Mathematical Model for Hydropower Plant (HPP) Electricity Forecasting with High Time Resolution.
Saved in:
| 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 |
|
Full text is not displayed to guests.
Login for full access.
|
|
| 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] |
|---|---|
| ISSN: | 19961073 |
| DOI: | 10.3390/en19092217 |
