Author: Sam Ginzburg
Email: [email protected]
Tech Stack: Python · Pandas · NumPy · Statsmodels · Matplotlib · Time Series Analysis · SARIMA
Accurate short-term power consumption forecasting is critical for grid operators, utilities, and energy planners to ensure reliability, control costs, and anticipate demand spikes driven by daily human activity and seasonality.
This project demonstrates how a data analyst approaches real-world time-series forecasting, where data exhibits:
- Strong intraday seasonality
- Non-stationary trends
- Structured noise and occasional outliers
- The need for interpretability and diagnostic rigor, not just point forecasts
Rather than treating forecasting as a black-box exercise, this notebook emphasizes model selection logic, statistical validation, and transparency—skills that translate directly to production analytics and forecasting roles.
This project delivers an end-to-end time-series analysis and forecasting workflow that:
- Explores raw power consumption data and underlying patterns
- Diagnoses trend and seasonal structure using decomposition
- Applies seasonal and regular differencing to achieve stationarity
- Uses ACF / PACF diagnostics to guide model specification
- Trains and evaluates a SARIMA model for short-horizon forecasting
- Compares forecasts against actuals with uncertainty bounds
- Communicates results visually and analytically
The scope is intentionally focused on methodological correctness and interpretability, not brute-force optimization.
This project highlights the following analytical and technical competencies:
- Time-Series Analysis
- Stationarity assessment
- Seasonal decomposition (trend / seasonality / residuals)
- Regular and seasonal differencing
- Statistical Modeling
- SARIMA model specification and fitting
- ACF / PACF interpretation for AR & MA term selection
- Model Diagnostics
- Residual behavior analysis
- Over-differencing avoidance
- Forecast uncertainty interpretation
- Python & Data Handling
- Pandas time-indexed transformations
- Numerical analysis with NumPy
- Visualization & Communication
- Clear, decision-oriented plots using Matplotlib
- Side-by-side forecast vs. actual comparisons
- Production-Minded Thinking
- Rolling forecast windows
- Explicit modeling assumptions and limitations
The original series exhibits strong daily seasonality and a slowly evolving trend. Seasonal decomposition cleanly separates these components, validating the need for a seasonal forecasting model.
Seasonal differencing at lag = 24 removes the dominant intraday cycle. A single regular difference addresses remaining trend behavior without over-differencing.
The final differenced series demonstrates stable mean and variance, confirming readiness for SARIMA modeling.
Model parameters were selected based on diagnostic evidence, not trial-and-error:
- Seasonal structure identified via autocorrelation at lag 24
- Non-seasonal AR/MA terms guided by PACF and ACF cutoff behavior
- Emphasis on parsimony and interpretability over overfitting
This mirrors how time-series models are justified in professional forecasting environments.
The trained SARIMA model generates short-term forecasts that closely track observed consumption, including peak and trough demand periods.
Confidence intervals widen appropriately with forecast horizon, reflecting realistic uncertainty rather than overconfident point predictions.
The final SARIMA model successfully:
- Captures daily power consumption cycles with high fidelity
- Produces stable short-horizon forecasts suitable for operational planning
- Maintains interpretable parameters aligned with observed data behavior
- Demonstrates disciplined time-series modeling best practices
This project highlights how a data analyst reasons through forecasting problems, not just how a model is fit.
Power_Consumption_Forecasting/
│
├── notebooks/
│ └── time_series_analysis.ipynb # Full exploratory analysis & SARIMA modeling
├── data/
│ └── powerconsumption.csv
├── assets/
│ └── screenshots/ # Optional visuals for README
├── requirements.txt
└── README.md
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Clone the repository
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Install dependencies:
pip install -r requirements.txt- Open the notebook:
jupyter notebook notebooks/time_series_analysis.ipynb- Forecasts are short-term and optimized for intraday horizons
- External drivers (weather, holidays, macro events) are not explicitly modeled
- Model is univariate by design to emphasize statistical clarity
This project is intended for educational and analytical demonstration purposes only. It does not represent operational forecasting guidance for real-world energy systems.