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Accurate prediction of the future electricity consumption is crucial for production electricity management. Since the storage of electrical energy is very difficult, reliable and accurate prediction of power consumption is important. Different approaches for this purpose were used. In this paper, Grey model (1,1) based on grey system theory has been used for forecasting results. Annual electricity consumption and forecasting data in Mazandaran were used as our case study. Root mean squared error, Mean absolute error and Mean of average percentage error accuracy testing results show that GM(1,1) is outperformed compared with model fitting and model forecasting.

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The purpose of this paper is describes the use of grey pridiction in linear programming problems. Some definitions and concepts of grey system theory are introduced and then, we introduced GM(1,1) and fractional order accumulation into grey model. Due to the fluctuation of prices and the lack of certainty data in the market, optimal production was calculated to optimize the profit from sales using the grey prediction. The use of grey prediction models will be very useful in optimization problems and can provide decision-makers with an acceptable level. Prediction of the profit or cost of goods and services given the fluctuation of prices and, in some cases, the deficiency of data needed to make a decision can accurately reflect the power of predictive grey that uses very small data.