Home Appliance Classifiers
Machine learning algorithms used to identify certain home appliances based on electricity consumption
Overview
Publications
Home appliance features
Specification of collected data
| Attribute | Type |
|---|---|
| Current Waveform | Integer Array |
| Real Power | Float |
| Apparent Power | Float |
| Power Factor | Float |
Dataset
Fetures:
- Real electrical power
- Harmonic components from 60Hz to 1320Hz
Classifier tests
import matplotlib.pyplot as plt
import pandas as pd
from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler# Reading the base dataset
dataset = pd.read_csv('data/dataset.csv')# Splitting data sets for training
inputs = dataset.iloc[:, 1:].values
outputs = dataset.iloc[:, 0].values
x_train, x_test, y_train, y_test = train_test_split(inputs, outputs, test_size=.3)# Standardizing the dataset
scaler = StandardScaler()
scaler.fit(x_train)
x_train = scaler.transform(x_train)
x_test = scaler.transform(x_test)classifier = RandomForestClassifier()
classifier.fit(x_train, y_train)
y_predict = classifier.predict(x_test)print(metrics.classification_report(y_test, y_predict, zero_division=0)) precision recall f1-score support
Air Fryer 1.00 1.00 1.00 34
Batedeira 1.00 1.00 1.00 38
Cafeteira 1.00 1.00 1.00 31
Espremedor de Frutas 1.00 1.00 1.00 29
Ferro Eletrico 0.97 1.00 0.99 36
Furadeira 1.00 1.00 1.00 34
Gelagua 1.00 1.00 1.00 29
Lampada Incandescente 1.00 1.00 1.00 159
Liquidificador 1.00 1.00 1.00 119
Maquina de Lavar 1.00 1.00 1.00 53
Micro-ondas 1.00 1.00 1.00 52
Prancha de Cabelo 1.00 1.00 1.00 48
Sanduicheira 1.00 1.00 1.00 94
Secador de Cabelo 1.00 0.99 0.99 67
TV 1.00 1.00 1.00 57
Ventilador 1.00 1.00 1.00 83
accuracy 1.00 963
macro avg 1.00 1.00 1.00 963
weighted avg 1.00 1.00 1.00 963
print(metrics.confusion_matrix(y_test, y_predict))[[ 34 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 38 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 31 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 29 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 36 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 34 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 29 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 159 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 119 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 53 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 52 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 48 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 94 0 0 0]
[ 0 0 0 0 1 0 0 0 0 0 0 0 0 66 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 57 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 83]]
classifier = DecisionTreeClassifier()
classifier.fit(x_train, y_train)
y_predict = classifier.predict(x_test)print(metrics.classification_report(y_test, y_predict, zero_division=0)) precision recall f1-score support
Air Fryer 1.00 1.00 1.00 34
Batedeira 0.93 1.00 0.96 38
Cafeteira 0.94 0.97 0.95 31
Espremedor de Frutas 1.00 1.00 1.00 29
Ferro Eletrico 0.94 0.94 0.94 36
Furadeira 1.00 1.00 1.00 34
Gelagua 0.97 1.00 0.98 29
Lampada Incandescente 1.00 1.00 1.00 159
Liquidificador 1.00 0.98 0.99 119
Maquina de Lavar 0.98 1.00 0.99 53
Micro-ondas 1.00 1.00 1.00 52
Prancha de Cabelo 1.00 0.96 0.98 48
Sanduicheira 1.00 0.98 0.99 94
Secador de Cabelo 0.97 0.97 0.97 67
TV 1.00 1.00 1.00 57
Ventilador 1.00 1.00 1.00 83
accuracy 0.99 963
macro avg 0.98 0.99 0.99 963
weighted avg 0.99 0.99 0.99 963
print(metrics.confusion_matrix(y_test, y_predict))[[ 34 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 38 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 30 0 0 0 0 0 0 1 0 0 0 0 0 0]
[ 0 0 0 29 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 34 0 0 0 0 0 0 0 0 2 0 0]
[ 0 0 0 0 0 34 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 29 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 159 0 0 0 0 0 0 0 0]
[ 0 1 0 0 0 0 1 0 117 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 53 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 52 0 0 0 0 0]
[ 0 2 0 0 0 0 0 0 0 0 0 46 0 0 0 0]
[ 0 0 2 0 0 0 0 0 0 0 0 0 92 0 0 0]
[ 0 0 0 0 2 0 0 0 0 0 0 0 0 65 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 57 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 83]]
classifier = KNeighborsClassifier()
classifier.fit(x_train, y_train)
y_predict = classifier.predict(x_test)print(metrics.classification_report(y_test, y_predict, zero_division=0)) precision recall f1-score support
Air Fryer 1.00 0.88 0.94 34
Batedeira 0.83 0.50 0.62 38
Cafeteira 0.76 0.90 0.82 31
Espremedor de Frutas 0.74 1.00 0.85 29
Ferro Eletrico 0.78 1.00 0.88 36
Furadeira 0.89 0.74 0.81 34
Gelagua 0.74 0.69 0.71 29
Lampada Incandescente 0.71 0.82 0.76 159
Liquidificador 1.00 0.91 0.95 119
Maquina de Lavar 0.76 0.83 0.79 53
Micro-ondas 1.00 1.00 1.00 52
Prancha de Cabelo 0.62 0.60 0.61 48
Sanduicheira 0.99 0.93 0.96 94
Secador de Cabelo 0.98 0.94 0.96 67
TV 1.00 0.88 0.93 57
Ventilador 0.51 0.51 0.51 83
accuracy 0.82 963
macro avg 0.83 0.82 0.82 963
weighted avg 0.83 0.82 0.82 963
print(metrics.confusion_matrix(y_test, y_predict))[[ 30 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0]
[ 0 19 0 0 0 0 4 6 0 0 0 3 0 0 0 6]
[ 0 0 28 0 0 0 0 0 0 3 0 0 0 0 0 0]
[ 0 0 0 29 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 36 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 4 0 25 0 0 0 5 0 0 0 0 0 0]
[ 0 1 0 0 0 0 20 3 0 0 0 1 0 0 0 4]
[ 0 0 0 0 0 0 0 130 0 0 0 3 0 0 0 26]
[ 0 0 0 5 0 3 0 0 108 3 0 0 0 0 0 0]
[ 0 0 9 0 0 0 0 0 0 44 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 52 0 0 0 0 0]
[ 0 2 0 0 0 0 1 11 0 0 0 29 0 0 0 5]
[ 0 0 0 0 3 0 0 0 0 3 0 0 87 1 0 0]
[ 0 0 0 0 3 0 0 0 0 0 0 0 1 63 0 0]
[ 0 0 0 1 0 0 1 0 0 0 0 5 0 0 50 0]
[ 0 1 0 0 0 0 1 33 0 0 0 6 0 0 0 42]]
classifier = SVC(kernel='linear', C=5.0)
classifier.fit(x_train, y_train)
y_predict = classifier.predict(x_test)print(metrics.classification_report(y_test, y_predict, zero_division=0)) precision recall f1-score support
Air Fryer 1.00 1.00 1.00 34
Batedeira 0.97 0.97 0.97 38
Cafeteira 1.00 0.94 0.97 31
Espremedor de Frutas 0.97 1.00 0.98 29
Ferro Eletrico 0.86 0.86 0.86 36
Furadeira 1.00 1.00 1.00 34
Gelagua 1.00 1.00 1.00 29
Lampada Incandescente 0.99 1.00 1.00 159
Liquidificador 1.00 0.99 1.00 119
Maquina de Lavar 0.96 1.00 0.98 53
Micro-ondas 1.00 1.00 1.00 52
Prancha de Cabelo 0.98 0.98 0.98 48
Sanduicheira 1.00 1.00 1.00 94
Secador de Cabelo 0.93 0.93 0.93 67
TV 1.00 1.00 1.00 57
Ventilador 1.00 0.99 0.99 83
accuracy 0.98 963
macro avg 0.98 0.98 0.98 963
weighted avg 0.98 0.98 0.98 963
print(metrics.confusion_matrix(y_test, y_predict))[[ 34 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 37 0 0 0 0 0 0 0 0 0 1 0 0 0 0]
[ 0 0 29 0 0 0 0 0 0 2 0 0 0 0 0 0]
[ 0 0 0 29 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 31 0 0 0 0 0 0 0 0 5 0 0]
[ 0 0 0 0 0 34 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 29 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 159 0 0 0 0 0 0 0 0]
[ 0 0 0 1 0 0 0 0 118 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 53 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 52 0 0 0 0 0]
[ 0 1 0 0 0 0 0 0 0 0 0 47 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 94 0 0 0]
[ 0 0 0 0 5 0 0 0 0 0 0 0 0 62 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 57 0]
[ 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 82]]
classifier = MLPClassifier(hidden_layer_sizes=(20, 20, 20))
classifier.fit(x_train, y_train)
y_predict = classifier.predict(x_test)/home/dev/.local/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
warnings.warn(
print(metrics.classification_report(y_test, y_predict, zero_division=0)) precision recall f1-score support
Air Fryer 1.00 1.00 1.00 34
Batedeira 0.97 0.76 0.85 38
Cafeteira 1.00 1.00 1.00 31
Espremedor de Frutas 0.97 1.00 0.98 29
Ferro Eletrico 0.75 1.00 0.86 36
Furadeira 0.91 0.91 0.91 34
Gelagua 0.72 0.97 0.82 29
Lampada Incandescente 0.98 0.99 0.98 159
Liquidificador 0.97 0.97 0.97 119
Maquina de Lavar 1.00 0.96 0.98 53
Micro-ondas 1.00 1.00 1.00 52
Prancha de Cabelo 0.94 0.94 0.94 48
Sanduicheira 0.98 1.00 0.99 94
Secador de Cabelo 1.00 0.82 0.90 67
TV 1.00 1.00 1.00 57
Ventilador 0.99 0.95 0.97 83
accuracy 0.96 963
macro avg 0.95 0.95 0.95 963
weighted avg 0.96 0.96 0.96 963
print(metrics.confusion_matrix(y_test, y_predict))[[ 34 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 29 0 0 0 0 9 0 0 0 0 0 0 0 0 0]
[ 0 0 31 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 29 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 36 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 31 0 0 3 0 0 0 0 0 0 0]
[ 0 1 0 0 0 0 28 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 157 0 0 0 2 0 0 0 0]
[ 0 0 0 1 0 3 0 0 115 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 51 0 0 2 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 52 0 0 0 0 0]
[ 0 0 0 0 0 0 2 0 0 0 0 45 0 0 0 1]
[ 0 0 0 0 0 0 0 0 0 0 0 0 94 0 0 0]
[ 0 0 0 0 12 0 0 0 0 0 0 0 0 55 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 57 0]
[ 0 0 0 0 0 0 0 3 0 0 0 1 0 0 0 79]]
# Splitting data sets for training
inputs = dataset.iloc[:, 1:3].values
outputs = dataset.iloc[:, 0].valuespca = PCA(n_components=2)
components = pca.fit_transform(StandardScaler().fit_transform(inputs))
targets = set(outputs)fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(1, 1, 1)
ax.set_title('Components PCA', fontsize=20)
ax.set_xlabel('Firs Component', fontsize=15)
ax.set_ylabel('Second Component', fontsize=15)
final_frame = pd.DataFrame(data=components, columns=['component_1', 'component_2'])
final_df = pd.concat([final_frame, dataset[['home_appliance']]], axis=1)
colors = ["lime", "grey", "maroon", "red", "m", "seagreen", "coral", "orange", "blue",
"pink", "c", "k", "darkblue", "y"]
for target, color in zip(targets, colors):
indices_to_keep = final_df['home_appliance'] == target
x = final_df.loc[indices_to_keep, 'component_1']
y = final_df.loc[indices_to_keep, 'component_2']
ax.scatter(x, y, c=color, s=50)
ax.legend(targets)
ax.grid()
plt.show()