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Mathematical Model for Hydropower Plant (HPP) Electricity Forecasting with High Time Resolution.

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Title: Mathematical Model for Hydropower Plant (HPP) Electricity Forecasting with High Time Resolution.
Authors: Alexiev, Viktor1 (AUTHOR), Marinov, Boris1,2 (AUTHOR), Shterev, Vasil2,3 (AUTHOR) vashterev@uni-sofia.bg, Stanev, Rad3,4 (AUTHOR), Bozhilov, Bozhidar1,3 (AUTHOR)
Source: Energies (19961073). May2026, Vol. 19 Issue 9, p2217. 34p.
Subject Terms: *Electric power production forecasting, *Mathematical models, *Hydrologic models, *Renewable energy sources, *Statistics, *Interpolation, *Forecasting, *Hydroelectric power plants
Abstract: Forecasting hydropower plant power production is a great challenge in the context of maintaining power system stability, reliability and efficiency, especially in an age with variable renewable energy sources when demand for electricity is steadily rising. Accurate forecasting methods are a crucial enabler for the operational existence of power systems that rely on renewable sources. And while in the pursuit of increased accuracy of predictions, many recent research works rely on artificial intelligence and machine learning techniques, this study proposes and adopts a more conventional approach with standardized mathematical models to address the problem of hydropower production forecasting. The model predicts the runoff–power relationship. It starts with the normalization of different rain phenomena as a part of the statistical characterization of runoff events. The system transforms rain occurrence to runoff events via the USDA SCS CN model and then feature vectors are composed, which are used to generate kernel coefficients via interpolation. Contrary to models based on artificial intelligence, the proposed approach has several practical advantages requiring a minimal set of input parameters, which significantly reduces data preprocessing demands and allows for a straightforward integration into existing systems, thereby lowering the cost and the implementation and deployment time. Furthermore, the simplicity and universality of the model make it so that it can be adapted across a wide range of hydropower plants of varying scales and with diverse hydrological and meteorological conditions. The model's performance and prediction accuracy are evaluated using empirical data records of time series over a five-year period for the meteorological parameters and production of an existing real-life hydropower plant in Bulgaria. The performance of the newly proposed model is assessed using widely accepted statistical error metrics, namely, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), the Nash–Sutcliffe Efficiency (NSE) coefficient, and the Pearson correlation coefficient (R). These metrics provide a comprehensive assessment of the forecasts' precision and effectiveness. The results show that the proposed model offers admissible accuracy with low computational effort. Thus, it can be successfully implemented in practice in a number of hydropower plant production forecasting applications. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 193716113
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PubTypeId: academicJournal
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  Label: Title
  Group: Ti
  Data: Mathematical Model for Hydropower Plant (HPP) Electricity Forecasting with High Time Resolution.
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  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Alexiev%2C+Viktor%22">Alexiev, Viktor</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Marinov%2C+Boris%22">Marinov, Boris</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shterev%2C+Vasil%22">Shterev, Vasil</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> vashterev@uni-sofia.bg</i><br /><searchLink fieldCode="AR" term="%22Stanev%2C+Rad%22">Stanev, Rad</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bozhilov%2C+Bozhidar%22">Bozhilov, Bozhidar</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2026, Vol. 19 Issue 9, p2217. 34p.
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  Data: *<searchLink fieldCode="DE" term="%22Electric+power+production+forecasting%22">Electric power production forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink><br />*<searchLink fieldCode="DE" term="%22Hydrologic+models%22">Hydrologic models</searchLink><br />*<searchLink fieldCode="DE" term="%22Renewable+energy+sources%22">Renewable energy sources</searchLink><br />*<searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink><br />*<searchLink fieldCode="DE" term="%22Interpolation%22">Interpolation</searchLink><br />*<searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Hydroelectric+power+plants%22">Hydroelectric power plants</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Forecasting hydropower plant power production is a great challenge in the context of maintaining power system stability, reliability and efficiency, especially in an age with variable renewable energy sources when demand for electricity is steadily rising. Accurate forecasting methods are a crucial enabler for the operational existence of power systems that rely on renewable sources. And while in the pursuit of increased accuracy of predictions, many recent research works rely on artificial intelligence and machine learning techniques, this study proposes and adopts a more conventional approach with standardized mathematical models to address the problem of hydropower production forecasting. The model predicts the runoff–power relationship. It starts with the normalization of different rain phenomena as a part of the statistical characterization of runoff events. The system transforms rain occurrence to runoff events via the USDA SCS CN model and then feature vectors are composed, which are used to generate kernel coefficients via interpolation. Contrary to models based on artificial intelligence, the proposed approach has several practical advantages requiring a minimal set of input parameters, which significantly reduces data preprocessing demands and allows for a straightforward integration into existing systems, thereby lowering the cost and the implementation and deployment time. Furthermore, the simplicity and universality of the model make it so that it can be adapted across a wide range of hydropower plants of varying scales and with diverse hydrological and meteorological conditions. The model's performance and prediction accuracy are evaluated using empirical data records of time series over a five-year period for the meteorological parameters and production of an existing real-life hydropower plant in Bulgaria. The performance of the newly proposed model is assessed using widely accepted statistical error metrics, namely, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), the Nash–Sutcliffe Efficiency (NSE) coefficient, and the Pearson correlation coefficient (R). These metrics provide a comprehensive assessment of the forecasts' precision and effectiveness. The results show that the proposed model offers admissible accuracy with low computational effort. Thus, it can be successfully implemented in practice in a number of hydropower plant production forecasting applications. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/en19092217
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 34
        StartPage: 2217
    Subjects:
      – SubjectFull: Electric power production forecasting
        Type: general
      – SubjectFull: Mathematical models
        Type: general
      – SubjectFull: Hydrologic models
        Type: general
      – SubjectFull: Renewable energy sources
        Type: general
      – SubjectFull: Statistics
        Type: general
      – SubjectFull: Interpolation
        Type: general
      – SubjectFull: Forecasting
        Type: general
      – SubjectFull: Hydroelectric power plants
        Type: general
    Titles:
      – TitleFull: Mathematical Model for Hydropower Plant (HPP) Electricity Forecasting with High Time Resolution.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Alexiev, Viktor
      – PersonEntity:
          Name:
            NameFull: Marinov, Boris
      – PersonEntity:
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            NameFull: Shterev, Vasil
      – PersonEntity:
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            NameFull: Stanev, Rad
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            NameFull: Bozhilov, Bozhidar
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          Dates:
            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19961073
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            – Type: volume
              Value: 19
            – Type: issue
              Value: 9
          Titles:
            – TitleFull: Energies (19961073)
              Type: main
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