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A Review on Short-Term Load Forecasting Using Different Techniques

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Recent Advances in Power Systems

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 812))

Abstract

The load prediction in the power system is very important. Logically, different methods and artificial intelligence (AI) have been applied in short-term load forecasting (STLF). This manuscript presents a survey of all load forecasting techniques. Every method and technique discussed in this review paper by evaluating their work in different areas of the energy system with its advantages and disadvantages. At last, a hybrid method is also presented.

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Abbreviations

STLF:

Short-Term Load Forecasting

LR:

Linear Regression

TS:

Time Series

GES:

General Exponential Smoothing

SS:

State Space

KBES:

Knowledge-Based Expert System

ALF:

Adaptive Load Forecasting

IRLS:

Iterative Reweighted Least Squares

SD:

Similar Day

DM:

Data Mining

FL:

Fuzzy Logic

NN:

Neural Network

WT:

Wavelet Transform

SVM:

Support Vector Machine

EA:

Evolutionary Algorithm

ANN:

Artificial Neural Network

HRESDR:

Hybrid Renewable Energy Sources and Demand Response

AR:

Autoregressive

MA:

Moving average

ARMA:

Autoregressive Moving average

ARIMA:

Autoregressive Integrated Moving average

SARIMA:

Seasonal Autoregressive Integrated Moving average

MAPE:

Mean Absolute Percentage Error

PV:

Photovoltaic

FC:

Fuel Cell

NVC:

Novel Voltage Controller

BESS:

Battery Energy Storage System

STATCOM:

Static Synchronous Compensator

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Acknowledgements

This research work was funded by “Woosong University’s Academic Research Funding - 2022”.

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Authors and Affiliations

  1. Department of Electrical Engineering, Veer Surendra Sai University of Technology, Burla, Odisha, India

    Saroj Kumar Panda & Papia Ray

  2. Department of Railroad and Electrical Engineering, Woosong University, Daejeon, Republic of Korea

    Surender Reddy Salkuti

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  1. Saroj Kumar Panda
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  2. Papia Ray
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  3. Surender Reddy Salkuti
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Correspondence to Surender Reddy Salkuti .

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Editors and Affiliations

  1. Department of Electrical Engineering, National Institute of Technology Jamshedpur, Jamshedpur, India

    Om Hari Gupta

  2. Faculty of Engineering and Applied Science, Ontario Tech University, Oshawa, ON, Canada

    Vijay Kumar Sood

  3. Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB, Canada

    Om P. Malik

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Panda, S.K., Ray, P., Salkuti, S.R. (2022). A Review on Short-Term Load Forecasting Using Different Techniques. In: Gupta, O.H., Sood, V.K., Malik, O.P. (eds) Recent Advances in Power Systems. Lecture Notes in Electrical Engineering, vol 812. Springer, Singapore. https://doi.org/10.1007/978-981-16-6970-5_33

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