Adaptive ensemble models for medium-term forecasting of water inflow when planning electricity generation under climate change
| Autor: | Sergey Kokin, Murodbek Safaraliev, Pavel Matrenin, Stepan A. Dmitriev, Anastasia G. Rusina, Bahtiyor Eshchanov |
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| Rok vydání: | 2022 |
| Předmět: |
Ensemble models
Operations research SMALL HYDROPOWER PLANT MULTILAYER NEURAL NETWORKS Reliability (computer networking) CLIMATE CHANGE Decision tree CLIMATE MODELS Climate change WATER INFLOWS Inflow ELECTRIC POWER SYSTEM FORECASTING ACCURACY RESERVOIRS (WATER) Electric power system Small hydropower plant SMALL HYDROELECTRIC POWER PLANTS Isolated power system Hydroelectricity FORECASTING HYDROELECTRIC POWER PLANTS WATER INFLOW HYDROELECTRIC POWER ENSEMBLE MODELS RECURRENT NEURAL NETWORKS DECISION TREES ELECTRIC POWER SYSTEM PLANNING Ensemble forecasting ISOLATED POWER SYSTEM STOCHASTIC SYSTEMS TK1-9971 MEDIUM-TERM FORECASTING General Energy Electricity generation Environmental science SMALL RESERVOIRS Medium-term forecasting Electrical engineering. Electronics. Nuclear engineering ELECTRICITY-GENERATION Water inflow POWER SUPPLY SMALL HYDRO POWER PLANTS REMOTE POWER |
| Zdroj: | Energy Rep. Energy Reports Energy Reports, Vol 8, Iss, Pp 439-447 (2022) |
| ISSN: | 2352-4847 |
| DOI: | 10.1016/j.egyr.2021.11.112 |
| Popis: | 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. © 2021 The Author(s). The reported study was funded by RFBR, Sirius University of Science and Technology, JSC Russian Railways and Educational Fund “Talent and success”, project number 20-38-51007. |
| Databáze: | OpenAIRE |
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