Computer · Data Science
Time-Series Forecasting: Energy Load Prediction (LSTM)
Intermediate 5 weeks 35-50 hours
Build LSTM model predicting hourly electricity load from historical data. Validate on test set. Deploy prediction API. Show accuracy and error analysis.
Major
Computer
Focus area
Data Science
Total hours
35-50 hours
What you'll need
- Software only - Python, TensorFlow, Prophet, scikit-learn, public energy datasets
Steps
- Download a public hourly electricity load dataset.
- Clean the data and add features like hour, day, and temperature.
- Train an LSTM model and a Prophet baseline.
- Compare accuracy on a test set.
- Deploy the best model as a simple prediction API.
- Document the error analysis and results for your portfolio.
What to photograph for your portfolio
- Forecasting accuracy graphs
- RMSE/MAE metrics
- predicted vs actual plots
- residual analysis
Resume bullet starters
Copy one, then swap in your own numbers.
Engineered a time-series forecasting: energy load prediction using Python, TensorFlow/PyTorch, time-series analysis
Applied model evaluation and API development to implement, test, and validate the analysis end-to-end
Quantified performance with [insert your result, such as accuracy, error reduction, response time, or load capacity] after iterative tuning
Skills you'll show off
PythonTensorFlow/PyTorchtime-series analysismodel evaluationAPI developmentARIMA/Prophet