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Forecasting Electricity Production in a Small Hydropower Plant (SHP) Using Artificial Intelligence (AI)

Author

Listed:
  • Dawid Maciejewski

    (Department of Bioprocess Engineering, Power Engineering and Automation, University of Agriculture in Krakow, 31-120 Krakow, Poland)

  • Krzysztof Mudryk

    (Department of Mechanical Engineering and Agrophysics, University of Agriculture in Krakow, 31-120 Krakow, Poland)

  • Maciej Sporysz

    (Department of Production Engineering, Logistics and Applied Computer Science, University of Agriculture in Krakow, 31-120 Krakow, Poland)

Abstract

This article devises the Artificial Intelligence (AI) methods of designing models of short-term forecasting (in 12 h and 24 h horizons) of electricity production in a selected Small Hydropower Plant (SHP). Renewable Energy Sources (RESs) are difficult to predict due to weather variability. Electricity production by a run-of-river SHP is marked by the variability related to the access to instantaneous flow in the river and weather conditions. In order to develop predictive models of an SHP facility (installed capacity 760 kW), which is located in Southern Poland on the Skawa River, hourly data from nearby meteorological stations and a water gauge station were collected as explanatory variables. Data on the water management of the retention reservoir above the SHP were also included. The variable to be explained was the hourly electricity production, which was obtained from the tested SHP over a period of 3 years and 10 months. Obtaining these data to build models required contact with state institutions and private entrepreneurs of the SHP. Four AI methods were chosen to create predictive models: two types of Artificial Neural Networks (ANNs), Multilayer Perceptron (MLP) and Radial Base Functions (RBFs), and two types of decision trees methods, Random Forest (RF) and Gradient-Boosted Decision Trees (GBDTs). Finally, after applying forecast quality measures of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R 2 ), the most effective model was indicated. The decision trees method proved to be more accurate than ANN models. The best GBDT models’ errors were MAPE 3.17% and MAE 9.97 kWh (for 12 h horizon), and MAPE 3.41% and MAE 10.96 kWh (for 24 h horizon). MLPs had worse results: MAPE from 5.41% to 5.55% and MAE from 18.02 kWh to 18.40 kWh (for 12 h horizon), and MAPE from 7.30% to 7.50% and MAE from 24.12 kWh to 24.83 kWh (for 24 h horizon). Forecasts using RBF were not made due to the very low quality of training and testing (the correlation coefficient was approximately 0.3).

Suggested Citation

  • Dawid Maciejewski & Krzysztof Mudryk & Maciej Sporysz, 2024. "Forecasting Electricity Production in a Small Hydropower Plant (SHP) Using Artificial Intelligence (AI)," Energies, MDPI, vol. 17(24), pages 1-26, December.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:24:p:6401-:d:1547742
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    References listed on IDEAS

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