4 research outputs found
Distributed Control of Multi-zone HVAC Systems Considering Indoor Air Quality
This paper studies a scalable control method for multi-zone heating,
ventilation and air-conditioning (HVAC) systems to optimize the energy cost for
maintaining thermal comfort and indoor air quality (IAQ) (represented by CO2)
simultaneously. This problem is computationally challenging due to the complex
system dynamics, various spatial and temporal couplings as well as multiple
control variables to be coordinated. To address the challenges, we propose a
two-level distributed method (TLDM) with a upper level and lower level control
integrated. The upper level computes zone mass flow rates for maintaining zone
thermal comfort with minimal energy cost, and then the lower level
strategically regulates zone mass flow rates and the ventilation rate to
achieve IAQ while preserving the near energy saving performance of upper level.
As both the upper and lower level computation are deployed in a distributed
manner, the proposed method is scalable and computationally efficient. The
near-optimal performance of the method in energy cost saving is demonstrated
through comparison with the centralized method. In addition, the comparisons
with the existing distributed method show that our method can provide IAQ with
only little increase of energy cost while the latter fails. Moreover, we
demonstrate our method outperforms the demand controlled ventilation strategies
(DCVs) for IAQ management with about 8-10% energy cost reduction.Comment: 12 pages, 12 figure
A Proximal Linearization-based Decentralized Method for Nonconvex Problems with Nonlinear Constraints
Decentralized optimization for non-convex problems are now demanding by many
emerging applications (e.g., smart grids, smart building, etc.). Though
dramatic progress has been achieved in convex problems, the results for
non-convex cases, especially with non-linear constraints, are still largely
unexplored. This is mainly due to the challenges imposed by the non-linearity
and non-convexity, which makes establishing the convergence conditions
bewildered. This paper investigates decentralized optimization for a class of
structured non-convex problems characterized by: (i) nonconvex global objective
function (possibly nonsmooth) and (ii) coupled nonlinear constraints and local
bounded convex constraints w.r.t. the agents. For such problems, a
decentralized approach called Proximal Linearizationbased Decentralized Method
(PLDM) is proposed. Different from the traditional (augmented) Lagrangian-based
methods which usually require the exact (local) optima at each iteration, the
proposed method leverages a proximal linearization-based technique to update
the decision variables iteratively, which makes it computationally efficient
and viable for the non-linear cases. Under some standard conditions, the PLDM
global convergence and local convergence rate to the epsilon-critical points
are studied based on the Kurdyka-Lojasiewicz property which holds for most
analytical functions. Finally, the performance and efficacy of the method are
illustrated through a numerical example and an application to multi-zone
heating, ventilation and air-conditioning (HVAC) control.Comment: 17 pages, 5 figure
Occupancy Estimation and Activity Recognition in Smart Buildings using Mixture-Based Predictive Distributions
Labeled data is a necessary part of modern computer science, such as machine learning and deep learning. In that context, large amount of labeled training data is required. However, collecting of labeled data as a crucial step is time consuming, error prone and often requires people involvement. On the other hand, imbalanced data is also a challenge for classification approaches. Most approaches simply predict the majority class in all cases.
In this work, we proposed several frameworks about mixture models based predictive distribution. In the case of small training data, predictive distribution is data-driven, which can take advantage of the existing training data at its maximum and don't need many labeled data. The flexibility and adaptability of Dirichlet family distribution as mixture models further improve classification ability of frameworks.
Generalized inverted Dirichlet (GID), inverted Dirichlet (ID) and generalized Dirichlet (GD) are used in this work with predictive distribution to do classification. GID-based predictive distribution has an obvious increase for activity recognition compared with the approach of global variational inference using small training data. ID-based predictive distribution with over-sampling is applied in occupancy estimation. More synthetic data are sampling for small classes. The total accuracy is improved in the end. An occupancy estimation framework is presented based on interactive learning and predictive distribution of GD. This framework can find the most informative unlabeled data and interact with users to get the true label. New labeled data are added in data store to further improve the performance of classification