Electricity Demand Forecasting
1. Introduction
Objective: Forecast electricity demand using historical data, seasonal
decomposition, and machine learning models.
Purpose: Aid utility companies in efficient energy management and load planning
by leveraging data-driven forecasting.
2. Project Workflow
1. Problem Definition:
- Predict electricity demand using
historical consumption data.
- Key questions:
- What are the seasonal patterns in
electricity demand?
- How accurately can we forecast
future demand?
2. Data Collection:
- Source: Public electricity
consumption datasets, utility records, or APIs.
3. Data Preprocessing:
- Handle missing values, outliers, and
format time series data.
4. Exploratory Data Analysis:
- Analyze trends, seasonality, and
residuals using decomposition.
5. Modeling:
- Use ML models (e.g., ARIMA, Random
Forest, LSTM) for demand forecasting.
6. Evaluation and Insights:
- Evaluate the model's performance and
provide actionable insights.
3. Technical Requirements
- Programming Language: Python
- Libraries/Tools:
- Data Handling: Pandas, NumPy
- Visualization: Matplotlib, Seaborn,
Plotly
- Time Series Analysis: statsmodels,
scikit-learn
- Machine Learning: TensorFlow/Keras,
XGBoost
4. Implementation Steps
Step 1: Setup Environment
Install required libraries:
```
pip install pandas numpy matplotlib seaborn statsmodels scikit-learn tensorflow
xgboost
```
Step 2: Collect and Explore Data
Load electricity consumption data:
```
import pandas as pd
data = pd.read_csv("electricity_demand.csv",
parse_dates=['Timestamp'], index_col='Timestamp')
print(data.head())
```
Inspect the dataset:
```
print(data.info())
print(data.describe())
```
Step 3: Perform Seasonal Decomposition
Decompose the time series to analyze trend, seasonality, and residuals:
```
from statsmodels.tsa.seasonal import seasonal_decompose
decomposition = seasonal_decompose(data['Demand'], model='additive')
decomposition.plot()
plt.show()
```
Step 4: Preprocess Data for ML Models
Create features and labels for ML models:
```
data['Hour'] = data.index.hour
data['Day'] = data.index.day
data['Month'] = data.index.month
X = data[['Hour', 'Day', 'Month']]
y = data['Demand']
```
Split data into training and testing sets:
```
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=42)
```
Step 5: Train Machine Learning Models
Train a Random Forest model:
```
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
model = RandomForestRegressor()
model.fit(X_train, y_train)
predictions = model.predict(X_test)
mse = mean_squared_error(y_test, predictions)
print(f'Mean Squared Error: {mse}')
```
Step 6: Visualize Forecasts
Plot actual vs. predicted demand:
```
import matplotlib.pyplot as plt
plt.plot(y_test.values[:100], label='Actual')
plt.plot(predictions[:100], label='Predicted')
plt.title('Electricity Demand Forecasting')
plt.legend()
plt.show()
```
5. Expected Outcomes
1. Seasonal decomposition plots showing trends and patterns in electricity
demand.
2. A machine learning model capable of accurately forecasting electricity
demand.
3. Insights into demand patterns and recommendations for energy management.
6. Additional Suggestions
- Use advanced models like LSTM or Prophet for more accurate forecasts.
- Incorporate external factors (e.g., temperature, holidays) for improved
predictions.
- Build a dynamic dashboard to monitor real-time forecasts and trends.