基于遗传算法的中国清洁能源需求Logistic预测模型

Logistic Forecast Model of Clear Energy Requirement Based on Genetic Algorithm

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。

According to analysis of the variation of energy requirement in China,it finds that the trend has greatly changed during the passed 10 years.In order to modeling the trend of energy requirement,the data sequences in passed 10 years and the total data sequences are used to build the Logistic model,respectively.Then the parameters of coupling two models are optimized by using genetic algorithm and the long-term Logistic forecast model of energy requirement are obtained.The proposed model is used to forecast the energy requirement of China in 2020 and construct the scene of economic development for future society.Clear energy requirement is also determined by considering the objective of reduction carbon emission.Based on genetic algorithm,the long-term Logistic forecast model of clear energy requirement is established and it is used to analyze the clear energy requirement of China in 2020.The results show that the proposed model has characteristics of explicit physical concept and clarity of thinking;the forecasted results accord with the goal of energy planning in China and it has certain rationality.