Project

Critical Challenges

Increasing renewable generation adoption by buildings and communities increases variability and challenges traditional energy management paradigms. Rapid decarbonization relies on integrating renewable sources with the energy supply chain. Accurate prediction of end-use energy needs and self-production patterns is the key to the desired integration. However, traditional approaches for predicting electricity consumption and production are inadequate to achieve such an objective.

This proposal intends to address this gap by creating a collaborative prediction solution that improves prediction accuracy at the system level while preserving end-user customers’ privacy.

Approach 

This project intends to use a novel federated learning model for predicting building temporal presumption in transactive energy communities. The centralized coordinator (aggregator) has access to the historical net-metering profiles of the agents (buildings) and uses this information for feature extraction. 

The selected buildings locally compute an update to the model by executing the training program using their private data. Then, the aggregator collects the aggregated data and locally updates the shared model. Therefore, clients maintain their private data as confidential and simultaneously contribute to improving the prediction in their building and the community.

Contribution

The results of the developed models will ensure reliable and detailed previsions of the temporal presumption in buildings and communities. Such information will be crucial for optimizing energy resources in communities and smart cities. Put differently, data sharing empowers effective management of energy resources (such as battery energy storage and electric vehicles) and ensures economic and technical benefits for buildings, communities, and utilities.

This will provide an effective transition to future decarbonized energy systems with large-scale integration of renewable energy generation while ensuring a high-reliability level in the electric grid infrastructure.

At a Glance 

• Title: Distributed Machine Learning Solutions for Coordinating Distributed Energy Resources at the Edge of the Power Grid

• Reference: UTAP-EXPL/CA/0065/2021

• Scientific Area: Advanced Computing

• Funding (PT): 49 256,6 EUR

• Funding (US): 50 000 USD

• Leading Institutions: Institute for Systems and Robotics, University of Coimbra, PT; Department of Civil, Architectural and Environmental Engineering, Cockrell School of Engineering, UT Austin, USA

• Duration: 12 months

• Start date: March 1, 2022

• End date: February 28, 2023

• Keywords: Collaborative Learning, Distributed Computation, Transactive Energy, Community Microgrids