Energy demand prediction method based on PSO-GA-SA algorithm

Abstract

The invention discloses an energy demand prediction method based on a PSO-GA-SA algorithm. The method includes the following steps: selecting six energy demand impact factors including GDP, population, fixed-asset investment, energy efficiency, energy consumption structure and per capita energy consumption and taking the six factors as the input quantity of an energy demand assessment equation; adopting a secondary non-linear energy demand assessment equation; employing a PSO-GA-SA algorithm to obtain the optimal weight coefficient of the secondary non-linear energy demand assessment equation; and obtaining an energy demand prediction result in China by means of the secondary non-linear energy demand assessment equation and the six energy demand impact factors including GDP, population, fixed-asset investment, energy efficiency, energy consumption structure and per capita energy consumption. According to the method, the maximum relative error between an energy predicted value and an observed value can be controlled to be not more than 0.009%, the mean absolute percentage error (MAPE) is only 0.004%, and the prediction accuracy is high.

Description

technical field [0001] The present invention relates to an energy demand forecasting method, in particular to an energy demand forecasting method based on the PSO-GA-SA algorithm, which is used for medium and long-term energy demand forecasting, and is used as a basis for decision-making by energy management departments at all levels, and is directly applied to energy exploration , development, production, utilization, transportation, trade and other fields. Background technique [0002] As the world's largest energy consumer, China's energy demand not only directly affects my country's energy security, but also occupies a pivotal position in the global energy market. Therefore, accurate prediction of China's future energy demand is of great practical significance for rational planning of energy and ensuring sustainable and healthy economic development. [0003] Scholars and research institutions at home and abroad have adopted many technical means to predict energy consump...

AI Technical Summary

Problems solved by technology

[0005] In the prior art, the invention patent with the publication number CN102646216A "Energy Demand Forecasting Method Based on "S" Shape Model" introduces an energy demand forecasting method based on the PSO-GA-SA algorithm, which is based on The "S"-shaped physical model between per capita GDP and per capita energy consumption is a forecasting technology constructed using mathematical methods such as hyperbolic tangent functions. The energy impact factors selected by this energy demand forecasting method are relatively single, which cannot fully and accurately reflect China's energy demand factors

[0006] However, the existing models still have the following deficiencies: First, the energy system is a complex system, which is affected and restricted by various factors in the process of its development and evolution

Unfortunately, however, the PSO algorithm tends to fall into a local minimum, that is, the algorithm has a tendency to converge prematurely

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