{
  "abstract": "Accurate joint forecasting of hydro-wind-photovoltaic bases is difficult because hydropower regulation, wind randomness and photovoltaic intermittency interact through both temporal complementarity and spatial-electrical coupling. This study aims to improve source-level and bundled-power forecasting by embedding the complementary mechanism of heterogeneous renewable sources into both representation learning and model training. A hierarchical complementary-prior regularized multi-scale seasonal-trend decoupled heterogeneous graph Transformer, denoted MSTD-HGFormer-HCPR, is proposed. The model first separates the historical multi-source sequence into low-frequency trend and high-frequency fluctuation components through learnable multi-scale moving-average kernels. The two components are then encoded by parallel heterogeneous graph Transformer branches using temporal patch attention and a static-dynamic fused adjacency that combines physical topology, historical correlation and learnable latent relations. A gated fusion module produces the final forecast, while a composite loss introduces capacity-weighted system- level and correlation-aware topology-level complementary-prior constraints. Experiments on a real 17-node hydro-wind-photovoltaic base show that the proposed method reduces rRMSE by 12.9–50.6% against seven baselines, improves the correlation coefficient by 0.011–0.013 over the strongest baseline, and keeps the forecasting accuracy at or above 96.2% for hydro, wind, photovoltaic and aggregate outputs. A second, 23-node cascade-radial base with five-year records is further examined under a rolling-origin and season- stratified protocol; the aggregate rRMSE is reduced by 13.2–44.0%, and the pooled accuracies remain 96.0% for hydropower, 96.0% for wind, 96.4% for photovoltaic and 96.2% for aggregate outputs. The proposed regularized joint forecaster provides stable and accurate spatiotemporal prediction for complementary renewable bases and reduces the accumulation of source-level errors in bundled output forecasting.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "Weijie Zhao"
    },
    {
      "name": "Yi Jiang"
    },
    {
      "name": "Yuxin Xie"
    },
    {
      "name": "Panfeng Guo"
    },
    {
      "name": "Lingfei Li"
    },
    {
      "name": "Jianxiang Huang"
    },
    {
      "name": "Zhixiong Huang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/37rkc88w38crt.bib",
  "cited_by": 0,
  "doi": "10.4108/ew.15713",
  "fulltext_url": "https://publications.eai.eu/index.php/ew/article/view/15713",
  "id": "37rkc88w38crt",
  "issns": [
    "2032-944X"
  ],
  "landing_url": "https://doi.org/10.4108/ew.15713",
  "language": "en",
  "licence": "CC-BY-NC-SA",
  "licence_label": "CC BY-NC-SA",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-sa/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "QA1-939"
    ],
    [
      "lcc",
      "QA75.5-76.95"
    ],
    [
      "openalex",
      "2208|Energy Load and Power Forecasting"
    ]
  ],
  "openalex": "W7223403426",
  "pdf_url": "https://publications.eai.eu/index.php/ew/article/download/15713/4494",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-24",
  "publisher": "European Alliance for Innovation (EAI)",
  "ris": "https://research.draconitic.com/paper/37rkc88w38crt.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:44:32Z",
      "native_id": "844634104f264906a7d234317b1e5ead",
      "source": "doaj",
      "url": "https://doi.org/10.4108/ew.15713"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "lcc:QA1-939",
      "subfield": "general"
    }
  ],
  "title": "Spatiotemporal Joint Power Forecasting for Hydro-Wind-Solar Complementary Systems via a Hierarchical Complementary-Prior Regularized MSTD-HGFormer",
  "title_text": "Spatiotemporal Joint Power Forecasting for Hydro-Wind-Solar Complementary Systems via a Hierarchical Complementary-Prior Regularized MSTD-HGFormer",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:44:32Z",
  "url": "https://research.draconitic.com/paper/37rkc88w38crt",
  "venue": "EAI Endorsed Transactions on Energy Web",
  "versions": []
}