基于改进的粒子群优化BP神经网络浙江电能替代潜力预测模型

近年来,中国煤炭等化石能源占终端能源消费的比例偏高,引起了严重的环境污染和能源资源的浪费,为了实现经济社会的绿色、可持续发展,中国提出了在终端能源消费环节实施电能替代的发展战略。因此,为了更精确地对电能替代潜力预测,基于改进的GRA-IPSO-BP模型,基于电能替代潜力影响因素的量化指标,构建了基于改进的GRA-IPSO-BP电能替代潜力预测模型。以浙江地区为例,拟合浙江地区电能替代电量的历史变化规律,并对浙江地区未来电能替代电量进行预测,研究方法有助于判断电能替代发展水平,有助于电能替代工作的推进。

Zhejiang Province Electric Power Substitution Potential Prediction Model Based on Improved Particle Swarm Optimization BP Neural Network

In recent years, China''s coal and other fossil energy accounts for a high proportion of terminal energy consumption, causing serious environmental pollution and waste of energy resources. In order to achieve green and sustainable development of the economy and society, China has proposed to implement energy in the terminal energy consumption. Alternative development strategy. Therefore, based on the improved GRA-IPSO-BP model, based on the quantitative indicators of the factors affecting the potential of electric energy replacement, an improved GRA-IPSO-BP alternative energy potential prediction model is constructed. Taking Zhejiang area as an example, this paper fits the historical change law of electric energy substitution in Zhejiang area, and predicts the future electric energy substitution in Zhejiang area. The research method in this paper is helpful to judge the level of electric energy replacement development and contribute to the advancement of electric energy substitution work.