Data-driven Predictive Control for Unlocking Building Energy Flexibility: A Review

TL;DR

This review explores how data-driven predictive control, integrated with IoT, can enhance building energy flexibility for smart grids by addressing modeling challenges and control strategies.

Contribution

It provides a comprehensive analysis of recent data-driven predictive control methods for demand side management, focusing on model development and control integration.

Findings

Data-driven models can replace traditional physics-based models.

Effective feature selection is crucial for control performance.

Identifies gaps and future directions in data-driven building energy management.

Abstract

Managing supply and demand in the electricity grid is becoming more challenging due to the increasing penetration of variable renewable energy sources. As significant end-use consumers, and through better grid integration, buildings are expected to play an expanding role in the future smart grid. Predictive control allows buildings to better harness available energy flexibility from the building passive thermal mass. However, due to the heterogeneous nature of the building stock, developing computationally tractable control-oriented models, which adequately represent the complex and nonlinear thermal-dynamics of individual buildings, is proving to be a major hurdle. Data-driven predictive control, coupled with the "Internet of Things", holds the promise for a scalable and transferrable approach,with data-driven models replacing traditional physics-based models. This review examines recent work utilising data-driven predictive control for demand side management application with a special focus on the nexus of model development and control integration, which to date, previous reviews have not addressed. Further topics examined include the practical requirements for harnessing passive thermal mass and the issue of feature selection. Current research gaps are outlined and future research pathways are suggested to identify the most promising data-driven predictive control techniques for grid integration of buildings.