原文信息：
Multi-scale electricity consumption prediction model based on land use and interpretable machine learning: A case study of China
原文链接：
https://doi.org/10.1016/j.adapen.2024.100197
Highlights
• 从土地利用这一新兴视角，提出了一种高性能、可解释、多尺度适配的电力消耗预测模型；
• 分析土地利用承载电力消费的机制，基于时空大数据与机器学习实现土地利用进行精细化处理与功能识别；
• 揭示了土地利用变化对电力消费的边际影响特征曲线；
• 建立了中国全国-省-市-区县过去10年多尺度电力消费表征与未来10年电力消费预测数据集；
• 识别电力消费的空间统计学特征，评估2505个区县的可再生能源资源禀赋。
Abstract
The prediction of electricity consumption plays a vital role in promoting sustainable development, ensuring energy security and resilience, facilitating regional planning, and integrating renewable energy sources. A novel electricity consumption characterization and prediction model based on land use was proposed. This model achieves land-use subdivision to provide highly correlated variables; exhibits strong interpretability, thereby revealing even marginal effects of land use on electricity consumption; and demonstrates high performance, thereby enabling large-scale simulations and predictions. Using 297 cities and 2,505 counties as case studies, the key findings are as follows: (1) The model demonstrates strong generalization ability (Testing R² = 0.91), high precision (Kappa = 0.77), and robustness, with an overall prediction accuracy exceeding 80 %; (2) The marginal impact of industrial land on electricity consumption is more complex, with more efficiency achieved by limiting its area to either 104.3 km² or between 288.2 and 657.3 km²; (3) The marginal impact of commercial and residential land on electricity consumption exhibits a strong linear relationship (R² > 0.80). Restricting the scale to 11.3 km² could effectively mitigate this impact. Mixed commercial and residential land is advantageous for overall electricity consumption control, but after exceeding 43.5 km², separate layout considerations for urban residential land are necessary; (4) In 2030, Shanghai's electricity consumption is projected to reach 155,143 million kW·h, making it the highest among the 297 cities. Meanwhile, Suzhou Industrial Park leads among the 2,505 districts with a consumption of 30,996 million kW·h; (5) Identify future electricity consumption hotspots and clustering characteristics, evaluate the renewable energy potential in these hotspot areas, and propose targeted strategies accordingly.
Keywords
Electricity consumption
Land use
High-performance prediction model
Interpretable machine learning
Multi scale spatial characterization
Graphics
图1. 图摘要
图2. 研究技术路线图：基于土地利用的电力消费预测模型
图3. 面向电力消费表征预测的中国土地利用功能识别
图4. 土地利用对电力消费的边际影响
图5. 城市级电力消费预测结果
图6. 区县级电力消费预测结果
图7. 热点区域的替代性可再生能源资源禀赋评估
作者简介
团队介绍：
本研究由西安交通大学、厦门大学以及曼彻斯特大学等机构的研究人员共同完成。研究团队长期从事城市－园区－楼宇多尺度的能耗监测、表征、预测与规划研究，在应用能源及碳排放核算相关领域发表SCI论文40余篇。
延伸阅读：
该研究团队近期还在Applied Energy发表了基于土地利用的碳排放预测模型，欢迎关注与研讨：
【Applied Energy最新原创论文】基于土地利用和可解释机器学习模型的多尺度碳排放表征与预测—以中国长江三角洲地区为例：
https://mp.weixin.qq.com/s/bEx_NqJIiU_XBJY3agUOlA
【Applied Energy最新原创论文】基于土地利用的城市尺度碳排放预测模型研究：以中国西安市为例：
https://mp.weixin.qq.com/s/qoUidQW-UylqBnw_mTtUXg
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