E3S Web Conf.
Volume 86, 2019The First International Scientific Conference on Ecological and Environmental Engineering 2018
|Number of page(s)||7|
|Published online||22 February 2019|
Possibility of the modelling of electricity production from hydropower
1 University of Agriculture in Krakow, Faculty of Environmental Engineering and Land Surveying, Department of Sanitary Engineering and Water Management, Al. Mickiewicza 21, 31-120, Krakow, Poland
2 Kielce University of Technology, Al. Tysiąclecia Państwa Polskiego 7, 25-314, Kielce, Poland
3 77 Construction USA CORP, 114 Essex St Ste 308 Rochelle Park, NJ, 07662, USA
* Corresponding author: email@example.com
In hydropower plants benefits depends on available flow. The paper presents a hybrid model for forecasting the operation of a hydropower plant, including the production of electricity. The possibility of mathematical modeling was chosen to show connections between observed in the past hydrological conditions (available flow) and energy deliver in the future. The available flow which is not enough for start turbines was forecasting by logistic regression model. The opposite situation when the flow starts turbine to produce energy, regression models (the support vector machines SVM, random forest RF, k nearest neighbour k-NN) were used. Results from hybrid model were compared with chosen data-mining methods. The possibility of forecasting of the length of periods when hydropower plant will be working could be very useful. It provides the prognosis of energy value which could be produced from hydropower plant. From the investors’ point of view the economic justification for the execution of the project based on the future energy producing could be a main criteria to realize or buy/sell hydropower plant. Also the secondary importance could be a possibility of planning review and maintenance work. Knowledge of power plant working periods could be a base for assessing a potential production from hydropower plant.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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