Fuzzy Inference Systems for Multi-Step Ahead Daily Inflow Forecasting

Luna, Ivette; Hidalgo, Ieda Geriberto; Pedro, P.; Barbosa, Paulo Sérgio Franco; Francato, Alberto Luiz; Correia, Paulo B.

doi:10.1590/0101-7438.2017.037.01.0129

Cited by 5 publications

(3 citation statements)

References 18 publications

(9 reference statements)

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“…Luna et al [15] presented a TS-FIS model for inflow forecasting. The validation of the model has been performed using MAPE, RMSE, and MAE.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Artificial Intelligence for the Optimization of Hydropower Energy Generation

Kumar¹,

Saini²

2021

EAI Endorsed Transactions on Industrial Networks and Intelligen

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Hydropower is one of the most promising sources of renewable energy. However, a substantial initial investment requires for the construction of large civil structures. Feasibility study, detailed project report preparation, construction planning, and timely execution of work are the important activities of a hydropower plant. Energy generation in hydropower plants are mainly depends on discharge and head. Therefore, an accurate estimation of discharge and head is important to decide the plant capacity. Erosion, cavitation, and operation & maintenance are the key challenges in hydropower energy generation. Artificial Intelligence (AI) has become popular, which can be utilized for site selection, parameters assessment, and operation & maintenance optimization. In this paper, a literature review on applications of AI in hydropower has been presented, and an attempt has also been made to identify the future potential areas of hydropower plants.

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“…Luna et al [15] presented a TS-FIS model for inflow forecasting. The validation of the model has been performed using MAPE, RMSE, and MAE.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Artificial Intelligence for the Optimization of Hydropower Energy Generation

Kumar¹,

Saini²

2021

EAI Endorsed Transactions on Industrial Networks and Intelligen

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“…On the other hand, rainfall‐runoff models are used for daily inflow forecasting (ONS, 2005). In this sense, the literature also suggests a variety of alternatives for the Brazilian case, such as the SMAP models and other computational intelligence based approaches (Luna et al., 2017). The ONS nowadays adopts a revised version of the SMAP model for short‐term inflow forecasting at some hydroplants of the national interconected system (SIN) (Freitas, 2018).…”

Section: Case Studymentioning

confidence: 99%

Recursive linear models optimized by bioinspired metaheuristics to streamflow time series prediction

Siqueira

Belotti

Boccato

et al. 2020

Int Trans Operational Res

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Time series forecasting problems are often addressed using linear techniques, especially the autoregressive (AR) models, due to their simplicity combined with good performances. It is possible to generalize a linear predictor by allowing infinite impulse response (IIR) through the addition of feedback loops, as occurs in the autoregressive and moving average (ARMA) models and IIR filters. However, the calculation of the free coefficients of these structures is more complex, as the optimization problem has no closed-form solution. This work conducts an extensive investigation on the use of linear models to predict monthly seasonal streamflow series associated with Brazilian hydroelectric plants. The main goal is to reach the best achievable performance with linear approaches. We propose the application of recursive models, estimating their parameters with the aid of bioinspired metaheuristics: particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), and two immune-inspired algorithms, the CLONALG, and the artificial immune network for optimization (Opt-aiNet). The AR model is also considered. The results to multistep ahead forecasting indicated that the insertion of feedback loops increased the performances, with ARMA being the best predictor. The DE, PSO, and GA led to the minimum values of mean-squared error during the tests, while DE yielded the smallest dispersion.

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“…Besides neural networks, another popular soft computing tools which is used for time series prediction is Fuzzy Inference System (FIS), e.g. [8,9]. To the best our knowledge, there were no attempts to use FIS for forecasting cryptocurrency time series.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Cryptocurrency Time Series Using Fuzzy Transform, Fourier Transform and Fuzzy Inference System

Jelinek¹,

Poledica

Petrović

et al. 2019

Proceedings of the 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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In this paper we propose a new approach for forecasting the cryptocurrency time series, which combines the fuzzy transform and the fuzzy inference system. We also test whether fuzzy transform yields better results forecasting results in comparison to Fourier transform. Finally, we will investigate whether fuzzy rules used in fuzzy inference system can successfully capture high and low volatility moments in the time series, as well as high correlations between the three different cryptocurrencies.

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Fuzzy Inference Systems for Multi-Step Ahead Daily Inflow Forecasting

Cited by 5 publications

References 18 publications

Application of Artificial Intelligence for the Optimization of Hydropower Energy Generation

Application of Artificial Intelligence for the Optimization of Hydropower Energy Generation

Recursive linear models optimized by bioinspired metaheuristics to streamflow time series prediction

Forecasting Cryptocurrency Time Series Using Fuzzy Transform, Fourier Transform and Fuzzy Inference System

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