Build Accurate Energy Consumption Prediction Model for Utilities
Overview
This question evaluates a data scientist's mastery of end-to-end supervised regression for time-indexed panel data, including time-series and seasonal feature engineering, weather-dependent modeling, robust baselines, time-aware cross-validation, residual analysis, leakage prevention, and production concerns such as deployment, monitoring, and retraining triggers. It is commonly asked in Machine Learning interviews to assess practical application skills in building production-ready predictive systems rather than purely conceptual understanding, emphasizing model validation, operational monitoring, and handling of panel/time-series challenges.
Predicting Daily Energy Consumption: End-to-End Regression to Production
Context
You need to build and productionize a supervised regression model that predicts next-day daily energy consumption (kWh) for utility clients (a panel of customers/meters). The data is time-indexed and exhibits strong seasonality and weather dependence.
Task
Describe, in order, the exact steps you would take from raw data to a monitored production system, covering:
- Data acquisition and definition of the prediction problem
- Data quality checks and exploratory data analysis (EDA)
- Feature engineering
- Baselines and model selection
- Training, validation, and cross-validation design
- Model evaluation and residual analysis
- Productionization (pipelines, deployment)
- Monitoring, alerting, and retraining triggers
Requirements
- Mention strong baselines (e.g., yesterday/last-week, seasonal averages)
- Use appropriate cross-validation for time series/panel data
- Address feature scaling and encoding
- Perform residual analysis and guard against leakage
- Define concrete retraining triggers
Solution
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