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

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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.

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Abbreviations

ϕ i :

Auto-regression coefficients

θ i :

Moving average coefficients

ε t :

Random error

y(t):

Output signal

w i, w o :

Weights matrices with respect to the input and the output

b o, b i :

Biases matrices with respect to the input and the output

w h1, w h1 :

Weights and biases matrices of the first hidden layer

β i :

Regression parameters

x(t):

Input signal

d x, d y :

Input and output time offsets

c ip :

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

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Authors and Affiliations

  1. Université de Sousse, Ecole Nationale d’Ingénieurs de Sousse, LATIS- Laboratory of Advanced Technology and Intelligent Systems, 4023, Sousse, Tunisia

    Sirine Essallah & Adel Khedher

Authors
  1. Sirine Essallah
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  2. Adel Khedher
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Correspondence to Sirine Essallah.

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Appendix

Appendix

See Tables 2 and 3.

Table 2 MAPE of different techniques as a function of forecasting horizon for a 5-year study period
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Table 3 MAPE of different techniques as a function of forecasting horizon for a study period higher than 5 years
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Essallah, S., Khedher, A. A comparative study of long-term load forecasting techniques applied to Tunisian grid case. Electr Eng 101, 1235–1247 (2019). https://doi.org/10.1007/s00202-019-00859-w

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