End User
City of Athens
Description
Building Energy Consumption Forecasting service uses historical energy bills, building characteristics, real-time data from smart meters and seasonal patterns to forecast electricity consumption for municipal buildings. Leveraging machine learning models, it provides accurate short-term and mid-term forecasts. This service helps municipalities anticipate energy needs, optimize procurement, and identify potential inefficiencies before they escalate, supporting smarter and more sustainable energy management across the building portfolio.
Core Capabilities
Predictive & Prescriptive Analytics
Optimization & Decision Support
Business Need
Enables accurate cost forecasting, detects abnormal or unexpected energy consumption trends early, supports proactive energy management and procurement strategies.
Key Performance Indicators
MAE, MAPE, RMSE, Energy Consumption Variance
Data Provided
Building Metadata (size, building category, year of construction) - 500 buildings with the potential for expansion -no documentation-static data
Historical Energy Consumption Bills – 500 buildings – no documentation – monthly
Historical Energy Consumption – 50 buildings with the potential to add more as smart meter installations progress – no documentation – hourly
External datasets (calendar days)
TEF
TEF BUILD