Data-driven predictive control for unlocking building energy flexibility: A review
Reads0
Chats0
TLDR
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.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.read more
Citations
More filters
Journal ArticleDOI
Energy flexibility of residential buildings: A systematic review of characterization and quantification methods and applications
TL;DR: This paper reviews recent studies on residential building demand side management, with a focus on characterization and quantification of energy flexibility covering various types of flexible loads, metrics, methods, and applications, and reveals research opportunities to address significant gaps in the existing literature.
Journal ArticleDOI
Transfer learning for smart buildings: A critical review of algorithms, applications, and future perspectives
TL;DR: In this paper , a comprehensive overview of transfer learning applications in smart buildings, classifying and analyzing 77 papers according to their applications, algorithms, and adopted metrics, is presented, highlighting the role of deep learning in transfer learning in smart building applications.
Journal ArticleDOI
Hourly energy consumption prediction of an office building based on ensemble learning and energy consumption pattern classification
TL;DR: This study presents a prediction strategy of building energy consumption based on ensemble learning and energy consumption patterns classification and illustrates that the proposed strategy is reliable and effective and can obtain acceptable performance with less training data, which is helpful to the application of energy consumption prediction.
Journal ArticleDOI
Physics-constrained deep learning of multi-zone building thermal dynamics
TL;DR: The proposed physics-constrained control-oriented deep learning method incorporates structural priors from traditional physics-based building modeling into the neural network thermal dynamics model structure, thereby bounding predictions within physically realistic and safe operating ranges.
Journal ArticleDOI
Deep reinforcement learning control of electric vehicle charging in the presence of photovoltaic generation
TL;DR: The benchmarking of the deep RL control against naive, rule-based, deterministic optimization, and model-predictive control demonstrates that the suggested methodology can produce consistent and employable EV charging strategies, while its performance holds a great promise for real-time implementations.
References
More filters
Book
Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Proceedings ArticleDOI
Cyber Physical Systems: Design Challenges
TL;DR: It is concluded that it will not be sufficient to improve design processes, raise the level of abstraction, or verify designs that are built on today's abstractions to realize the full potential of cyber-Physical Systems.
Journal ArticleDOI
Review of energy system flexibility measures to enable high levels of variable renewable electricity
TL;DR: In this paper, the authors review different approaches, technologies, and strategies to manage large-scale schemes of variable renewable electricity such as solar and wind power, considering both supply and demand side measures.
Journal ArticleDOI
Use of model predictive control and weather forecasts for energy efficient building climate control
Frauke Oldewurtel,Alessandra Parisio,Colin N. Jones,Dimitrios Gyalistras,Markus Gwerder,Vanessa J. Stauch,Beat Lehmann,Manfred Morari +7 more
TL;DR: In this paper, the authors investigated how ModelPredictive control and weatherpredictions can increase the energy efficiency in Integrated Room Automation (IRA) while respecting occupant comfort.
Journal ArticleDOI
Electricity price forecasting: A review of the state-of-the-art with a look into the future
TL;DR: In this paper, a review article aims to explain the complexity of available solutions, their strengths and weaknesses, and the opportunities and threats that the forecasting tools offer or that may be encountered.