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A comparative study of long-term load forecasting techniques applied to Tunisian grid case

  • Sirine EssallahEmail author
  • Adel Khedher
Original Paper
  • 37 Downloads

Abstract

The rapid development of population, economy and technology currently has led to the fast increase in electric consumption. Therefore, efficient energy management and load forecasting have been flagged as a very important need for electric networks planning and operation. Recently, different techniques have been developed to deal with load forecasting. However, the stochastic nature and uncertainty characteristics of the electric load make demand forecasting a challenging task for electric utilities where no technique can be identified as the most efficient one. It becomes therefore relevant to identify the technique that best fits a specific dataset. Thus, in this paper we propose a comparative study of four techniques for long-term load forecasting, namely Box–Jenkins, multiple linear regressions, MLR and nonlinear autoregressive neuronal networks with and without exogenous variables, NARX and NAR. The study is conducted on the Tunisian electric consumption, and the performance of different techniques is evaluated as a function of historical data and forecasting period’s variations. Quantitative and qualitative assessments of results are reported in this study in order to pinpoint the strengths and weaknesses of the different assessed forecasting techniques.

Keywords

Forecasting Long-term Box–Jenkins NAR MLR NARX 

List of symbols

ϕi

Auto-regression coefficients

θi

Moving average coefficients

εt

Random error

y(t)

Output signal

wi, wo

Weights matrices with respect to the input and the output

bo, bi

Biases matrices with respect to the input and the output

wh1, wh1

Weights and biases matrices of the first hidden layer

βi

Regression parameters

x(t)

Input signal

dx, dy

Input and output time offsets

cip

Constants of simple linear regressions

ARIMA

Autoregressive integrated moving average

EXGS

Exports of goods and services

GDP

Gross domestic product

IMGS

Imports of goods and services

IT

Internet subscriptions

MAPE

Mean absolute percentage error

MCS

Mobile cellular subscriptions

MLR

Multiple linear regression

NAR

Nonlinear autoregressive neuronal networks

NARX

Nonlinear autoregressive neuronal networks with exogenous variables

SSE

Secondary school enrollment rate

TF

Fixed telephone lines

Notes

References

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Université de Sousse, Ecole Nationale d’Ingénieurs de SousseLATIS- Laboratory of Advanced Technology and Intelligent SystemsSousseTunisia

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