Comparison of Inflow Forecasting Methods in Optimizing Operation and Maintenance Pattern Study Case: Bakaru Hydropower Plant
Keywords:
Inflow Forecasting, Run of River, Numerical, ANN, SVM, MLR, SARIMAAbstract
Hydropower is an exceptionally reliable and environmentally friendly power generation technology in today's energy landscape. Its eco-friendliness significantly aids in reducing greenhouse gas emissions. However, maintaining the reliability and performance of hydropower plants is paramount to ensuring a stable electricity supply. One of the crucial factors influencing the effectiveness of operation and maintenance planning for hydropower plants is the availability of water inflow. To achieve optimal performance and reliability, it's essential to have a clear understanding of how much water the plant can harness for energy generation. The primary objective of this paper is to conduct a comprehensive and in-depth exploration of various methodologies for forecasting water inflow, with a particular focus on empirical forecasting methods. These forecasting methods are essential in enhancing the planning and execution of operation and maintenance activities in water resource management and hydropower generation. In order to illustrate the practical use of these methodologies, the paper presents a case study on inflow forecasting for the Bakaru Hydropower Plant. This case study utilises empirical methods such as Multiple Linear Regression (MLR), Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Seasonal Autoregressive Integrated Moving Average (SARIMA) to predict water inflow patterns. The results of the case study indicate that the most effective method for information forecasting at the Bakaru Hydroelectric Power Plant is the Artificial Neural Network (ANN) method, with an R-squared value of 0.39 and a Root Mean Square Error (RMSE) of 25.15.
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