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  4. Vol 7, Issue 11, 2023

Application of a Modified Grey Model Based on Least Squares in Energy Prediction

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DOI: 10.23977/acss.2023.071105 | Downloads: 9 | Views: 230

Author(s)

Ruizhi Li 1

Affiliation(s)

1 Jiangxi University of Technology, Nanchang, 330000, China

Corresponding Author

Ruizhi Li

ABSTRACT

The GM (1,1) model is a prediction method based on the grey system theory, which can be used to handle the prediction problem of time-series data. Compared with the traditional time series model, GM (1,1) model has the characteristics of a simple model, small calculation amount and small samples, so it has a wide application prospect in practical application. Grey GM (1,1) model is a commonly used prediction model in the energy industry, which can effectively deal with small amounts of data and incomplete data, as an accurate, reliable and efficient prediction model to predict energy consumption. In this paper, based on the classical grey GM (1,1) model, the constant free term is introduced, the modified grey GM (1,1) model is proposed, and the least squares method is used to construct an optimization problem related to the model parameters, and finally solve the general expression of the constant free term. Finally, the model is used to predict more accurately the per capita electricity consumption (kilowatt-hours). The results show that the improved GM (1,1) model is better than the traditional GM(1,1) model, which verifies the effectiveness and practicability of the improved model and is suitable for the forecast of per capita electricity consumption.

KEYWORDS

Grey GM (1,1) Model, Least Squares Method, Optimization Problem, Per Capita Electricity Consumption

CITE THIS PAPER

Ruizhi Li, Application of a Modified Grey Model Based on Least Squares in Energy Prediction. Advances in Computer, Signals and Systems (2023) Vol. 7: 27-34. DOI: http://dx.doi.org/10.23977/acss.2023.071105.

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