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| “双碳”背景下基于模糊自回归分布滞后模型的电力需求预测方法研究 |
| Research on the Method of Electricity Demand Prediction Based on Fuzzy Autoregressive Distributed Lag Model under the Background of "Dual Carbon" |
| 投稿时间：2024-10-14 修订日期：2025-01-21 |
| DOI： |
| 中文关键词: “双碳”目标 电力需求预测 LMDI分解 模糊自回归分布滞后模型 模糊度优化 |
| 英文关键词: "Dual carbon" target electricity demand prediction LMDI decomposition fuzzy autoregressive distributed lag model fuzzy optimization |
| 基金项目:国家电网有限公司华东分部科技项目(项目编号:52992424001A)； 国家自然科学基金资助项目(52307119)； |
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| 中文摘要: |
| “双碳”目标的驱动下,社会经济发展、能源生产及消费结构等将发生较大变化,导致电力需求预测影响因素、变化趋势等也会呈现新的特点,电力行业是保障“双碳”目标切实实现的重点领域,因此有必要对新形势下的电力需求预测开展研究。本文首先基于对数平均迪式分解模型(Logarithmic Mean Index Method,LMDI)和通径分析方法研究“双碳”背景下电力需求影响因素,提取得到电气化率、清洁能源发电比例和能源强度三项碳排放相关的电力需求相关因素。进一步提出了考虑碳约束的基于模糊自回归分布滞后模型的电力需求预测方法,在考虑政策时滞效果的自回归分布滞后模型基础上将回归系数模糊化,通过建立最小模糊度优化模型,得到不确定性最小的回归参数,提升了“双碳”背景下远景年电力需求预测精度。采用我国1998-2022年的电力需求及社会经济因素等历史数据集,结合国家政策目标,对不同低碳路径下我国电力需求进行了预测,对比验证了本文所提出的电力需求预测方法的可行性与有效性。 |
| 英文摘要: |
| Driven by the "dual carbon" goal, significant changes will occur in social and economic development, energy production, and consumption structure, leading to new characteristics in factors and trends affecting electricity de-mand forecasting. The power industry is a key area to ensure the practical achievement of the "dual carbon" goals, so it is necessary to conduct research on electricity demand forecasting under the new situation. Logarithmic Mean Index Method (LMDI) and path analysis method are used to study the influencing factors of electricity demand under the background of "dual carbon", and extracts three carbon emission related influencing factors of electricity demand: electrification rate, clean energy generation ratio, and energy intensity. A method for predicting electricity demand based on fuzzy autoregressive distributed lag model was proposed. The regression coefficients were fuzzi-fied on the basis of the autoregressive distributed lag model considering policy lag effects. By establishing a min-imum fuzziness optimization model, the regression parameters with the least uncertainty were obtained, which improved the accuracy of long-term electricity demand prediction under the background of "dual carbon". Based on China's historical data sets from 1998 to 2022, a case study was produced to verify the feasibility and effective-ness of the power demand forecasting method proposed in this article. |
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