Medium-term forecasting of power generation by hydropower plants in isolated power systems under climate change

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dc.contributor.authorSafaraliev, Murodbek
dc.contributor.authorKiryanova, Natalya
dc.contributor.authorMatrenin, Pavel
dc.contributor.authorDmitriev, Stepan
dc.contributor.authorKokin, Sergey
dc.contributor.authorKamalov, Firuz
dc.date.accessioned2022-12-22T06:34:22Z
dc.date.available2022-12-22T06:34:22Z
dc.date.copyright© 2022
dc.date.issued2022-11
dc.description.abstractReliable operation of power systems (PS), including those with a significant share of hydropower plants (HPPs) in the energy balance, largely depends on the accuracy of forecasting power generation. The importance of power generation forecasts increases with the development of renewable power generation, which is stochastic by nature. Those kinds of tasks are complicated by the lack of reliable information on metrological data and estimated energy consumption, which is also stochastic. In the medium-term forecasting (MTF) of power generation by HPPs, the seasonality of changes in flow and inflow of water should be taken into account, which significantly affects the reserves and regulatory capabilities of the power system as a whole. This work discusses the problem of constructing a model for MTF of power generation HPP in isolated power systems (IPS), taking into account such atmospheric parameters as air temperature, wind speed and humidity. To address constant climatic changes, this paper suggests implementing machine learning models. The proposed approach is characterized by a high degree of autonomy and learning automation. The paper provides a comparative study of the machine learning models such as polynomial model with Tikhonov's regularization (LR), k-nearest neighbors (kNN), multilayer perceptron (MLP), ensembles of decision trees, adaptive boosting of linear models (ABLR), etc. Computational experiments have shown that the machine learning approach yields the results of sufficient quality, which allows to use them for forecasting of power generation HPP in isolated power systems under conditions of climate change. The Adaptive Boosting Linear Regression model is the simplest and most reliable machine learning model that has proven itself well in the tasks with a relatively small amount of training samples. © 2022 The Author(s)
dc.description.sponsorshipThe study was financially supported as part of the Novosibirsk State Technical University, Russia development program, scientific project C22-15.
dc.identifier.citationSafaraliev, M., Kiryanova, N., Matrenin, P., Dmitriev, S., Kokin, S., & Kamalov, F. (2022). Medium-term forecasting of power generation by hydropower plants in isolated power systems under climate change. Energy Reports, 8, 765-774. doi:10.1016/j.egyr.2022.09.164
dc.identifier.issn23524847
dc.identifier.urihttps://doi.org/10.1016/j.egyr.2022.09.164
dc.identifier.urihttps://hdl.handle.net/20.500.12519/735
dc.language.isoen_US
dc.publisherElsevier Ltd
dc.relationAuthors Affiliations : Safaraliev, M., Department of Automated Electrical Systems, Ural Federal University, Yekaterinburg, Russian Federation; Kiryanova, N., Department of Automated Electrical Power Systems, Novosibirsk State Technical University, Novosibirsk, Russian Federation; Matrenin, P., Department of Industrial Power Supply Systems, Novosibirsk State Technical University, Novosibirsk, Russian Federation; Dmitriev, S., Department of Automated Electrical Systems, Ural Federal University, Yekaterinburg, Russian Federation; Kokin, S., Department of Automated Electrical Systems, Ural Federal University, Yekaterinburg, Russian Federation; Kamalov, F., Department of Electrical Engineering, Canadian University Dubai, Dubai, United Arab Emirates
dc.relation.ispartofseriesEnergy Reports; Volume 8
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License
dc.rights.holderCopyright : © 2022 The Author(s)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectClimate change
dc.subjectEnsemble models
dc.subjectGBAO
dc.subjectHydropower plant
dc.subjectIsolated power system
dc.subjectMedium-term forecasting of power generation
dc.subjectTemperature
dc.titleMedium-term forecasting of power generation by hydropower plants in isolated power systems under climate change
dc.typeArticle

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