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The Adaptive Neuro-Fuzzy Inference System Model for Short-Term Load, Price, and Topology Forecasting of Distribution System

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Application of Machine Learning and Deep Learning Methods to Power System Problems

Part of the book series: Power Systems ((POWSYS))

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Abstract

This book chapter presents a short-term load, locational marginal price, and optimal topology-forecasting procedure for a distribution system that utilizes fossil-fueled distributed generation units. An intelligent hybrid model is proposed for forecasting of price and load based on mutual information and Kalman-Kohonen feature selection. The proposed method uses an adaptive neuro-fuzzy inference system. Further, the optimal topology of the system is forecasted based on the historical data and co-active neuro-fuzzy inference system. The feature selection determines the most proper inputs among a huge historical data. The proposed feature selection is based on Kalman-Kohonen model for load forecasting and adaptive neuro-fuzzy inference system model for price forecasting. The obtained results for a distribution system confirmed the model’s effective performance.

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Abbreviations

ANFIS:

Adaptive neuro-fuzzy inference system

ANN:

Artificial neural network

ARIMA:

Autoregressive integrated moving average

ARMA:

Autoregressive moving average

CANFIS:

Co-active neuro-fuzzy inference system

CSI:

Contingency severity index

DG:

Distributed generation

GA:

Genetic algorithm

GO:

Gravitational search optimization

LMP:

Locational marginal prices

LPF:

Load and price forecasting

MAPE:

Mean absolute percentage error

MLP:

Multilayer perceptron

SVM:

Support vector machine

SVR:

Support vector regression

WT:

Wavelet transform

l :

Decomposition index of wavelet decomposition

k :

Scaling index of wavelet decomposition

NB:

Number of buses

NCC:

Number of critical contingencies

NL:

Number of lines

P max :

Maximum active power flow of line

V min :

Minimum voltage of bus

α, β, χ :

Coarse and fine-scale coefficients of wavelet decomposition machine

W :

Weighting factor

P :

Active power flow of line

V :

Voltage of bus

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Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran

    Mehrdad Setayesh Nazar & Ashkan Eslami Fard

Authors
  1. Mehrdad Setayesh Nazar
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  2. Ashkan Eslami Fard
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Corresponding author

Correspondence to Mehrdad Setayesh Nazar .

Editor information

Editors and Affiliations

  1. Department of Architectural Engineering, Pennsylvania State University, University Park, PA, USA

    Morteza Nazari-Heris

  2. Department of Architectural Engineering, Pennsylvania State University, University Park, PA, USA

    Somayeh Asadi

  3. Department of Energy Technology Aalborg University, Aalborg, Denmark

    Behnam Mohammadi-Ivatloo

  4. Deakin University, Geelong, VIC, Australia

    Moloud Abdar

  5. Department of Architectural Engineering, Pennsylvania State University, University Park, PA, USA

    Houtan Jebelli

  6. Department of Architectural Engineering, Pennsylvania State University, University Park, PA, USA

    Milad Sadat-Mohammadi

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Setayesh Nazar, M., Eslami Fard, A. (2021). The Adaptive Neuro-Fuzzy Inference System Model for Short-Term Load, Price, and Topology Forecasting of Distribution System. In: Nazari-Heris, M., Asadi, S., Mohammadi-Ivatloo, B., Abdar, M., Jebelli, H., Sadat-Mohammadi, M. (eds) Application of Machine Learning and Deep Learning Methods to Power System Problems. Power Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-77696-1_15

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  • DOI: https://doi.org/10.1007/978-3-030-77696-1_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77695-4

  • Online ISBN: 978-3-030-77696-1

  • eBook Packages: EnergyEnergy (R0)

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