Abstract
ABSTRACT This paper presents the evaluation of a daily inflow forecasting model using a tool that facilitates the analysis of mathematical models for hydroelectric plants. The model is based on a Fuzzy Inference System. An offline version of the Expectation Maximization algorithm is employed to adjust the model parameters. The tool integrates different inflow forecasting models into a single physical structure. It makes uniform and streamlines the management of data, prediction studies, and presentation of results. A case study is carried out using data from three Brazilian hydroelectric plants of the Parana basin, Tiete River, in southern Brazil. Their activities are coordinated by Operator of the National Electric System (ONS) and inspected by the National Agency for Electricity (ANEEL). The model is evaluated considering a multi-step ahead forecasting task. The graphs allow a comparison between observed and forecasted inflows. For statistical analysis, it is used the mean absolute percentage error, the root mean square error, the mean absolute error, and the mass curve coefficient. The results show an adequate performance of the model, leading to a promising alternative for daily inflow forecasting.
Highlights
Hydroelectric operation planning is important even in countries with wide availability of water resources
Given the importance of inflow forecasting for hydropower generation systems, this paper presents an evaluation of TS-Fuzzy Inference System (FIS) model, previously presented by Luna et al 2009
The first was employed to evaluate the performance of the hydrologic models using the following metrics: Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and E
Summary
Hydroelectric operation planning is important even in countries with wide availability of water resources. Neural networks and fuzzy systems are versatile tools as they can be applied to several time series problems They are employed in situations where it is difficult to determine the physical process or when it is not possible to obtain a mathematical representation of the process (Bowden et al, 2005). Neural Networks and fuzzy systems models can process non-linear problems and complex data They are interesting for forecasting of hydrological data. The annual inflows are disaggregated into monthly samples and used for long-term hydropower scheduling The use of this tool for daily or hourly forecasting may be an interesting alternative that complements time series analysis for inflow modeling, usually performed in hydroinformatics via physical and conceptual models (Luna et al, 2009). The objective is to increase the quality of the forecasted water inflow, contributing to the choice of an operational policy that meets demand in an economical and safe way
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