Research on Power Load Forecasting based on Machine learning

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# Import library yang diperlukan import pandas as pd

import numpy as np

from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression

from sklearn.metrics import mean_squared_error, r2_score import matplotlib.pyplot as plt

# Baca dataset (pastikan sesuaikan path file) data = pd.read_csv('path/to/your/dataset.csv')

# Melihat beberapa baris pertama dari dataset print(data.head())

# Pilih fitur dan target

X = data[['Feature1', 'Feature2', 'Feature3']] # Sesuaikan dengan nama fitur pada dataset Anda

y = data['Power_Load'] # Sesuaikan dengan nama target pada dataset Anda

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# Bagi dataset menjadi data pelatihan dan data pengujian

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Membuat model regresi linier model = LinearRegression()

# Melatih model

model.fit(X_train, y_train)

# Memprediksi power load untuk data pengujian y_pred = model.predict(X_test)

# Evaluasi model

mse = mean_squared_error(y_test, y_pred) r2 = r2_score(y_test, y_pred)

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print(f'Mean Squared Error: {mse}') print(f'R-squared: {r2}')

# Visualisasi hasil prediksi

plt.scatter(X_test['Feature1'], y_test, color='black', label='Actual Power Load')

plt.scatter(X_test['Feature1'], y_pred, color='blue', label='Predicted Power Load') plt.xlabel('Feature1')

plt.ylabel('Power Load') plt.legend()

plt.show()