Wang Zhihui, Hu Long, Huang Xiaojia, Tan Jieren, Ye Kaihui
Guangzhou Power Supply Bureau, Guangdong Power Grid, Co., Ltd., CSG, Guangzhou, 510000, People's Republic of China.
Guangdong Electric Power Design Institute Co., Ltd., Guangzhou, 510000, People's Republic of China.
Sci Rep. 2024 Feb 15;14(1):3812. doi: 10.1038/s41598-024-54317-0.
The study investigates the optimization of life cycle carbon emissions in smart sustainable energy systems through power transformation and transmission project power load predictions. Firstly, a multi-task learning-based short-term user load forecasting technique is developed, where the power load curves of multiple residential customers are grouped and classified using the K-means clustering method. Additionally, the Bidirectional Long Short-Term Memory (BiLSTM) technique is introduced to anticipate the power load intelligently. Secondly, a life cycle carbon emission assessment model for the power transmission and transformation project (PTTP) is constructed based on the life cycle assessment (LCA) method, which divides the project's life cycle into four stages: production, installation and construction, operation and maintenance, and demolition. Finally, an experimental evaluation of this model is conducted. The results demonstrate that compared with the baseline model Long Short-Term Memory (LSTM), this model achieves a significantly lower average Mean Absolute Error (MAE) at 3.62% while achieving significantly higher accuracy in power load forecasting at 94.34%. A comprehensive examination of carbon emissions across all four phases reveals that overall carbon emissions are highest during the operation and maintenance stage followed by the equipment production stage and installation/construction stage, with the lowest overall carbon emissions observed. Hence, this study endeavors to forecast power load demand with precision and identify the principal determinants of carbon emissions in power engineering. By discerning and managing these key factors, an optimal, energy-efficient intelligent power load scheme can be derived.
该研究通过电力变换与输电项目的电力负荷预测，探讨智能可持续能源系统中生命周期碳排放的优化。首先，开发了一种基于多任务学习的短期用户负荷预测技术，使用K均值聚类方法对多个住宅用户的电力负荷曲线进行分组和分类。此外，引入双向长短期记忆（BiLSTM）技术来智能预测电力负荷。其次，基于生命周期评估（LCA）方法构建了输变电项目（PTTP）的生命周期碳排放评估模型，该模型将项目生命周期分为四个阶段：生产、安装与建设、运行与维护以及拆除。最后，对该模型进行了实验评估。结果表明，与基线模型长短期记忆（LSTM）相比，该模型的平均平均绝对误差（MAE）显著降低，为3.62%，同时在电力负荷预测方面的准确率显著提高，达到94.34%。对所有四个阶段的碳排放进行综合考察发现，总体碳排放在运行与维护阶段最高，其次是设备生产阶段和安装/建设阶段，总体碳排放在拆除阶段最低。因此，本研究致力于精确预测电力负荷需求，并确定电力工程中碳排放的主要决定因素。通过识别和管理这些关键因素，可以得出最优、节能的智能电力负荷方案。
