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Typical daily scenario extraction method based on key features to promote building renewable energy system optimization efficiency

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  • Tian, Zhe
  • Wang, Yi
  • Li, Xiaoyuan
  • Wen, Li
  • Niu, Jide
  • Lu, Yakai

Abstract

Given its intrinsic volatility and randomness, the effective design of a building renewable energy system (BRES) necessitates long-period, short-step boundaries. Nevertheless, this poses computational burdens. In this context, time-series aggregation (TSA) emerges as a valuable approach, which involves obtaining a set of typical scenarios to substitute original time series. Numerous studies have focused on mathematical algorithms to minimize the deviation between typical scenarios and original time series. However, the premise of enhancing model-solving efficiency by extracting typical scenarios is to ensure the optimality of planning. Henceforth, identifying and quantifying key features in the original time series that significantly influence the planning, and further extracting need to be addressed. This paper proposes a typical day extraction method based on key features, named the feature-based method. Key features are summarized through an extensive literature review and then quantitatively expressed through different indicator models. Finally, a feature-based mixed integer linear programming (MILP) model is established for extraction. To evaluate the effectiveness of the feature-based method, it is applied to design a BRES and compared with traditional TSA methods such as Averaging Method (AM) and k-means. Results indicate that the feature-based method can effectively retain key features, thereby achieving more efficient and accurate designs.

Suggested Citation

  • Tian, Zhe & Wang, Yi & Li, Xiaoyuan & Wen, Li & Niu, Jide & Lu, Yakai, 2024. "Typical daily scenario extraction method based on key features to promote building renewable energy system optimization efficiency," Renewable Energy, Elsevier, vol. 236(C).
    • Handle: RePEc:eee:renene:v:236:y:2024:i:c:s0960148124014885
    DOI: 10.1016/j.renene.2024.121420
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