Publication record · 18.cifr/2021.kou.hvac-demand-response

Model-Based and Data-Driven HVAC Control Strategies for Residential Demand Response

v1.0.0

Xiao Kou (University of Tennessee), Yan Du (University of Tennessee), Fangxing Li (University of Tennessee), Hector Pulgar-Painemal (University of Tennessee), Helia Zandi (Oak Ridge National Laboratory), Jin Dong (Oak Ridge National Laboratory), Mohammed M. Olama (Oak Ridge National Laboratory)

RAI18.cifr/2021.kou.hvac-demand-response

IEEE Open Access Journal of Power and Energy· 2021· doi:10.1109/OAJPE.2021.3075426

This paper presents model-based and data-driven HVAC control strategies for residential demand response. A first-order RC thermal model captures building thermal dynamics. Model predictive control (MPC) optimizes HVAC scheduling to minimize energy cost while satisfying thermal comfort constraints. A data-driven rule-based strategy provides a computationally lighter alternative. Both strategies are evaluated under time-of-use pricing and direct load control scenarios.

demand responseHVAC controlmodel predictive controlRC thermal modelresidential buildingsload flexibility

✦ Research context

What this agent contributes to the literature.

Problem solved

Residential HVAC loads offer flexibility but lack practical control strategies balancing cost, comfort, and computation.
MPC-based HVAC control is computationally expensive, limiting deployment in resource-constrained residential settings.
Utility operators and homeowners need quantified load reduction metrics under both price signals and curtailment commands.

Novelty

Combines MPC with a first-order RC thermal model to schedule residential HVAC under TOU pricing and direct load control.

Related research

Computing related research...

Canvas contract1-in / 1-out · unpacked into building, grid_signal legacy ports

Sample data

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Total calls

This month

Citations

Last called

4/16/2026

Image digest

sha256:024577cef221a2e9dea744800ca7199f4e10272fce7fe313bf8dfe8367000f01

Invoke command

python main.py

Inputs

input:application/json

Outputs

output:application/json

Citation

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Invoke

CPU compute only

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Pre-filled with the paper's canonical scenario. Click Invoke agent to reproduce the original result, or edit the JSON below to run a counterfactual.

inputapplication/jsonoptional

Unified canvas input. The executor unpacks this JSON blob into legacy per-port files: building, grid_signal

Leave empty to run the paper's canonical scenario.

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  "building": {
    "R": 2,
    "C": 2.5,
    "hvac_rated_power_kw": 3.5,
    "setpoint_c": 23,
    "deadband_c": 2,
    "initial_temp_c": 23.5,
    "precool_offset_c": 1,
    "mpc_horizon_steps": 12
  },
  "grid_signal": {
    "dt_minutes": 5,
    "dr_price_threshold": 0.18,
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    "price_per_kwh": [
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