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image of A Multivariate Intuitionistic Fuzzy Grey Model for Forecasting Electricity Consumption

Abstract

Background

Whether in the short, medium or long term, forecasting electricity consumption has always been an essential study area. In the literature, many methods are used for future forecasting and are being improved daily to achieve better results.

Objective

The main objective of this study is to make the most accurate long-term electricity consumption forecast, which is the basis for optimal future planning in the energy sector. Electric consumption forecasting is performed regionally since planning at the regional level is essential for more precise planning.

Methods

There may be different variables that affect electricity consumption. This study extends the multivariate grey model for electricity consumption prediction to intuitionistic triangular fuzzy numbers for nine regions. In the grey model, population, export, and gross domestic product variables were used as independent variables, and future predictions for these variables were obtained through the univariate intuitionistic triangular fuzzy grey model.

Results

The results of the proposed method are compared with those of the classical univariate grey model, univariate intuitionistic triangular fuzzy grey model, and classical multivariate grey model. The results show that the error values of the proposed method are lower.

Conclusion

The study contributes to the development of the grey model. More accurate prediction results are obtained with the proposed method compared to similar methods

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2024-12-23
2025-03-07

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A Multivariate Intuitionistic Fuzzy Grey Model for Forecasting Electricity Consumption
Bentham Science Publishers ; https://doi.org/10.2174/0126662949338213241010120212
/content/journals/iscce/10.2174/0126662949338213241010120212
/content/journals/iscce/10.2174/0126662949338213241010120212
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  • Article Type:     Research Article
Keywords: national economy ; forecasting ; intuitionistic fuzzy set ; multivariate grey model ; regional forecast ; Electricity consumption
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