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Simplification of ANN-Based Adaptive Load Prediction and Offline Controller for Photovoltaic Heating Systems.

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Title: Simplification of ANN-Based Adaptive Load Prediction and Offline Controller for Photovoltaic Heating Systems.
Authors: Xu, Shimin1 (AUTHOR) yaxiongw@hotmail.com, Wang, Yaxiong2 (AUTHOR), An, Shengli1,3 (AUTHOR) xusmgw@163.com, Su, Qingzong1,3 (AUTHOR)
Source: Energies (19961073). Mar2026, Vol. 19 Issue 5, p1305. 16p.
Subject Terms: *Artificial neural networks, *Solar heating, *Adaptive control systems, *Demand forecasting, *Optimization algorithms, *Energy development
Abstract: This study examines how strongly demand-load prediction and adaptive load control in photovoltaic heating systems rely on computationally intensive artificial neural network (ANN) models. To streamline the computational workflow and reduce runtime resource requirements, we propose an ANN load-prediction-and-validation algorithm coupled with a corresponding offline control strategy. By optimizing the algorithmic structure and shifting heavy computations away from online execution, the proposed method substantially lowers the operational computational burden while preserving predictive accuracy, enabling efficient real-time load prediction and adaptive control. Based on a modelling study of a monocrystalline PV string comprising two 330 W modules connected in series, the proposed simplified prediction method produced annual cumulative energy outputs of 139.9, 391.2, 320.2, 251.4, and 154.1 kW·h across the five irradiance intervals [200, 400), [400, 600), [600, 800), [800, 1000), and [1000, ∞), respectively. Compared with a conventional artificial neural network (ANN)-based prediction approach, the corresponding deviations were 1.1%, −0.1%, 0.0%, 0.1%, and −0.4%, the total annual cumulative energy outputs across all intervals was 1256.7 kW·h with a mean deviation of −0.07%. Moreover, the simplified load-control strategy required only 3.57% of the computational resources consumed by the conventional ANN method. In addition, the method rapidly reallocates computational resources in response to changes in real-time input data, thereby minimizing redundant computation. Overall, the results demonstrate that the proposed framework markedly reduces computational complexity without sacrificing accuracy, providing an effective alternative to traditional ANN-based solutions and facilitating the practical deployment of photovoltaic heating systems. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 192641030
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PubTypeId: academicJournal
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Simplification of ANN-Based Adaptive Load Prediction and Offline Controller for Photovoltaic Heating Systems.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Xu%2C+Shimin%22">Xu, Shimin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> yaxiongw@hotmail.com</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Yaxiong%22">Wang, Yaxiong</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22An%2C+Shengli%22">An, Shengli</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> xusmgw@163.com</i><br /><searchLink fieldCode="AR" term="%22Su%2C+Qingzong%22">Su, Qingzong</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Mar2026, Vol. 19 Issue 5, p1305. 16p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Solar+heating%22">Solar heating</searchLink><br />*<searchLink fieldCode="DE" term="%22Adaptive+control+systems%22">Adaptive control systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Demand+forecasting%22">Demand forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+development%22">Energy development</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This study examines how strongly demand-load prediction and adaptive load control in photovoltaic heating systems rely on computationally intensive artificial neural network (ANN) models. To streamline the computational workflow and reduce runtime resource requirements, we propose an ANN load-prediction-and-validation algorithm coupled with a corresponding offline control strategy. By optimizing the algorithmic structure and shifting heavy computations away from online execution, the proposed method substantially lowers the operational computational burden while preserving predictive accuracy, enabling efficient real-time load prediction and adaptive control. Based on a modelling study of a monocrystalline PV string comprising two 330 W modules connected in series, the proposed simplified prediction method produced annual cumulative energy outputs of 139.9, 391.2, 320.2, 251.4, and 154.1 kW·h across the five irradiance intervals [200, 400), [400, 600), [600, 800), [800, 1000), and [1000, ∞), respectively. Compared with a conventional artificial neural network (ANN)-based prediction approach, the corresponding deviations were 1.1%, −0.1%, 0.0%, 0.1%, and −0.4%, the total annual cumulative energy outputs across all intervals was 1256.7 kW·h with a mean deviation of −0.07%. Moreover, the simplified load-control strategy required only 3.57% of the computational resources consumed by the conventional ANN method. In addition, the method rapidly reallocates computational resources in response to changes in real-time input data, thereby minimizing redundant computation. Overall, the results demonstrate that the proposed framework markedly reduces computational complexity without sacrificing accuracy, providing an effective alternative to traditional ANN-based solutions and facilitating the practical deployment of photovoltaic heating systems. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=192641030
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/en19051305
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 16
        StartPage: 1305
    Subjects:
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Solar heating
        Type: general
      – SubjectFull: Adaptive control systems
        Type: general
      – SubjectFull: Demand forecasting
        Type: general
      – SubjectFull: Optimization algorithms
        Type: general
      – SubjectFull: Energy development
        Type: general
    Titles:
      – TitleFull: Simplification of ANN-Based Adaptive Load Prediction and Offline Controller for Photovoltaic Heating Systems.
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Xu, Shimin
      – PersonEntity:
          Name:
            NameFull: Wang, Yaxiong
      – PersonEntity:
          Name:
            NameFull: An, Shengli
      – PersonEntity:
          Name:
            NameFull: Su, Qingzong
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          Dates:
            – D: 01
              M: 03
              Text: Mar2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19961073
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            – Type: volume
              Value: 19
            – Type: issue
              Value: 5
          Titles:
            – TitleFull: Energies (19961073)
              Type: main
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