Adaptive ensemble models for medium-term forecasting of water inflow when planning electricity generation under climate change

Medium-term forecasting of water inflow is of great importance for small hydroelectric power plants operating in remote power supply areas and having a small reservoir. Improving the forecasting accuracy is aimed at solving the problem of determining the water reserve for the future generation of el...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Pavel Matrenin, Murodbek Safaraliev, Stepan Dmitriev, Sergey Kokin, Bahtiyor Eshchanov, Anastasia Rusina
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Medium-term forecasting Water inflow Small hydropower plant Electric power system Ensemble models Isolated power system Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/ce51cc1e59644e58a1db4c761c266149
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:ce51cc1e59644e58a1db4c761c266149
record_format	dspace
spelling	oai:doaj.org-article:ce51cc1e59644e58a1db4c761c2661492021-12-04T04:35:03ZAdaptive ensemble models for medium-term forecasting of water inflow when planning electricity generation under climate change2352-484710.1016/j.egyr.2021.11.112https://doaj.org/article/ce51cc1e59644e58a1db4c761c2661492022-04-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721012592https://doaj.org/toc/2352-4847Medium-term forecasting of water inflow is of great importance for small hydroelectric power plants operating in remote power supply areas and having a small reservoir. Improving the forecasting accuracy is aimed at solving the problem of determining the water reserve for the future generation of electricity at hydroelectric power plants, taking into account the regulation in the medium term. Medium-term regulation is necessary to amplify the load in the peak and semi-peak portions of the load curve. The solution to such problems is aggravated by the lack of sufficiently reliable information on water inflow and prospective power consumption, which is of a stochastic nature. In addition, the mid-term planning of electricity generation should consider the seasonality of changes in water inflow, which directly affects the reserves and the possibility of regulation. The paper considers the problem of constructing a model for medium-term forecasting of water inflow for planning electricity generation, taking into account climatic changes in isolated power systems. Taking into account the regularly increasing effect of climate change, the current study proposes using an approach based on machine learning methods, which are distinguished by a high degree of autonomy and automation of learning, that is, the ability to self-adapt. The results showed that the error (RMSE) of the model based on the ensemble of regression decision trees due to constant self-adaptation decreased from 4.5 m3/s to 4.0 m3/s and turned out to be lower than the error of a more complex multilayer recurrent neural network (4.9 m3/s). The research results are intended to improve forecasting reliability in the planning, management, and operation of isolated operating power systems.Pavel MatreninMurodbek SafaralievStepan DmitrievSergey KokinBahtiyor EshchanovAnastasia RusinaElsevierarticleMedium-term forecastingWater inflowSmall hydropower plantElectric power systemEnsemble modelsIsolated power systemElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 8, Iss , Pp 439-447 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medium-term forecasting Water inflow Small hydropower plant Electric power system Ensemble models Isolated power system Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Medium-term forecasting Water inflow Small hydropower plant Electric power system Ensemble models Isolated power system Electrical engineering. Electronics. Nuclear engineering TK1-9971 Pavel Matrenin Murodbek Safaraliev Stepan Dmitriev Sergey Kokin Bahtiyor Eshchanov Anastasia Rusina Adaptive ensemble models for medium-term forecasting of water inflow when planning electricity generation under climate change
description	Medium-term forecasting of water inflow is of great importance for small hydroelectric power plants operating in remote power supply areas and having a small reservoir. Improving the forecasting accuracy is aimed at solving the problem of determining the water reserve for the future generation of electricity at hydroelectric power plants, taking into account the regulation in the medium term. Medium-term regulation is necessary to amplify the load in the peak and semi-peak portions of the load curve. The solution to such problems is aggravated by the lack of sufficiently reliable information on water inflow and prospective power consumption, which is of a stochastic nature. In addition, the mid-term planning of electricity generation should consider the seasonality of changes in water inflow, which directly affects the reserves and the possibility of regulation. The paper considers the problem of constructing a model for medium-term forecasting of water inflow for planning electricity generation, taking into account climatic changes in isolated power systems. Taking into account the regularly increasing effect of climate change, the current study proposes using an approach based on machine learning methods, which are distinguished by a high degree of autonomy and automation of learning, that is, the ability to self-adapt. The results showed that the error (RMSE) of the model based on the ensemble of regression decision trees due to constant self-adaptation decreased from 4.5 m3/s to 4.0 m3/s and turned out to be lower than the error of a more complex multilayer recurrent neural network (4.9 m3/s). The research results are intended to improve forecasting reliability in the planning, management, and operation of isolated operating power systems.
format	article
author	Pavel Matrenin Murodbek Safaraliev Stepan Dmitriev Sergey Kokin Bahtiyor Eshchanov Anastasia Rusina
author_facet	Pavel Matrenin Murodbek Safaraliev Stepan Dmitriev Sergey Kokin Bahtiyor Eshchanov Anastasia Rusina
author_sort	Pavel Matrenin
title	Adaptive ensemble models for medium-term forecasting of water inflow when planning electricity generation under climate change
title_short	Adaptive ensemble models for medium-term forecasting of water inflow when planning electricity generation under climate change
title_full	Adaptive ensemble models for medium-term forecasting of water inflow when planning electricity generation under climate change
title_fullStr	Adaptive ensemble models for medium-term forecasting of water inflow when planning electricity generation under climate change
title_full_unstemmed	Adaptive ensemble models for medium-term forecasting of water inflow when planning electricity generation under climate change
title_sort	adaptive ensemble models for medium-term forecasting of water inflow when planning electricity generation under climate change
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/ce51cc1e59644e58a1db4c761c266149
work_keys_str_mv	AT pavelmatrenin adaptiveensemblemodelsformediumtermforecastingofwaterinflowwhenplanningelectricitygenerationunderclimatechange AT murodbeksafaraliev adaptiveensemblemodelsformediumtermforecastingofwaterinflowwhenplanningelectricitygenerationunderclimatechange AT stepandmitriev adaptiveensemblemodelsformediumtermforecastingofwaterinflowwhenplanningelectricitygenerationunderclimatechange AT sergeykokin adaptiveensemblemodelsformediumtermforecastingofwaterinflowwhenplanningelectricitygenerationunderclimatechange AT bahtiyoreshchanov adaptiveensemblemodelsformediumtermforecastingofwaterinflowwhenplanningelectricitygenerationunderclimatechange AT anastasiarusina adaptiveensemblemodelsformediumtermforecastingofwaterinflowwhenplanningelectricitygenerationunderclimatechange
_version_	1718372996287037440

Adaptive ensemble models for medium-term forecasting of water inflow when planning electricity generation under climate change

Ejemplares similares