Electricity Load Diagrams

Level: Hard
Goal: Model and forecast electricity demand (multiple time series, rich seasonality).

Dataset (Example Source)

• Source: Electricity load diagrams dataset (via libraries like GluonTS/HuggingFace, etc.)
• Example Link: https://huggingface.co/datasets/tulipa762/electricity_load_diagrams

Download (Simple Manual Approach)

1. Open the link above.
2. Download the CSV file containing the load data (if provided).
3. Save to data/electricity/.

Data Loading (If CSV is Available)

import pandas as pd

df = pd.read_csv("data/electricity/electricity.csv")  # adjust filename
print(df.head())

# Example: parse datetime column like "timestamp"
df["timestamp"] = pd.to_datetime(df["timestamp"])
df = df.set_index("timestamp").sort_index()

Implementation Steps

1. Data Loading and Exploration

• Load electricity load data
• Understand data structure (multiple consumers, time series)
• Inspect data frequency (hourly, daily)
• Examine data quality and missing values
• Identify available variables

2. Exploratory Data Analysis (EDA)

• Plot load time series (overall and by consumer if multiple)
• Identify strong seasonality patterns:
  • Daily patterns (hourly data)
  • Weekly patterns (weekday vs weekend)
  • Yearly patterns (seasonal variations)
• Analyze load distributions
• Calculate basic statistics
• Perform time series decomposition

3. Data Preprocessing

• Handle missing values
• Handle outliers (may be real events)
• Create time-based features (hour, day of week, month, season)
• Normalize or scale if needed
• Prepare data for modeling

4. Feature Engineering

• Time Features:
  • Hour of day, day of week, month, season
  • Holiday indicators
  • Weekend indicators
• Lag Features:
  • Previous hour, day, week loads
  • Same hour previous day, week
• Rolling Statistics:
  • Rolling mean, std (daily, weekly windows)
• External Features (if available):
  • Temperature, weather data
  • Economic indicators

5. Model Building

• Univariate Models (per consumer):
  • ARIMA/SARIMA (strong seasonality - multiple seasonal patterns)
  • Exponential smoothing (Holt-Winters with multiple seasonality)
• Multivariate Models:
  • VAR models for multiple consumers
  • Hierarchical models
• Machine Learning:
  • Feature-based models (XGBoost, LightGBM)
  • Handle multiple seasonality with features
• Advanced (if using libraries):
  • GluonTS, darts, or similar time series libraries
  • Deep learning models (LSTM, Transformer)

6. Model Evaluation

• Use time series cross-validation
• Split data temporally
• Calculate metrics (MAE, RMSE, MAPE)
• Evaluate at different time horizons (1-hour, 1-day, 1-week ahead)
• Compare multiple approaches

7. Forecasting

• Generate forecasts for different horizons
• Include prediction intervals
• Aggregate forecasts if multiple consumers
• Visualize forecasts with actual values
• Analyze forecast accuracy by time of day/day of week

8. Advanced Analysis (Optional)

• Analyze load patterns by consumer type
• Identify peak demand periods
• Analyze seasonal variations
• Compare different modeling approaches
• Analyze feature importance

Expected Deliverables

1. EDA Report:

   • Load time series plots
   • Seasonality analysis (daily, weekly, yearly)
   • Pattern identification
   • Statistical summaries

2. Model Results:

   • Selected model(s) with parameters
   • Performance metrics (by horizon)
   • Forecast plots
   • Comparison of approaches

3. Code:

   • Complete Python notebook
   • Data processing functions
   • Feature engineering utilities
   • Modeling pipeline

Tips

• Electricity load has very strong and complex seasonality (multiple patterns)
• Daily patterns are crucial (peak hours, off-peak hours)
• Weekly patterns (weekday vs weekend) are important
• Yearly patterns (summer vs winter) affect demand
• Multiple seasonality requires SARIMA with multiple seasonal components or feature engineering
• Consider using specialized time series libraries (GluonTS, darts) for complex seasonality
• Feature engineering is essential (time features, lags, rolling stats)
• External features (temperature) can significantly improve forecasts
• Handle multiple consumers appropriately (panel data structure)
• Use appropriate evaluation metrics and cross-validation
• Document all preprocessing and feature engineering steps
• Compare simple models (ARIMA) with complex models (XGBoost, LSTM)
• Consider ensemble methods for better performance
• This is a challenging problem - expect to try multiple approaches