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通过分析中国历年能源需求量变化规律,得出序列近10 a增长趋势与以往明显不同。为准确描述能源需求增长趋势,用整体序列和近10 a序列分别建立Logistic模型,再将两者耦合,并运用遗传算法优化模型参数,由此建立基于遗传算法的能源需求Logistic中长期预测模型。运用该模型对中国2020年能源需求量进行预测,并构建未来社会经济发展情景,结合碳减排目标推求清洁能源需求量,由此建立基于遗传算法的清洁能源Logistic中长期预测模型,并对中国2020年清洁能源需求量进行预测分析。结果表明,该模型物理概念明确,思路清晰,预测结果与中国能源规划目标相符,具有一定的合理性。
By analyzing the changing law of energy demand in China over the years, it is concluded that the growth trend of the past 10 years is obviously different from the past. In order to accurately describe the trend of energy demand growth, the Logistic model was established by using the whole sequence and nearly 10-year sequence respectively, and then the two models were coupled. The genetic algorithm was used to optimize the model parameters to establish the medium- and long-term energy demand Logistic model based on genetic algorithm. This model is used to predict China’s energy demand in 2020 and to build a scenario for future socio-economic development. Based on the target of carbon emission reduction, the demand for clean energy is deduced. Based on this, a medium- and long-term forecast model of clean energy based on genetic algorithm is established. 2020 clean energy demand forecast analysis. The results show that the model has a clear physical concept and a clear idea, and the prediction result is in line with the goal of China’s energy planning. It is reasonable.