Lessons Learned from Data-Driven Building Control Experiments:
Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement
Learning
- URL: http://arxiv.org/abs/2205.15703v1
- Date: Tue, 31 May 2022 11:40:22 GMT
- Title: Lessons Learned from Data-Driven Building Control Experiments:
Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement
Learning
- Authors: Loris Di Natale, Yingzhao Lian, Emilio T. Maddalena, Jicheng Shi and
Colin N. Jones
- Abstract summary: This manuscript offers the perspective of experimentalists on a number of modern data-driven techniques.
It is compared in terms of data requirements, ease of use, computational burden, and robustness in the context of real-world applications.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This manuscript offers the perspective of experimentalists on a number of
modern data-driven techniques: model predictive control relying on Gaussian
processes, adaptive data-driven control based on behavioral theory, and deep
reinforcement learning. These techniques are compared in terms of data
requirements, ease of use, computational burden, and robustness in the context
of real-world applications. Our remarks and observations stem from a number of
experimental investigations carried out in the field of building control in
diverse environments, from lecture halls and apartment spaces to a hospital
surgery center. The final goal is to support others in identifying what
technique is best suited to tackle their own problems.
Related papers
- A Review of Machine Learning Techniques in Imbalanced Data and Future
Trends [0.0]
We have collected and reviewed 258 peer-reviewed papers from archival journals and conference papers.
This work aims to provide a structured review of methods used to address the problem of imbalanced data in various domains.
arXiv Detail & Related papers (2023-10-11T22:14:17Z) - Instruction Tuning for Large Language Models: A Survey [52.86322823501338]
We make a systematic review of the literature, including the general methodology of IT, the construction of IT datasets, the training of IT models, and applications to different modalities, domains and applications.
We also review the potential pitfalls of IT along with criticism against it, along with efforts pointing out current deficiencies of existing strategies and suggest some avenues for fruitful research.
arXiv Detail & Related papers (2023-08-21T15:35:16Z) - Pitfalls in Experiments with DNN4SE: An Analysis of the State of the
Practice [0.7614628596146599]
We conduct a mapping study, examining 194 experiments with techniques that rely on deep neural networks appearing in 55 papers published in premier software engineering venues.
Our study reveals that most of the experiments, including those that have received ACM artifact badges, have fundamental limitations that raise doubts about the reliability of their findings.
arXiv Detail & Related papers (2023-05-19T09:55:48Z) - Efficient Deep Reinforcement Learning Requires Regulating Overfitting [91.88004732618381]
We show that high temporal-difference (TD) error on the validation set of transitions is the main culprit that severely affects the performance of deep RL algorithms.
We show that a simple online model selection method that targets the validation TD error is effective across state-based DMC and Gym tasks.
arXiv Detail & Related papers (2023-04-20T17:11:05Z) - Physics-Informed Kernel Embeddings: Integrating Prior System Knowledge
with Data-Driven Control [22.549914935697366]
We present a method to incorporate priori knowledge into data-driven control algorithms using kernel embeddings.
Our proposed approach incorporates prior knowledge of the system dynamics as a bias term in the kernel learning problem.
We demonstrate the improved sample efficiency and out-of-sample generalization of our approach over a purely data-driven baseline.
arXiv Detail & Related papers (2023-01-09T18:35:32Z) - What and How of Machine Learning Transparency: Building Bespoke
Explainability Tools with Interoperable Algorithmic Components [77.87794937143511]
This paper introduces a collection of hands-on training materials for explaining data-driven predictive models.
These resources cover the three core building blocks of this technique: interpretable representation composition, data sampling and explanation generation.
arXiv Detail & Related papers (2022-09-08T13:33:25Z) - Context-aware controller inference for stabilizing dynamical systems
from scarce data [0.0]
This work introduces a data-driven control approach for stabilizing high-dimensional dynamical systems from scarce data.
The proposed context-aware controller inference approach is based on the observation that controllers need to act locally only on the unstable dynamics to stabilize systems.
arXiv Detail & Related papers (2022-07-22T12:41:53Z) - An Extensible Benchmark Suite for Learning to Simulate Physical Systems [60.249111272844374]
We introduce a set of benchmark problems to take a step towards unified benchmarks and evaluation protocols.
We propose four representative physical systems, as well as a collection of both widely used classical time-based and representative data-driven methods.
arXiv Detail & Related papers (2021-08-09T17:39:09Z) - Data and its (dis)contents: A survey of dataset development and use in
machine learning research [11.042648980854487]
We survey the many concerns raised about the way we collect and use data in machine learning.
We advocate that a more cautious and thorough understanding of data is necessary to address several of the practical and ethical issues of the field.
arXiv Detail & Related papers (2020-12-09T22:13:13Z) - Monitoring and explainability of models in production [58.720142291102135]
Monitoring deployed models is crucial for continued provision of high quality machine learning enabled services.
We discuss the challenges to successful implementation of solutions in each of these areas with some recent examples of production ready solutions using open source tools.
arXiv Detail & Related papers (2020-07-13T10:37:05Z) - How Training Data Impacts Performance in Learning-based Control [67.7875109298865]
This paper derives an analytical relationship between the density of the training data and the control performance.
We formulate a quality measure for the data set, which we refer to as $rho$-gap.
We show how the $rho$-gap can be applied to a feedback linearizing control law.
arXiv Detail & Related papers (2020-05-25T12:13:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.