上海华瑞众信技术有限公司 张正芳，田海涛

摘要：为解决工业场景下能碳数据时序性分析滞后、边缘端实时决策能力不足、系统部署成本高等问题，本研究提出将长短期记忆网络（LSTM）技术与能碳融合一体机结合的数字化能碳管理中心建设方案。首先阐述LSTM网络的门控机制与时序数据处理优势，通过对比分析其与Transformer等架构在时间复杂度、硬件适配性等维度的差异，验证其在边缘计算场景的适用性；其次设计能碳融合一体机的硬件架构与软件功能模块，集成数据采集、LSTM模型推理、实时决策等核心能力；最后通过能耗预测、碳排放核算、可再生能源生产预测及设备故障预警四大典型应用场景的实证分析，验证该方案在预测准确率（准确率＞85%）及部署灵活性方面的技术优势。研究结果表明， LSTM技术与能碳融合一体机的深度融合，可实现能碳管理的数字化、智能化与边缘部署落地，为工业企业“双碳”目标达成提供高效技术支撑。

关键词：LSTM；能碳融合一体机；边缘计算；数字化能碳管理；时序数据建模

Abstract: To address issues such as lagging time-series analysis of energy and carbon data, insufficient real-time decision-making capabilities at the edge, and high system deployment costs in industrial scenarios, a construction plan for a digital energy and carbon management center is proposed, integrating Long Short-Term Memory (LSTM) technology with an energy and carbon fusion all-in-one machine. Firstly, the gating mechanism of the LSTM network and its advantages in time-series data processing are elaborated. By comparing and analyzing its differences from architectures such as Transformer in terms of time complexity and hardware adaptability, its applicability in edge computing scenarios is verified. Secondly, the hardware architecture and software functional modules of the energy and carbon fusion all-in-one machine are designed, integrating core capabilities such as data collection, LSTM model inference, and real-time decision-making. Finally, through empirical analysis of four typical application scenarios: energy consumption prediction, carbon emission accounting, renewable energy production prediction, and equipment failure warning, the technical advantages of this plan in terms of prediction accuracy (accuracy > 85%) and deployment flexibility are verified. The research results show that the deep integration of LSTM technology with the energy and carbon fusion all-in-one machine can achieve digitalization, intelligence, and edge deployment implementation of energy and carbon management, providing efficient technical support for industrial enterprises to achieve their "dual carbon" goals.

Key words: LSTM; Energy-Carbon Fusion All-in-One Machine; Edge Computing; Digital Energy-Carbon Management; Temporal Data Modeling Translation

在线预览：LSTM技术在数字化能碳管理中心建设中的应用研究.pdf

摘自《自动化博览》2026年第一期暨《2026具身智能专刊》