Can you describe your project in a few words?

The EMERGENT project aims to develop and demonstrate a user-driven Energy Management System (EMS) for energy efficiency in facilities. Specifically, EMERGENT leverages the available historical and real-time data related to a facility. It merges them with consumer-level energy demand forecasting and user feedback data to offer an EMS solution with higher levels of user acceptability and hence energy efficiency for facilities and buildings.

Who will help implement the AI solution?

Plegma Labs will implement the AI solution. Plegma Labs bridges protocol barriers and applies meaningful rules and workflows that add intelligence to its applications, leading to efficiency & optimization. Plegma Labs has substantial experience in AI & data analytics applications in energy, such as predictive maintenance, Photovoltaic (PV) generation forecasting, and energy optimization. Plegma's IoT platform product also includes data analytics services for Internet of Things (IoT) data, such as demand forecasting (https://pleg.ma/platform/) "smartCard-inline"

What is the AI solution the project plans to implement?

EMERGENT utilizes Reinforcement Learning (RL) techniques to train an agent that provides energy efficiency recommendations for specific facility assets, e.g. Heating, Ventilation and Air Conditioning (HVAC) and boilers. In addition, Deep Learning (DL) models are used to conduct high-granularity building energy demand forecasts, even in cases of limited historical data availability. The EMERGENT system continuously considers user feedback regarding energy efficiency recommendations to ensure the RL agent learns the end-user behaviour. The idea is to constantly train the RL agent for each facility with historical and real-time energy and non-energy data for the available assets.

The agent's goal is to produce asset/device re-scheduling recommendations for facility/building managers to minimize energy costs while considering explicit and implicit user feedback and preferences. The agent also finds high-granularity building-level energy demand forecasts to enhance its performance further.