Evaluation of optimal control for active and passive building thermal storage

By Gregor P. Henze, C. Felsmann, G. Knabe. 2004

355 citations recorded by the source corpus.

Subfields: Building and Construction, Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/24679cbfc1249e70229dc8e939dabd3c3f04ad81

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In Building and Construction

In Control and Systems Engineering

In Electrical and Electronic Engineering

Citations in the corpus

Demand Response-Enabled Model Predictive HVAC Load Control in Buildings using Real-Time Electricity Pricing (2013, background): “Their analysis shows that the combined utilization leads to cost savings that are significantly greater than either storage (active or passive building thermal storage) but less than the sum of the individual savings (Henze et al., 2004).”
Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design (2013, background): “2008 [85], Henze 2004 [153], Kummert 2007 [149], Magnier et al.”
Simplified building model for transient thermal performance estimation using GA-based parameter identification (2006, background): “In order to predict the overall energy consumption for reference, it is fundamental to have a model to estimate the cooling or heating energy consumption to maintain temperature and humidity of the air in the building [3–5].”
A roadmap towards intelligent net zero- and positive-energy buildings (2011, background): “No matter whether they are employing heuristic, datadriven, model-based approaches (Diakaki et al., 2008; Braun, 1990; Freire et al., 2008; Henze et al., 2004; Keeney and Braun, 1996; Kummert et al. 2000; Lee and Braun, 2008; Mahdavi and Pröglhöf, 2008; Spindler and Norford, 2009a,b; Xu et al., 2009”
Model predictive control for the operation of building cooling systems (2010, background): “We remark that the evaluation of optimal controllers for active and passive building thermal storage has been studied in the past by several authors (see [4], [9], [6], [5] and references therein).”
Predictive Control for Energy Efficient Buildings with Thermal Storage: Modeling, Stimulation, and Experiments (2012, methodology): “The objective of this section is to use a simple thermal mass model to show the basic principles of active thermal storage, to demonstrate that active storage naturally emerges as a closed-loop behavior of an MPC scheme, and to discuss the main tradeoffs that arise in active thermal storage.”