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Erschienen in: Energy Systems 2/2020

14.01.2019 | Original Paper

Short-term electric load forecasting in Tunisia using artificial neural networks

verfasst von: Rim Houimli, Mourad Zmami, Ousama Ben-Salha

Erschienen in: Energy Systems | Ausgabe 2/2020

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Abstract

The accuracy of short-term electricity load forecasting is of great interest since it allows avoiding unexpected blackouts and lowering operating costs. In this paper, we aim to implement the artificial neural networks to model and forecast the half-hourly electric load demand in Tunisia over the period 2000–2008. To improve the quality of forecasts, the proposed artificial neural network model uses not only past electric load values as inputs, but also climatic and calendar variables. To determine the optimal structure of the neural network model, this paper employs the pattern search algorithm. Moreover, the neural network model is equipped with the Levenberg–Marquardt learning algorithm. Our findings confirm the performance of this algorithm to the view of evaluation indicators since the mean absolute percentage error values range between 1.1 and 3.4%. The analysis also shows the superiority of the Levenberg–Marquardt algorithm compared to the resilient back propagation algorithm and the conjugate gradient algorithm. In the light of the current research, we stress the aptness of the proposed artificial neural network model in forecasting short-term electricity demand.

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Fußnoten
1
While other meteorological and climatic factors, such as snow, fog, humidity, and wind speed, might affect electricity demand, we do not introduce them as inputs for two reasons. First, data on such climatic variables do not exist in Tunisia. Second, and most important, several authors, such as [3133] point out that temperature is the most important climatic determinant of electricity demand since it has a direct impact on it.
 
2
To conduct the study, we used the software Matlab R2013b. Particularly, two toolboxes have been used, namely the pattern search optimization toolbox and the neural network toolbox.
 
3
As mentioned earlier, there is a big similarity regarding the electric load during Tuesday, Wednesday and Thursday. Consequently, we focus only on Thursday in the rest of the paper.
 
4
Appendix A presents the formulas of the different measures employed in the analysis.
 
5
It is the fastest algorithm for the problem of model identification and function approximation. The memory space required for this algorithm is relatively small compared to other algorithms proposed by Matlab.
 
6
It is an iterative algorithm in a finite number of iterations. Its advantage in terms of computing time, due to a clever initialization (preconditioning), allows obtaining in only few steps close estimates. .
 
7
More details on the structure of the ANN using the pattern search optimization algorithm are displayed in Appendix B.
 
8
The correlation coefficient would be equal to one if the predicted values are equal the observed values. In this case, all the data points would fall on the fitted regression line.
 
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Metadaten
Titel
Short-term electric load forecasting in Tunisia using artificial neural networks
verfasst von
Rim Houimli
Mourad Zmami
Ousama Ben-Salha
Publikationsdatum
14.01.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
Energy Systems / Ausgabe 2/2020
Print ISSN: 1868-3967
Elektronische ISSN: 1868-3975
DOI
https://doi.org/10.1007/s12667-019-00324-4

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