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Machine Learning and Power System Planning: Opportunities and Challenges

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

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

Abstract

Machine learning (ML) methods and their applications are among the most innovative and attractive engineering topics. The advent of artificial intelligence has given birth to various tools widely used in science and engineering. ML is utilized in solving various problems in the power system engineering community, such as power system planning and operation. In this chapter, the authors will investigate different machine learning methods, and we will discuss their applications in solving power system planning problems, including load forecasting. The authors will discuss different ML methods used in the power engineering field, and other ML applications in planning problems such as optimization problems will be studied. This chapter’s main objective is to serve as an introduction to ML for power system planning and the basic concepts of the ML methods commonly used in this field.

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Abbreviations

b, bi, bC, bf, bo:

Corresponding biases

C t :

Cell state at time t

\( {\tilde{C}}_t \) :

Vector of new candidate values

f(x):

The plane that fits closest to the data

H t :

Output at timestamp t

n :

Total number of features

s t :

State at timestamp t

Wi, Wf:

Weight matrixes corresponding to the input and forget gates

W T :

Weights

x i :

Input label at time stamp i

x t :

Input at timestamp t

y i :

Output label at time stamp i

\( {y}_{j,t}^{-} \) :

The predicted value of feature j at time t

y j, t :

Actual value of feature j at time t

ρ :

Correlation coefficient

σ :

Standard deviation

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Author information

Authors and Affiliations

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

    Mohammad Hosein Asgharinejad Keisami, Sasan Azad & Reza Mohammadi Chabanloo

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

    Morteza Nazari-Heris & Somayeh Asadi

Authors
  1. Mohammad Hosein Asgharinejad Keisami
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  2. Sasan Azad
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  3. Reza Mohammadi Chabanloo
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  4. Morteza Nazari-Heris
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  5. Somayeh Asadi
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Corresponding author

Correspondence to Sasan Azad .

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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Asgharinejad Keisami, M.H., Azad, S., Mohammadi Chabanloo, R., Nazari-Heris, M., Asadi, S. (2021). Machine Learning and Power System Planning: Opportunities and Challenges. 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_3

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  • DOI: https://doi.org/10.1007/978-3-030-77696-1_3

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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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