Verifying the Use of Evolving Fuzzy Systems for Multi-Step Ahead Daily Inflow Forecasting

Luna, Ivette; Lopes, João E. G.; Ballini, Rosângela; Soares, S.

doi:10.1109/isap.2009.5352814

Cited by 13 publications

(13 citation statements)

References 16 publications

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“…Moreover, it is of particular importance for the operation of hydropower reservoirs (e.g. Ballini et al (2001), Cheng et al (2008), Coulibaly et al (2000), Luna et al (2009)) and, by extension, for the energy industry, especially if we consider that hydropower is a form of energy both reliable and sustainable (Koutsoyiannis 2011). …”

Section: Introductionmentioning

confidence: 99%

Error Evolution in Multi-Step Ahead Streamflow Forecasting for the Operation of Hydropower Reservoirs

Papacharalampous¹,

Tyralis²,

Koutsoyiannis³

2017

Preprint

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Multi-step ahead streamflow forecasting is of practical interest for the operation of hydropower reservoirs. We provide generalized results on the error evolution in multi-step ahead forecasting by conducting several large-scale experiments based on simulations. We also present a multiple-case study using monthly time series of streamflow. Our findings suggest that some forecasting methods are more useful than others. However, the errors computed at each time step of a forecast horizon within a specific case study strongly depend on the case examined and can be either small or large, regardless of the forecasting method used and the time step of interest.

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Section: Introductionmentioning

confidence: 99%

Error Evolution in Multi-Step Ahead Streamflow Forecasting for the Operation of Hydropower Reservoirs

Papacharalampous¹,

Tyralis²,

Koutsoyiannis³

2017

Preprint

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“…Luna, et al, have applied fuzzy inference system (FIS) approach and have concluded that FIS approach is better than Soil Moisture Accounting Procedure (SMAP) approach .it is promising for daily runoff forecasting [192]. Sihui, has developed single element medium and long-term classification forecast model and the result indicates that the forecast model can describe the relationship between forecast factors and forecast object efficiently and accurately, the model is more credible, if the quantity of swatches is larger [193]. Guo, et al, have designed rough set theory (RST) approach, RST not only reduced the dimension and noise of the predictor variables dataset greatly in each grid, but also enhanced the performance of statistical downscaling models [194].…”

Section: Rainfall-runoff Modelingmentioning

confidence: 99%

Appropriateness of Neural Networks in Climate Prediction and Interpolations: A Comprehensive Literature Review

Karmakar¹,

Choubey²,

Mishra³

2016

IJAIS

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To be familiar with appropriateness of Neural Network in climate prediction and spatial interpolation, e comprehensive literature review of past 50 years is done and offered in this paper. And it is established that Neural Network such as BPN, RBF is best appropriate to be predicted chaotic behavior of climate variables like rainfall, rainfall runoff, and have efficient enough for prediction in long period. It is also found that Neural Network is significant for spatial interpolation of mean climate variables.

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“…The use of the FIS-EM reveals an adequate performance for forecasting tasks if one assumes a normal distribution over a set of different applications, including monthly inflow forecasting [11], [5]. However, the normality hypotheses about the inflow distribution affects the FIS performance considerably when used for the generation of synthetic inflows.…”

Section: Figure 2 Shows the Observed Histograms For The Furnas And Pementioning

confidence: 99%

“…Fuzzy rule-based systems and fuzzy clustering algorithms are another option which are widely used for inflow forecasting [5], [6], [7]). These papers have concluded that fuzzy models are able to deal with nonlinearities inherent in hydrological processes and that they provide an adequate performance in forecasting tasks.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy inference systems for synthetic monthly inflow time series generation

Luna¹,

Ballini²,

Soares³

et al. 2011

Proceedings of the 7th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011)

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Inflow data plays an important role in water and energy resources planning and management. In general, due to the limited availability of historical inflow data, synthetic streamflow time series have been widely used for several applications such as mid-and long-term hydropower scheduling and the identification of hydrological processes. This paper explores the use of fuzzy inference systems for the identification of two hydrological processes, and its use in the generation of synthetic monthly inflow sequences. Experiments using Brazilian monthly records show that fuzzy systems provide a promising approach for synthetic streamflow time series generation.

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Verifying the Use of Evolving Fuzzy Systems for Multi-Step Ahead Daily Inflow Forecasting

Cited by 13 publications

References 16 publications

Error Evolution in Multi-Step Ahead Streamflow Forecasting for the Operation of Hydropower Reservoirs

Error Evolution in Multi-Step Ahead Streamflow Forecasting for the Operation of Hydropower Reservoirs

Appropriateness of Neural Networks in Climate Prediction and Interpolations: A Comprehensive Literature Review

Fuzzy inference systems for synthetic monthly inflow time series generation

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