{
  "annotation_id": "f0955e5d-dea9-4135-af99-f591adb4b0d0",
  "date_created": "2026-02-17T05:53:16.131000Z",
  "date_modified": "2026-02-17T05:53:16.131000Z",
  "file_hash": "926fc4f30b4f3cf7b2f9844486d519a0e4ea448ad5a8e9696deb0190b8f94282",
  "private": false,
  "record": {
    "abstract": "Residential users in demand response programs must balance electricity costs and user dissatisfaction under real-time pricing. This study proposes a multiobjective model predictive control approach for home energy management systems with battery storage, aiming to minimize both objectives while mitigating uncertainties. Laguerre functions parameterize control signals, transforming the optimization problem into one with linear inequalities for efficient exploration. A constrained multiobjective evolutionary algorithm, incorporating convex sampler-based crossover and mutation, is developed to ensure feasible solutions. Simulations show that the proposed method outperforms existing approaches, limiting cost increases to 0.52\\% under uncertainties, compared to at least 2.3\\% with other methods.",
    "arxiv_id": "2601.08445",
    "authors": [
      "Guan-Ting Lin",
      "Wei-Yu Chiu",
      "Chien-Feng Wu",
      "Asef Nazari",
      "Dhananjay Thiruvady"
    ],
    "categories": [
      "eess.SY",
      "cs.SY"
    ],
    "doi": "10.1016/j.energy.2025.136249",
    "journal_ref": "Energy (2025)",
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Multiobjective Model Predictive Control for Residential Demand Response Management Under Uncertainty",
    "url": "https://arxiv.org/abs/2601.08445",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "859d8fb9-a6c1-4e99-a658-f88becb16da3",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}