A Neural Network Model for Estimation of Maximum Next Day Energy Generation Capacity of a Hydropower Station: A Case Study from Turkey

Abstract

Energy planning in a hydro power station (HPS) is essential for reservoir management, and to ensure efficient operation and financial usage. For robust energy planning, operators should estimate next day energy generation capacity correctly. This paper investigates use of a robust neural network model to estimate maximum next day energy generation capacity by using reservoir inflow rates for the previous four days, the current level of water in the reservoir, and the weather forecast for the Darıca-2 HPS in Ordu Province, Turkey. The generated energy in an HPS is directly dependent on the level of stored water in the reservoir, which depends on reservoir inflow. As the level of water in a reservoir varies during the year depending on climatic conditions, it is important to be able to estimate energy generation in an HPS to operate the HPS most effectively. This paper uses reservoir inflow data that has been collected daily during 2020 for the training phase of a neural network. The neural network is tested using a data set that has been collected daily during the first four months of 2021. Used neural network structure is called as LWNRBF (Linear Weighted Normalized Radial Basis Function) network, which is developed form of RBF network. In order to be able to be created valid model, LWNRBF network is trained with a two-pass hybrid training algorithm. After the training and testing stages, average training and testing error percentages have been obtained as 0.0012% and -0.0044% respectively.

Keywords

• hydro-electric power generation
• hydropower generation
• neural network
• reservoir inflow
• renewable energy sources

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