Forecasting
How to Build an Energy Demand Forecast
Energy demand forecasting estimates future load from historical consumption, calendar, weather and operational variables. A successful model does more than minimise error: it provides reliable and explainable output at the decision maker's required horizon.
Data preparation comes before modelling
Missing intervals, meter resets, daylight-saving changes and outliers should be identified. Training data must represent current operating conditions. Structural changes such as maintenance shutdowns or capacity expansions can lead to misleading generalisation unless explicitly modelled.
Choose the right variables and horizon
Hour, day type, holidays, temperature and recent load are strong variables for hourly forecasts. Industrial models should also incorporate production schedules, shifts and order volumes. Day-ahead procurement and annual budgeting require different horizons, models and error metrics.
Monitor model performance
MAE, RMSE and percentage-based measures capture different error behaviour. Results should be tested against a simple baseline and monitored for data drift over time. PowerForecast is designed to connect forecasting with operational planning and deviation tracking.
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