文章基本信息

标题：Possibility of the modelling of electricity production from hydropower
本地全文：下载
作者：Agnieszka Operacz ; Bartosz Szeląg ; Mads Grahl-Madsen 等
期刊名称：E3S Web of Conferences
印刷版ISSN：2267-1242
电子版ISSN：2267-1242
出版年度：2019
卷号：86
页码：1-7
DOI：10.1051/e3sconf/20198600008
出版社：EDP Sciences
摘要：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.
其他摘要：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.