Smart Home Energy Saving Analysis in R

Smart home technology, such as Amazon Echo and Google Home, has grown significantly. These smart devices are taking over traditional manual controls in homes, offering new ways to manage home energy usage. Most research today looks at how these smart home devices make life easier or more convenient, not how they save energy. However, it's important to determine which smart products can help save energy.

Objectives and Goals of Smart Home Energy Saving Analysis

Here we discuss the main Objectives and Goals of Smart Home Energy Saving Analysis.

1. Analyze historical energy consumption data to identify trends and patterns.
2. Determine peak usage times and the factors influencing energy consumption.
3. Pinpoint areas where energy consumption can be reduced without compromising comfort or functionality.
4. Recognize inefficient appliances or systems that could be upgraded or replaced.
5. Create models to forecast future energy consumption based on historical data and other influencing factors.
6. Use predictive analytics to expect high-energy usage periods and plan accordingly.
7. Achieve a measurable reduction in energy bills by implementing the identified energy-saving measures.

Description of Smart Home Energy Dataset

The dataset Smart Home Energy Usage Dataset contains detailed records of energy consumption in a smart home setting. It provides insights into how different factors affect energy usage, including environmental conditions and operational states of various appliances. Here’s a summary of the dataset:

• Timestamp: Records the date and time when the data was collected.
• HomeID: Identifier for the specific home where the data was recorded.
• Energy Consumption: Total energy consumed in kilowatt-hours (kWh).
• Temperature Setting: Temperature setting of the home’s HVAC system in degrees Celsius.
• Occupancy Status: Indicates whether the home is occupied or not.
• Appliance: Specific appliance or system being monitored (e.g., HVAC, lighting).
• Usage Duration (minutes): Duration for which the appliance was used, measured in minutes.
• Season: Season during which the data was recorded (e.g., Spring, Summer, Autumn, Winter).
• Days of the Week: Day of the week when the data was recorded (e.g., Monday, Tuesday).
• Holiday: Indicates if the data was recorded on a holiday or not.

Dataset Link: Smart Home Energy Usage Dataset

Now we discuss step by step Implementing Smart Home Energy Saving Analysis in R Programming Language.

Step 1: Install and Load Necessary Packages

First we will Install and Load Necessary Packages.

install.packages(c("tidyverse", "lubridate", "ggplot2", "forecast", "cluster",
                   "factoextra"))
library(tidyverse)
library(lubridate)
library(ggplot2)
library(forecast)
library(cluster)
library(factoextra)

Step 2 : Load the Dataset

Now we will load the dataset.

data <- read.csv("smart_home_energy_usage_dataset.csv")

# Check the first few rows of the dataset
head(data)

Output:

            timestamp home_id energy_consumption_kWh temperature_setting_C
1 2023-01-01 00:00:00      44                   2.87                  22.1
2 2023-01-01 01:00:00      81                   0.56                  15.4
3 2023-01-01 02:00:00      94                   4.49                  22.4
4 2023-01-01 03:00:00      20                   2.13                  24.6
5 2023-01-01 04:00:00       3                   2.74                  21.4
6 2023-01-01 05:00:00      34                   0.66                  19.5
  occupancy_status    appliance usage_duration_minutes season day_of_week holiday
1         Occupied Refrigerator                    111 Spring      Sunday       0
2         Occupied         HVAC                    103 Summer      Sunday       0
3         Occupied  Electronics                     12 Autumn      Sunday       0
4       Unoccupied   Dishwasher                     54 Autumn      Sunday       0
5       Unoccupied         HVAC                      6 Summer      Sunday       0
6       Unoccupied  Electronics                      6 Winter      Sunday       0

Step 3: Data Preprocessing

Now we will perform data Preprocessing techniques.

# Check for missing values
colSums(is.na(data))

# Check the summary of the data
summary(data)

# Example: Normalizing Energy Consumption
data$energy_consumption_kWh <- scale(data$energy_consumption_kWh)

# Extract date components from the timestamp
data$Date <- ymd_hms(data$timestamp)
data$DayOfWeek <- wday(data$Date, label = TRUE)
data$Month <- month(data$Date, label = TRUE)
data$Year <- year(data$Date)

Output:

             timestamp                home_id energy_consumption_kWh 
                     0                      0                      0 
 temperature_setting_C       occupancy_status              appliance 
                     0                      0                      0 
usage_duration_minutes                 season            day_of_week 
                     0                      0                      0 
               holiday 
                     0 

               timestamp         home_id      energy_consumption_kWh
 2023-01-01 00:00:00:     1   Min.   : 1.00   Min.   :0.100         
 2023-01-01 01:00:00:     1   1st Qu.:25.00   1st Qu.:1.320         
 2023-01-01 02:00:00:     1   Median :50.00   Median :2.550         
 2023-01-01 03:00:00:     1   Mean   :50.02   Mean   :2.549         
 2023-01-01 04:00:00:     1   3rd Qu.:75.00   3rd Qu.:3.780         
 2023-01-01 05:00:00:     1   Max.   :99.00   Max.   :5.000         
 (Other)            :999994                                         
 temperature_setting_C   occupancy_status            appliance     
 Min.   :15.0          Occupied  :500394   Dishwasher     :166629  
 1st Qu.:17.5          Unoccupied:499606   Electronics    :166638  
 Median :20.0                              HVAC           :166241  
 Mean   :20.0                              Lighting       :167310  
 3rd Qu.:22.5                              Refrigerator   :166804  
 Max.   :25.0                              Washing Machine:166378  
                                                                   
 usage_duration_minutes    season          day_of_week        holiday       
 Min.   :  0.00         Autumn:250372   Friday   :142848   Min.   :0.00000  
 1st Qu.: 30.00         Spring:249559   Monday   :142872   1st Qu.:0.00000  
 Median : 59.00         Summer:250046   Saturday :142848   Median :0.00000  
 Mean   : 59.51         Winter:250023   Sunday   :142872   Mean   :0.09959  
 3rd Qu.: 90.00                         Thursday :142848   3rd Qu.:0.00000  
 Max.   :119.00                         Tuesday  :142864   Max.   :1.00000  
                                        Wednesday:142848 

Step 4: Data Visualizations

Average energy consumption by Appliance

Now we Shows which appliances use the most energy on average.

# Calculate average energy consumption by appliance
appliance_consumption <- data %>%
  group_by(appliance) %>%
  summarize(Appliance_Consumption = mean(energy_consumption_kWh))

# Create bar plot
ggplot(appliance_consumption, aes(x = reorder(appliance, -Appliance_Consumption),
                                  y = Appliance_Consumption)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  coord_flip() +
  labs(title = "Average Energy Consumption by Appliance", x = "Appliance", 
       y = "Energy Consumption (kWh)")

Output:

Energy consumption by Day of the week

Displays energy consumption patterns by day of the week.

# Calculate average energy consumption by day of the week
daily_consumption_weekday <- data%>%
  group_by(DayOfWeek) %>%
  summarize(Daily_Consumption = mean(energy_consumption_kWh))

# Create bar plot
ggplot(daily_consumption_weekday, aes(x = reorder(DayOfWeek, -Daily_Consumption), 
                                      y = Daily_Consumption)) +
  geom_bar(stat = "identity", fill = "lightcoral") +
  labs(title = "Average Energy Consumption by Day of the Week", x = "Day of the Week",
       y = "Energy Consumption (kWh)")

Output:

Energy Consumption by Month

Highlights seasonal trends in energy usage.

# Calculate average energy consumption by month
monthly_consumption <- data %>%
  group_by(Month) %>%
  summarize(Monthly_Consumption = mean(energy_consumption_kWh))

# Create bar plot
ggplot(monthly_consumption, aes(x = reorder(Month, -Monthly_Consumption), 
                                y = Monthly_Consumption)) +
  geom_bar(stat = "identity", fill = "darkorange") +
  labs(title = "Average Energy Consumption by Month", x = "Month",
       y = "Energy Consumption (kWh)")

Output:

Energy Consumption by Season

Now we will see how energy consumption varies by season.

# Calculate average energy consumption by season
seasonal_consumption <- data %>%
  group_by(season) %>%
  summarize(Seasonal_Consumption = mean(energy_consumption_kWh))

# Create bar plot
ggplot(seasonal_consumption, aes(x = reorder(season, -Seasonal_Consumption), 
                                 y = Seasonal_Consumption)) +
  geom_bar(stat = "identity", fill = "forestgreen") +
  labs(title = "Average Energy Consumption by Season", x = "Season", 
       y = "Energy Consumption (kWh)")

Output:

Step 5: Clustering Analysis

We will use k-means clustering to group appliances based on their energy usage profiles. Use the elbow method to determine the optimal number of clusters.

# Prepare data for clustering
clustering_data <- data %>%
  select(energy_consumption_kWh, temperature_setting_C, usage_duration_minutes)

# Normalize the data for clustering
clustering_data_scaled <- scale(clustering_data)

# Determine the optimal number of clusters using the elbow method
wss <- function(k) {
  kmeans(clustering_data_scaled, k, nstart = 10)$tot.withinss
}
k_values <- 1:10
wss_values <- sapply(k_values, wss)

# Plot elbow method
ggplot(data.frame(k = k_values, wss = wss_values), aes(x = k, y = wss)) +
  geom_line() +
  geom_point() +
  labs(title = "Elbow Method for Optimal k", x = "Number of Clusters", 
       y = "Within Sum of Squares")

Output:

Apply K-Means Clustering

Now we will Apply K-Means Clustering to visualize the cluster.

# Apply k-means clustering with the optimal number of clusters
set.seed(123)
optimal_k <- 3 # Assume 3 clusters from the elbow method
kmeans_result <- kmeans(clustering_data_scaled, centers = optimal_k, nstart = 10)

# Add cluster assignments to data
data$Cluster <- as.factor(kmeans_result$cluster)

# Plot clusters
ggplot(data, aes(x = energy_consumption_kWh, y = temperature_setting_C, 
                       color = Cluster)) +
  geom_point() +
  labs(title = "K-Means Clustering of Energy Consumption", 
       x = "Energy Consumption (kWh)", y = "Temperature Setting (°C)") +
  theme_minimal()

Output:

Conclusion

Smart home energy saving analysis is a crucial step towards creating more efficient, sustainable, and cost-effective living environments. By using advanced technologies like sensors, smart meters, and home energy management systems, homeowners can gain detailed insights into their energy consumption patterns. Through the use of statistical techniques and data analysis in R, it's possible to identify inefficiencies, forecast future energy needs, and develop targeted strategies for reducing energy usage without compromising comfort. Automation and control mechanisms further enhance these efforts by enabling real-time adjustments based on environmental conditions and occupancy, thereby optimizing energy consumption dynamically.