End User
City of Athens
Description
The PV Self-Consumption Optimization service uses AI to maximize the use of locally generated solar energy within municipal buildings. By analyzing real-time consumption, historical PV production and energy consumption data, it identifies optimal load-shifting strategies to increase solar self-consumption and reduce reliance on grid electricity. The service recommends adjustments in building operations and controllable loads to align energy usage with PV generation peaks. This helps municipalities lower energy costs, improve sustainability performance, and enhance building energy autonomy.
Core Capabilities
Monitoring & Anomaly Detection
Predictive & Prescriptive Analytics
Optimization & Decision Support
Business Need
Maximizes the use of on-site solar energy, reducing the need to purchase grid electricity, supports sustainability and climate goals by reducing carbon emissions associated with external energy consumption, enhances building energy autonomy and resilience against electricity price volatility or grid disturbances, aligns with sustainability goals by reducing reliance on fossil-fuel-based peak generation, optimizes operational schedules to better match energy demand with PV generation peaks.
Key Performance Indicators
Self-Consumption Rate (%)-Percentage of PV-generated electricity used on-site
Improvement Over Baseline-Increase in self-consumption compared to current operation
Financial savings from the increase of self-consumption
Data Provided
Building Metadata (size, building category, year of construction) - 500 buildings with the potential for expansion -no documentation-static data
PV System Data (installed capacity, annual production, PV area used) - 500 buildings – 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 (weather data, calendar data, energy market prices)
Synthetic dataset of PV production – simulate PV production in hourly basis considering yearly PV data and weather data
TEF
TEF BUILD