| 方明慧,苏 娟,屈 博,李德智,操 谦,崔振宇.多情景下基于改进粒子群-支持向量机的电能替代潜力预测方法[J].电力需求侧管理,2024,26(6):01-07 |
| 多情景下基于改进粒子群-支持向量机的电能替代潜力预测方法 |
| IPSO-SVM-based scenario prediction method for electric energy substitution |
| 投稿时间：2024-07-11 修订日期：2024-09-06 |
| DOI：10. 3969 / j. issn. 1009-1831. 2024. 06. 001 |
| 中文关键词: 电能替代 潜力预测 粒子群优化 支持向量机 多情景 |
| 英文关键词: electric energy substitution potential prediction particle swarm optimization support vector machine multiple scenarios |
| 基金项目:国家重点研发计划项目（2022YFB2403000） |
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| 摘要点击次数: 74 |
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| 中文摘要: |
| 电能替代潜力的有效分析对于制定电能替代发展策略和促进地方节能减排具有重要意义,分析不同情景下的电能替代发展趋势,可为地区电能替代发展规划提供科学依据。提出一种多情景下基于改进粒子群-支持向量机的电能替代潜力预测模型。分析了电力消费占比、单位GDP能耗、城市居民可支配收入和单位GDPCO2排放量等电能替代潜力影响因素指标,并进行量化处理,采用皮尔逊相关系数法筛选关键指标并引入预测模型。考虑基准发展、技术进步、经济发展和低碳环保4种发展情景,预测多情景下的电能替代潜力。对我国南方某省实际数据进行分析,将灰狼优化算法支持向量机（grey wolf optimizer-supportvector machine,GWO-SVM）和SVM模型作为对比模型,验证了所提方法具有较好的预测效果,并着重分析了2030年、2035年不同情景下的电能替代潜力,为地区未来的电能替代规划提供理论依据。 |
| 英文摘要: |
| Effective analysis of the potential for electric energy substitution is significant for formulating development strategies and promoting local energy conservation and emission reduction. Analyzing the development trends of electric energy substitution under different scenarios can provide a scientific basis for regional planning. An improved particle swarm optimization-support vector machine model is proposed for predicting the potential of electric energy substitution under multiple scenarios. It analyzes indicators influencing electric energy substitution potential, such as the proportion of electricity consumption, energy consumption per unit of GDP, disposable income of urban residents, CO2 emissions per unit of GDP, and quantifies these indicators. Pearson correlation coefficient method is used to screen and introduce indicators into the prediction model. Four development scenarios—baseline development, technological progress, economic development, and low-carbon environmental protection are considered for predicting the potential for electric energy substitution. Actual data from a province in southern China is analyzed, comparing results with grey wolf optimizer-support vector machine（GWO-SVM）and SVM models, validating that the proposed method demonstrates good predictive performance. The electric energy substitution potential in 2030 and 2035 under various scenarios is also analyzed, providing theoretical support for future regional electric energy substitution planning. |
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