Performance Evaluation of ANFIS and GA-ANFIS for Predicting Stock Market Indices

Ghashami, Farnaz; Kamyar, Kamyar

doi:10.5539/ijef.v13n7p1

Cited by 9 publications

(7 citation statements)

References 9 publications

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“…By converting the equality constraint (23) into an inequality constraint, we can relax (23) and rewrite it as follows 𝑐𝑐 𝑖𝑖𝐿𝐿 2 + 𝑠𝑠 𝑖𝑖𝐿𝐿 2 ≤ 𝑐𝑐 𝑖𝑖𝑖𝑖 𝑐𝑐 𝐿𝐿𝐿𝐿 , ∀𝑖𝑖, 𝑗𝑗 ∈ 𝑁𝑁 (25) The relaxed constraint (25) can be represented using a rotated SOC. If the relaxed constraint (25) becomes binding at optimality, then the proposed SOC relaxation is exact and constraint (25) is equivalent to the original power flow constraint (23) [43]. We further investigated the exactness of the proposed SOC relaxation through numerical calculations.…”

Section: Second-order Conic Relaxation Of Power Flow Equationsmentioning

confidence: 99%

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A Robust Second-Order Conic Programming Model with Effective Budget of Uncertainty in Optimal Power Flow Problem

Zhang¹,

Fakhrabadi²,

Bagheri³

et al. 2022

Preprint

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Integrating large-scale wind energy in modern power systems is demanding more efficient mathematical models to properly address classical assumptions in power system problems. In particular, there are two main assumptions in power system problems with wind integration that have not been adequately studied yet; First, non-linear AC power flow equations have been linearized in most of the literature. Such simplifications can lead to inaccurate power flow calculations that may result in other technical issues. Second, wind power uncertainties are inevitable and have been mostly modelled using the traditional uncertainty modelling approaches, that may not be suitable for large-scale wind power integration. In this paper, we address both challenges: we present a tight second-order conic relaxation (SOCR) for optimal power flow (OPF) problem, and simultaneously, implement the new effective budget of uncertainty approach for uncertainty modelling that determines the maximum wind power admissibility first and then addresses the uncertainty in the model. To the best of our knowledge, this is the first study that proposes an effective robust second-order conic programming (ERSOCP) model that simultaneously addresses the issues of power flow linearization and wind power uncertainty with the new paradigm on the budget of uncertainty approach. Our numerical results show the merit of the proposed model against traditional linearized power flow equations as well as traditional uncertainty modelling approaches.

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Section: Second-order Conic Relaxation Of Power Flow Equationsmentioning

confidence: 99%

“…SOCR of power flow equations: Eqs. ( 21), ( 22), ( 24), (25) Operational constraints: Eqs. ( 4)-( 17)…”

Section: Ersocp Modelmentioning

confidence: 99%

A Robust Second-Order Conic Programming Model with Effective Budget of Uncertainty in Optimal Power Flow Problem

Zhang¹,

Fakhrabadi²,

Bagheri³

et al. 2022

Preprint

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show abstract

“…Therefore, the uncertainty set must be modified based on the wind power admissibility before incorporating the budget of uncertainty in the model. By doing so, the ineffective part of the uncertainty set that does not affect the solution can be removed, and the effective budget of uncertainty can be obtained [25,38] to include in the model.…”

Section: Effective Budget Of Uncertainty In Power Systemsmentioning

confidence: 99%

“…In recent years, various prediction methods have been proposed to increase the prediction accuracy [22][23][24][25]. However, prediction errors are inevitable and can lead to severe problems in highly sensitive applications [26].…”

Section: Introductionmentioning

confidence: 99%

“…In particular, the adjustable budget of uncertainty method [28] adjusts the solution degree of conservatism by changing the total amount of uncertainty in the model, and the total amount of uncertainty is modeled using a parameter called the budget of uncertainty. This method has been extensively studied [9,12,[23][24][25][26][27][28][29][30][31]. Alternatively, two-stage RO models [5,26,[32][33][34][35][36][37] have been used in power systems, where the first stage decisions are made before realizing the actual wind power, and the second stage decisions are "wait-and-see" decisions that can be adjusted after the actual wind power output is known.…”

Section: Introductionmentioning

confidence: 99%

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A Robust Second-Order Conic Programming Model with Effective Budget of Uncertainty in the Optimal Power Flow Problem

Salama¹,

Khoshkhoo²,

Fakhrabadi³

et al. 2022

J Energ Power Technol

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Integrating large-scale wind energy in modern power systems necessitates high-efficiency mathematical models to address classical assumptions in power systems. In particular, two main assumptions for wind energy integration in power systems have not been adequately studied. First, nonlinear AC power flow equations have been linearized in most of the literature. Such simplifications can lead to inaccurate power flow calculations and result in technical issues. Second, wind power uncertainties are inevitable and have been mostly modeled using traditional uncertainty modeling techniques, which may not be suitable for large-scale wind power integration. In this study, we addressed both challenges: we developed a tight second-order conic relaxation model for the optimal power flow problem and implemented the novel effective budget of uncertainty approach for uncertainty modeling to determine the maximum wind power admissibility and address the uncertainty in the model. To the best of our knowledge, this is the first study that proposes an effective, robust second-order conic programming model that simultaneously addresses the issues of power flow linearization and wind power uncertainty with the new paradigm on the budget of uncertainty approach. The numerical results revealed the advantages of the proposed model over traditional linearized power flow equations and traditional uncertainty modeling techniques.

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Information fusion-based genetic algorithm with long short-term memory for stock price and trend prediction

Thakkar

Chaudhari²

2022

Applied Soft Computing

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Performance Evaluation of ANFIS and GA-ANFIS for Predicting Stock Market Indices

Cited by 9 publications

References 9 publications

A Robust Second-Order Conic Programming Model with Effective Budget of Uncertainty in Optimal Power Flow Problem

A Robust Second-Order Conic Programming Model with Effective Budget of Uncertainty in Optimal Power Flow Problem

A Robust Second-Order Conic Programming Model with Effective Budget of Uncertainty in the Optimal Power Flow Problem

Information fusion-based genetic algorithm with long short-term memory for stock price and trend prediction

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