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Medium-term forecasting of natural inflow HPP
https://doi.org/10.15518/isjaee.2024.02.086-099
Abstract
The article contains a rationale for the importance and necessity of forecasting the natural inflow HPP, as well as a statement of the basic principles that complicate the management of HPP modes without forecasting models. Special attention is paid to the description of the forecasting methods used, assessment of their accuracy, advantages and disadvantages. The creation of a model and testing of algorithms for predicting natural inflow HPP was implemented using machine learning methods in Python and scikit-learn library. The Novosibirsk HPP was chosen as the object of the study. During the collection and analysis of data, a sample was created from meteorological information for 3287 days with values of air temperature, pressure, precipitation, humidity and natural inflow HPP. Forecasting of inflow HPP is implemented using linear regression, second-degree polynomial regression, nearest neighbors, decision trees and random decision forest models.
The obtained results of assessing the accuracy criteria MAPE, RMSE, R2 and MSE for each of the considered models for forecasting natural inflow HPP showed that a model based on a random forest of decision trees achieved the implementation of the most accurate medium-term forecast of inflow HPP.
About the Authors
S. V. Mitrofanov
Novosibirsk State Technical University; Tomsk Polytechnic University, ES IES ISD
Russian Federation
Russian Federation
Mitrofanov Sergey V., Ph.d. researcher at department of intelligent systems of the engineering school «Intelligent Energy Systems» (ES IES ISD), Tomsk Polytechnic University. Associate Professor, Department of Power Supply Systems, Novosibirsk State Technical University
K. Marx Avenue, 20, Novosibirsk, 630073
phone: 8 (383) 346-08-43, fax: (383) 346-02-09
Lenin Avenue, 30, Tomsk, 634050
phone: +7 (3822) 60-63-3, fax: +7 (3822) 60-64-44
A. V. Sidorova
Novosibirsk State Technical University
Russian Federation
Russian Federation
Sidorova Alena V., researcher at the Interdepartmental Research Laboratory for Processing, Analysis and Presentation of Data in Electric Power Systems
K. Marx Avenue, 20, Novosibirsk, 630073
phone: 8 (383) 346-08-43, fax: (383) 346-02-09
A. G. Rusina
Novosibirsk State Technical University
Russian Federation
Russian Federation
Rusina Anastasia G., Doc. of Sc., Dean of the Faculty of Energy, Head of the Power Plants Department at Novosibirsk State Technical University (NSTU NETI), expert at the RAS, member of IEEE
K. Marx Avenue, 20, Novosibirsk, 630073
phone: 8 (383) 346-08-43, fax: (383) 346-02-09
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For citations:
Mitrofanov S.V., Sidorova A.V., Rusina A.G. Medium-term forecasting of natural inflow HPP. Alternative Energy and Ecology (ISJAEE). 2024;(2):86-99. (In Russ.) https://doi.org/10.15518/isjaee.2024.02.086-099
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