Top 10 Data Science Projects For Beginners with PDF

Project 10:Energy Consumption Forecasting with Time-Series Analysis In Data Science

Project Description

This  Data Science project involves forecasting energy consumption using time-series analysis techniques. The goal is to predict future energy usage based on historical consumption data. Accurate energy consumption forecasts are crucial for optimizing energy production, reducing waste, and planning infrastructure. Time-series forecasting methods like ARIMA, SARIMA, and LSTM (Long Short-Term Memory) models will be applied to historical energy usage data to predict future demand. This project will demonstrate how predictive models can be leveraged to make data-driven decisions in energy management.

Skills Required

1. Time-Series Analysis:
   • Proficiency in time-series forecasting techniques like ARIMA, SARIMA, and exponential smoothing.
2. Machine Learning:
   • Familiarity with machine learning models, particularly LSTM networks for time-series forecasting.
3. Data Analysis:
   • Skilled in using Python libraries like Pandas and NumPy for data manipulation.
4. Visualization:
   • Familiarity with Matplotlib and Seaborn for visualizing time-series data and extracting insights.
5. Forecasting and Evaluation:
   • Experience in evaluating model accuracy using metrics like RMSE, MAE, and MAPE.

Steps to Execute the Project

Step 1: Data Collection

• Collect historical energy consumption data from publicly available sources or use datasets like the UCI Machine Learning Repository or Kaggle.
• The dataset should include hourly, daily, or monthly energy usage, along with any relevant external factors (e.g., temperature, holidays, or industrial activity).

python

import pandas as pd

# Load energy consumption dataset

data = pd.read_csv(‘energy_consumption.csv’, parse_dates=[‘Date’], index_col=’Date’)

print(data.head())

Step 2: Data Preprocessing

• Handle missing values, convert date columns to datetime objects, and ensure data is properly formatted for time-series analysis.

python

# Fill missing values with forward fill method

data.fillna(method=’ffill’, inplace=True)

# Check for missing data and outliers

data.plot()

Step 3: Exploratory Data Analysis (EDA)

• Visualize energy consumption patterns over time.
• Decompose the time-series data to understand trends, seasonality, and noise.

python

import matplotlib.pyplot as plt

from statsmodels.tsa.seasonal import seasonal_decompose

# Decompose the time series

decomposed = seasonal_decompose(data[‘Consumption’], model=’additive’, period=365)

decomposed.plot()

plt.show()

# Visualize the energy consumption over time

data[‘Consumption’].plot(figsize=(12, 6))

plt.title(“Energy Consumption Over Time”)

plt.xlabel(“Date”)

plt.ylabel(“Consumption”)

plt.show()

Step 4: Time-Series Forecasting with ARIMA

• Utilize ARIMA (AutoRegressive Integrated Moving Average) to model and analyze time series data.
• Determine the optimal parameters (p, d, q) using techniques like ACF (AutoCorrelation Function) and PACF (Partial AutoCorrelation Function).

python

from statsmodels.tsa.arima.model import ARIMA

# Train-test split (80-20%)

train_data, test_data = data[‘Consumption’][:int(0.8*len(data))], data[‘Consumption’][int(0.8*len(data)):]

# Fit ARIMA model (p=1, d=1, q=1 as an example)

model = ARIMA(train_data, order=(1, 1, 1))

model_fit = model.fit()

# Forecast on test data

forecast = model_fit.forecast(steps=len(test_data))

plt.plot(test_data.index, test_data, label=’Actual’)

plt.plot(test_data.index, forecast, label=’Forecasted’)

plt.legend()

plt.title(“ARIMA Forecasting: Energy Consumption”)

plt.show()

Step 5: Model Evaluation

• Evaluate the model performance using error metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE).

python

from sklearn.metrics import mean_squared_error, mean_absolute_error

import numpy as np

# Evaluate using RMSE and MAE

rmse = np.sqrt(mean_squared_error(test_data, forecast))

mae = mean_absolute_error(test_data, forecast)

print(f”RMSE: {rmse}, MAE: {mae}”)

Step 6: Advanced Model – LSTM (Long Short-Term Memory)

• Implement LSTM, a type of neural network designed for time-series forecasting.
• Preprocess data for LSTM input, which requires reshaping the data into sequences.

python

from keras.models import Sequential

from keras.layers import LSTM, Dense

from sklearn.preprocessing import MinMaxScaler

# Scale the data

scaler = MinMaxScaler(feature_range=(0, 1))

scaled_data = scaler.fit_transform(data[‘Consumption’].values.reshape(-1, 1))

# Prepare data for LSTM model (creating sequences)

X, y = [], []

for i in range(60, len(scaled_data)):

    X.append(scaled_data[i-60:i, 0])

    y.append(scaled_data[i, 0])

X = np.array(X)

y = np.array(y)

# Reshape data to be compatible with LSTM input

X = np.reshape(X, (X.shape[0], X.shape[1], 1))

# Build the LSTM model

model = Sequential()

model.add(LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)))

model.add(LSTM(units=50))

model.add(Dense(units=1))

model.compile(optimizer=’adam’, loss=’mean_squared_error’)

# Train the model

model.fit(X, y, epochs=10, batch_size=32)

# Predict the future values

predicted_consumption = model.predict(X)

predicted_consumption = scaler.inverse_transform(predicted_consumption)

# Plot predictions vs actual consumption

plt.plot(data.index[60:], data[‘Consumption’][60:], label=’Actual’)

plt.plot(data.index[60:], predicted_consumption, label=’Predicted’)

plt.legend()

plt.title(“LSTM Forecasting: Energy Consumption”)

plt.show()

Step 7: Forecasting Future Energy Consumption

• Forecast energy consumption for the next 30 days or longer based on the trained model.

python

# Forecast future energy consumption using ARIMA

future_forecast = model_fit.forecast(steps=30)

future_dates = pd.date_range(start=data.index[-1], periods=30, freq=’D’)

plt.plot(future_dates, future_forecast, label=’Forecasted Future Consumption’, color=’red’)

plt.legend()

plt.title(“Future Energy Consumption Forecasting (ARIMA)”)

plt.show()

Expected Outcomes

• A time-series forecasting model (ARIMA or LSTM) capable of predicting future energy consumption.
• Visualization of historical energy consumption trends, seasonal patterns, and forecast accuracy.
• Model performance evaluation using error metrics like RMSE, MAE, and MAPE.
• Practical experience in handling time-series data and forecasting using classical and deep learning methods.