M11R2056 2129086 KWh 2129086 15
## 6 2020-01-01 00:50:00 MC065-L01/M11R2056 2129103 KWh 2129103 17head(lib3)## # A tibble: 6 × 6
## ts name reading units cumulative rate
## <dttm> <chr> <dbl> <chr> <dbl> <dbl>
## 1 2020-01-01 00:00:00 MC065-L01/M13R2064 6914209 KWh 6914209 NA
## 2 2020-01-01 00:10:00 MC065-L01/M13R2064 6914266 KWh 6914266 57
## 3 2020-01-01 00:20:00 MC065-L01/M13R2064 6914310 KWh 6914310 44
## 4 2020-01-01 00:30:00 MC065-L01/M13R2064 6914376 KWh 6914376 66
## 5 2020-01-01 00:40:00 MC065-L01/M13R2064 6914439 KWh 6914439 63
## 6 2020-01-01 00:50:00 MC065-L01/M13R2064 6914474 KWh 6914474 35lib1 <- lib1 %>%
mutate(ts=ymd_hms(ts))%>%
arrange(ts)## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `ts = ymd_hms(ts)`.
## Caused by warning:
## ! 131 failed to parse.lib2 <- lib2 %>%
mutate(ts=ymd_hms(ts))%>%
arrange(ts)## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `ts = ymd_hms(ts)`.
## Caused by warning:
## ! 131 failed to parse.lib3 <- lib3 %>%
mutate(ts=ymd_hms(ts))%>%
arrange(ts)## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `ts = ymd_hms(ts)`.
## Caused by warning:
## ! 131 failed to parse.electricity_df <- data.frame(
time = lib1$ts,
library_1 = lib1$rate,
library_2 = lib2$rate,
library_3 = lib3$rate,
total_rate = lib1$rate + lib2$rate + lib3$rate
)
dim(electricity_df)## [1] 18864 5head(electricity_df)## time library_1 library_2 library_3 total_rate
## 1 2020-01-01 00:10:00 7 18 57 82
## 2 2020-01-01 00:20:00 7 20 44 71
## 3 2020-01-01 00:30:00 8 17 66 91
## 4 2020-01-01 00:40:00 7 15 63 85
## 5 2020-01-01 00:50:00 8 17 35 60
## 6 2020-01-01 01:00:00 7 17 47 71electricity_df <- electricity_df %>%
mutate(
across(
c(library_1, library_2, library_3),
~ ifelse(is.na(.x), mean(head(.x, 144), na.rm = TRUE), .x)
)
) %>%
mutate(total_rate = library_1 + library_2 + library_3)
head(electricity_df)## time library_1 library_2 library_3 total_rate
## 1 2020-01-01 00:10:00 7 18 57 82
## 2 2020-01-01 00:20:00 7 20 44 71
## 3 2020-01-01 00:30:00 8 17 66 91
## 4 2020-01-01 00:40:00 7 15 63 85
## 5 2020-01-01 00:50:00 8 17 35 60
## 6 2020-01-01 01:00:00 7 17 47 71# Merge by time
merged_df <- inner_join(electricity_df, wifi_resampled, by = "time")
# Cek hasil
dim(merged_df)## [1] 3657 6head(merged_df)## time library_1 library_2 library_3 total_rate occupancy
## 1 2020-02-01 00:10:00 13 11 73 97 20.625
## 2 2020-02-01 00:20:00 13 11 72 96 17.250
## 3 2020-02-01 00:30:00 11 15 74 100 15.375
## 4 2020-02-01 00:40:00 11 13 65 89 12.375
## 5 2020-02-01 00:50:00 11 16 64 91 10.500
## 6 2020-02-01 01:00:00 10 16 64 90 9.250Data Cleaning
# Cek duplikasi index (kalau ada kolom time jadi index)
sum(duplicated(merged_df$time))## [1] 0# Cek missing values per kolom
colSums(is.na(merged_df))## time library_1 library_2 library_3 total_rate occupancy
## 0 0 0 0 0 0# Deskripsi data
summary(merged_df)## time library_1 library_2 library_3
## Min. :2020-02-01 00:10:00 Min. : 7.00 Min. : 7.00 Min. : 29.00
## 1st Qu.:2020-02-07 09:30:00 1st Qu.:11.00 1st Qu.:19.00 1st Qu.: 73.00
## Median :2020-02-13 18:50:00 Median :15.00 Median :33.00 Median :104.00
## Mean :2020-02-13 18:52:54 Mean :15.18 Mean :32.44 Mean : 95.68
## 3rd Qu.:2020-02-20 04:20:00 3rd Qu.:19.00 3rd Qu.:45.00 3rd Qu.:120.00
## Max. :2020-02-26 13:40:00 Max. :31.00 Max. :64.00 Max. :136.00
## total_rate occupancy
## Min. : 49.0 Min. : 2.375
## 1st Qu.:103.0 1st Qu.: 18.625
## Median :151.0 Median : 99.125
## Mean :143.3 Mean :148.696
## 3rd Qu.:183.0 3rd Qu.:250.125
## Max. :219.0 Max. :512.125# Pilih kolom numerik yang relevan (tanpa time)
num_cols <- merged_df %>%
select(library_1, library_2, library_3, occupancy)
# Reshape untuk ggplot
df_melt <- melt(num_cols)## No id variables; using all as measure variables# Plot boxplot
ggplot(df_melt, aes(x = variable, y = value)) +
geom_boxplot(fill = "skyblue", color = "black") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Boxplot of Library Energy Consumption and Occupancy",
x = "Variable", y = "Value")
# Hitung IQR
Q1 <- quantile(merged_df$library_3, 0.25, na.rm = TRUE)
Q3 <- quantile(merged_df$library_3, 0.75, na.rm = TRUE)
IQR_val <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_val
upper_bound <- Q3 + 1.5 * IQR_val
# cari outlier
outliers <- merged_df[merged_df$library_3 < lower_bound |
merged_df$library_3 > upper_bound, ]
nrow(outliers)## [1] 0Visualization
Time Series
#library occupancy & energy consumption
library(ggplot2)
ggplot(merged_df, aes(x = time)) +
geom_line(aes(y = occupancy, color = "Library Occupancy (WiFi)"), size = 0.8) +
geom_line(aes(y = total_rate, color = "Library Energy Consumption"), size = 0.8) +
scale_color_manual(values = c("Library Occupancy (WiFi)" = "blue",
"Library Energy Consumption" = "orange")) +
labs(title = "Time Series of Library Occupancy and Energy Consumption (10-min interval)",
x = "Date",
y = "Count / Energy Rate",
color = "") +
theme_minimal() +
theme(legend.position = "top",
plot.title = element_text(hjust = 0.5))## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Scatter Plot
library(ggplot2)
ggplot(merged_df, aes(x = occupancy, y = total_rate)) +
geom_point(alpha = 0.4, colour = "blue", size = 2) +
ggtitle("Scatter Plot: Occupancy vs Energy Consumption") +
xlab("Occupancy (WiFi clients)") +
ylab("Energy Consumption (kWh or units)") +
theme_minimal() +
theme(
plot.title = element_text(hjust = 0.5)
) +
geom_smooth(method = "loess", se = FALSE, linetype = "dashed", alpha = 0.6)## `geom_smooth()` using formula = 'y ~ x'
Daily Profiles
library(ggplot2)
library(dplyr)
library(tidyr)##
## Attaching package: 'tidyr'## The following object is masked from 'package:reshape2':
##
## smithslibrary(lubridate)
# Buat kolom jam-menit (waktu dalam sehari) dan tanggal
merged_df <- merged_df %>%
mutate(
time_of_day = format(time, "%H:%M:%S"),
date = as.Date(time)
)
# Pivot untuk occupancy - menggunakan tidyr::pivot_wider
occupancy_daily <- merged_df %>%
select(time_of_day, date, occupancy) %>%
pivot_wider(
names_from = date,
values_from = occupancy,
values_fn = mean # mengambil rata-rata jika ada duplikasi
)
# Pivot untuk energy
energy_daily <- merged_df %>%
select(time_of_day, date, total_rate) %>%
pivot_wider(
names_from = date,
values_from = total_rate,
values_fn = mean # mengambil rata-rata jika ada duplikasi
)
# Plot Occupancy Daily Profiles
occupancy_plot =ggplot() +
lapply(names(occupancy_daily)[-1], function(col) {
geom_line(data = occupancy_daily,
aes(x = time_of_day, y = .data[[col]], group = 1),
alpha = 0.2, color = "blue")
}) +
labs(title = "Daily Profiles of Library Occupancy",
x = "Time of Day",
y = "Occupancy") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "none")
print(occupancy_plot)## Warning: Removed 61 rows containing missing values or values outside the scale range
## (`geom_line()`).
# Plot Energy Daily Profiles
energy_plot = ggplot() +
lapply(names(energy_daily)[-1], function(col) {
geom_line(data = energy_daily,
aes(x = time_of_day, y = .data[[col]], group = 1),
alpha = 0.2, color = "orange")
}) +
labs(title = "Daily Profiles of Library Energy Consumption",
x = "Time of Day",
y = "Energy Consumption") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "none") + scale_y_continuous(limits = c(50,225), breaks=seq(50,225,25))
print(energy_plot)## Warning: Removed 61 rows containing missing values or values outside the scale range
## (`geom_line()`).
library(ggplot2)
library(dplyr)
library(tidyr)
# Buat kolom jam-menit (waktu dalam sehari) dan tanggal
merged_df <- merged_df %>%
mutate(
time_of_day = format(time, "%H:%M:%S"),
date = as.Date(time)
)
# Pivot untuk occupancy - menggunakan pivot_wider
occupancy_daily <- merged_df %>%
select(time_of_day, date, occupancy) %>%
pivot_wider(
names_from = date,
values_from = occupancy,
values_fn = mean
) %>%
# Hitung rata-rata untuk setiap time_of_day
mutate(average = rowMeans(select(., -time_of_day), na.rm = TRUE))
# Pivot untuk energy - menggunakan pivot_wider
energy_daily <- merged_df %>%
select(time_of_day, date, total_rate) %>%
pivot_wider(
names_from = date,
values_from = total_rate,
values_fn = mean
) %>%
# Hitung rata-rata untuk setiap time_of_day
mutate(average = rowMeans(select(., -time_of_day), na.rm = TRUE))
# Plot Occupancy Daily Profiles
ggplot() +
# Plot semua hari dengan transparansi
lapply(names(occupancy_daily)[-c(1, ncol(occupancy_daily))], function(col) {
geom_line(data = occupancy_daily,
aes(x = time_of_day, y = .data[[col]], group = 1),
alpha = 0.2, color = "blue")
}) +
# Plot garis rata-rata (average)
geom_line(data = occupancy_daily,
aes(x = time_of_day, y = average, group = 1),
color = "black", size = 1, linetype = "solid") +
labs(title = "Daily Profiles of Library Occupancy",
x = "Time of Day",
y = "Occupancy") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "none")## Warning: Removed 61 rows containing missing values or values outside the scale range
## (`geom_line()`).
# Plot Energy Daily Profiles
ggplot() +
# Plot semua hari dengan transparansi
lapply(names(energy_daily)[-c(1, ncol(energy_daily))], function(col) {
geom_line(data = energy_daily,
aes(x = time_of_day, y = .data[[col]], group = 1),
alpha = 0.2, color = "orange")
}) +
# Plot garis rata-rata (average)
geom_line(data = energy_daily,
aes(x = time_of_day, y = average, group = 1),
color = "black", size = 1, linetype = "solid") +
labs(title = "Daily Profiles of Library Energy Consumption",
x = "Time of Day",
y = "Energy Consumption") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "none") +
scale_y_continuous(limits = c(50, 225), breaks = seq(50, 225, 25))## Warning: Removed 61 rows containing missing values or values outside the scale range
## (`geom_line()`).
Analysis
Peak Hours of Occupancy
avg_occupancy <- occupancy_daily %>%
mutate(avg = rowMeans(select(., -time_of_day), na.rm = TRUE)) %>%
select(time_of_day, avg)
# Pastikan kolom time_of_day jadi format waktu (tanpa tanggal)
avg_occupancy <- avg_occupancy %>%
mutate(time_of_day = hms(time_of_day))
# Identifikasi peak lagi (supaya match class baru)
peak_row <- avg_occupancy %>%
filter(avg == max(avg, na.rm = TRUE))
# Plot
ggplot(avg_occupancy, aes(x = time_of_day, y = avg)) +
geom_line(color = "steelblue", linewidth = 1) +
geom_point(data = peak_row, aes(x = time_of_day, y = avg),
color = "red", size = 3) +
scale_x_time() +
labs(
title = "Average Daily Profile of Library Occupancy",
x = "Time of Day",
y = "Occupancy"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid.major.x = element_blank(), # hilangkan grid vertikal
panel.grid.minor = element_blank()
)
Correlation
# Hitung korelasi
corr <- cor(merged_df$occupancy, merged_df$total_rate, use = "complete.obs")
cat("Correlation:", corr, "\n")## Correlation: 0.8789519# Regresi linear (total_rate ~ occupancy)
model <- lm(total_rate ~ occupancy, data = merged_df)
# Ringkasan model
summary(model)##
## Call:
## lm(formula = total_rate ~ occupancy, data = merged_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -57.17 -15.63 1.72 16.24 51.08
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.036e+02 4.965e-01 208.6 <2e-16 ***
## occupancy 2.673e-01 2.399e-03 111.4 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.89 on 3655 degrees of freedom
## Multiple R-squared: 0.7726, Adjusted R-squared: 0.7725
## F-statistic: 1.241e+04 on 1 and 3655 DF, p-value: < 2.2e-16Mismatch
merged_df <- merged_df %>%
mutate(
predicted_energy = predict(model, newdata = merged_df),
residual = total_rate - predicted_energy
)
# 3. Threshold mismatch
threshold <- 1.5 * sd(merged_df$residual, na.rm = TRUE)
# 4. Filter mismatch
mismatch <- merged_df %>%
filter(abs(residual) > threshold)
cat("Jumlah mismatch:", nrow(mismatch), "\n")## Jumlah mismatch: 484print(mismatch %>% select(occupancy, total_rate, predicted_energy, residual))## occupancy total_rate predicted_energy residual
## 1 7.375 71 105.5317 -34.53166
## 2 6.750 59 105.3646 -46.36462
## 3 9.000 69 105.9660 -36.96596
## 4 8.750 65 105.8991 -40.89914
## 5 5.875 71 105.1308 -34.13077
## 6 3.750 70 104.5628 -34.56285
## 7 3.750 65 104.5628 -39.56285
## 8 3.750 53 104.5628 -51.56285
## 9 3.750 56 104.5628 -48.56285
## 10 3.500 61 104.4960 -43.49603
## 11 3.625 68 104.5294 -36.52944
## 12 5.625 73 105.0640 -32.06396
## 13 109.625 166 132.8590 33.14105
## 14 113.500 168 133.8946 34.10542
## 15 119.375 168 135.4647 32.53527
## 16 196.750 192 156.1439 35.85606
## 17 154.875 180 144.9524 35.04755
## 18 146.750 178 142.7810 35.21904
## 19 47.875 150 116.3557 33.64433
## 20 4.625 72 104.7967 -32.79670
## 21 5.500 63 105.0306 -42.03055
## 22 5.250 72 104.9637 -32.96374
## 23 206.875 191 158.8499 32.15006
## 24 200.625 193 157.1796 35.82043
## 25 85.875 163 126.5115 36.48846
## 26 78.750 163 124.6073 38.39269
## 27 73.250 158 123.1374 34.86261
## 28 49.375 149 116.7566 32.24344
## 29 25.500 142 110.3757 31.62426
## 30 13.125 74 107.0684 -33.06840
## 31 437.500 187 220.4867 -33.48668
## 32 447.500 188 223.1593 -35.15927
## 33 447.000 188 223.0256 -35.02565
## 34 442.125 189 221.7228 -32.72275
## 35 462.500 189 227.1682 -38.16817
## 36 104.875 167 131.5895 35.41053
## 37 68.625 154 121.9013 32.09869
## 38 60.750 160 119.7966 40.20336
## 39 13.500 74 107.1686 -33.16863
## 40 11.500 69 106.6341 -37.63411
## 41 13.750 67 107.2354 -40.23544
## 42 9.000 72 105.9660 -33.96596
## 43 9.625 70 106.1330 -36.13300
## 44 9.875 71 106.1998 -35.19981
## 45 9.375 72 106.0662 -34.06618
## 46 9.375 66 106.0662 -40.06618
## 47 9.500 62 106.0996 -44.09959
## 48 10.375 69 106.3334 -37.33344
## 49 10.875 71 106.4671 -35.46707
## 50 10.750 73 106.4337 -33.43366
## 51 493.500 203 235.4532 -32.45321
## 52 487.875 198 233.9499 -35.94988
## 53 445.750 191 222.6916 -31.69157
## 54 226.375 197 164.0615 32.93849
## 55 184.750 195 152.9368 42.06317
## 56 179.125 183 151.4335 31.56651
## 57 165.375 183 147.7587 35.24133
## 58 160.250 183 146.3890 36.61103
## 59 148.000 191 143.1150 47.88496
## 60 135.875 176 139.8745 36.12549
## 61 126.000 184 137.2353 46.76467
## 62 124.000 180 136.7008 43.29919
## 63 117.875 167 135.0638 31.93616
## 64 111.000 167 133.2264 33.77357
## 65 103.875 168 131.3222 36.67779
## 66 87.250 177 126.8790 50.12098
## 67 80.125 170 124.9748 45.02521
## 68 72.625 164 122.9703 41.02965
## 69 64.375 167 120.7655 46.23454
## 70 50.000 168 116.9236 51.07640
## 71 47.500 151 116.2555 34.74455
## 72 44.625 158 115.4871 42.51292
## 73 41.250 149 114.5851 34.41492
## 74 14.750 68 107.5027 -39.50270
## 75 12.500 67 106.9014 -39.90137
## 76 13.125 73 107.0684 -34.06840
## 77 7.000 68 105.4314 -37.43144
## 78 7.250 66 105.4983 -39.49825
## 79 7.750 73 105.6319 -32.63188
## 80 7.250 60 105.4983 -45.49825
## 81 9.125 66 105.9994 -39.99937
## 82 7.250 73 105.4983 -32.49825
## 83 8.375 73 105.7989 -32.79892
## 84 12.125 67 106.8011 -39.80114
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## 479 8.375 69 105.7989 -36.79892
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## 482 11.125 71 106.5339 -35.53389
## 483 65.500 157 121.0661 35.93388
## 484 66.875 172 121.4336 50.56639#Visualisasi
ggplot(merged_df, aes(x = occupancy, y = total_rate)) +
geom_point(alpha = 0.4, color = "steelblue", size = 2, aes(label = NULL)) +
geom_point(data = mismatch, aes(x = occupancy, y = total_rate),
color = "red", size = 2.5, alpha = 0.8) +
geom_line(aes(y = predicted_energy), color = "black", linewidth = 1) +
labs(
x = "Occupancy",
y = "Energy Consumption",
title = "Occupancy vs Energy with Mismatch Highlighted"
) +
theme_minimal()
Weekend vs Weekday Comparison
Split Weekdays & Weekend
library(dplyr)
library(ggplot2)
# Tambah kolom hari dan kategori weekday/weekend
merged_df <- merged_df %>%
mutate(
day = weekdays(time),
day_type = ifelse(weekdays(time) %in% c("Saturday", "Sunday"), "Weekend", "Weekday")
)Visualisasi
Time Series
ggplot(merged_df, aes(x = time, y = occupancy, color = day_type)) +
geom_line(alpha = 0.7, linewidth = 0.8) +
labs(title = "Time Series of Occupancy: Weekday vs Weekend",
x = "Date",
y = "Occupancy",
color = "Day Type") +
scale_color_manual(values = c("Weekday" = "maroon", "Weekend" = "darkblue")) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
ggplot(merged_df, aes(x = time, y = total_rate, color = day_type)) +
geom_line(alpha = 0.7, linewidth = 0.8) +
labs(title = "Time Series of Energy Consumption: Weekday vs Weekend",
x = "Date",
y = "Energy Consumption",
color = "Day Type") +
scale_color_manual(values = c("Weekday" = "blue", "Weekend" = "red")) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
Scatter Plot
ggplot(merged_df, aes(x = occupancy, y = total_rate, color = day_type)) +
geom_point(alpha = 0.5, size = 1.5) +
labs(title = "Scatter Plot: Occupancy vs Energy Consumption",
x = "Occupancy (WiFi Clients)",
y = "Energy Consumption (kWh)",
color = "Day Type") +
scale_color_manual(values = c("Weekday" = "skyblue", "Weekend" = "darkred")) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
Daily Profiles
occupancy_profile <- merged_df %>%
group_by(day_type, time_of_day) %>%
summarise(avg_occupancy = mean(occupancy, na.rm = TRUE))## `summarise()` has grouped output by 'day_type'. You can override using the
## `.groups` argument.ggplot(occupancy_profile, aes(x = time_of_day, y = avg_occupancy, color = day_type, group = day_type)) +
geom_line(size = 1) +
labs(title = "Daily Profiles - Occupancy",
x = "Time of Day", y = "Average Occupancy", color = "Day Type") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5))
energy_profile <- merged_df %>%
group_by(day_type, time_of_day) %>%
summarise(avg_energy = mean(total_rate, na.rm = TRUE))## `summarise()` has grouped output by 'day_type'. You can override using the
## `.groups` argument.ggplot(energy_profile, aes(x = time_of_day, y = avg_energy, color = day_type, group = day_type)) +
geom_line(size = 1) +
labs(title = "Daily Profiles - Energy Consumption",
x = "Time of Day", y = "Average Energy Consumption (kWh)", color = "Day Type") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5))