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Record W3130233676 · doi:10.3390/en14041195

Model Predictive Control Strategies to Activate the Energy Flexibility for Zones with Hydronic Radiant Systems

2021· article· en· W3130233676 on OpenAlexafffund
Ali Saberi Derakhtenjani, Andreas Athienitis

Bibliographic record

VenueEnergies · 2021
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsHydro-QuébecNatural Sciences and Engineering Research Council of CanadaConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaConcordia University
KeywordsRadiant heatingElectricityFlexibility (engineering)Demand responseEnvironmental scienceAutomotive engineeringHeating systemEnergy consumptionComputer scienceSimulationEngineeringMechanical engineeringElectrical engineeringMaterials scienceMathematics

Abstract

fetched live from OpenAlex

This paper presents control strategies to activate energy flexibility for zones with radiant heating systems in response to changes in electricity prices. The focus is on zones with radiant floor heating systems for which the hydronic pipes are located deep in the concrete and, therefore, there is a significant thermal lag. A perimeter zone test-room equipped with a hydronic radiant floor system in an environmental chamber is used as a case study. A low order thermal network model for the perimeter zone, validated with experimental measurements, is utilized to study various control strategies in response to changes in the electrical grid price signal, including short term (nearly reactive) changes of the order of 10–15 min notice. An index is utilized to quantify the building energy flexibility with the focus on peak demand reduction for specific periods of time when the electricity prices are higher than usual. It is shown that the developed control strategies can aid greatly in enhancing the zone energy flexibility and minimizing the cost of electricity and up to 100% reduction in peak power demand and energy consumption is attained during the high-price and peak-demand periods, while maintaining acceptable comfort conditions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.833
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.201
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2021
Admission routes2
Has abstractyes

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