Paper Citation Record · LEDGER

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework

As of 21 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2607.26560.

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2607.26560 v1

Coverage vector

measured 41 of 41 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

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Outbound references

Observation 86ac2a83-bcc8-4901-8178-9e6cd441d128 · outbound

This paper cites Energy Requirements and Carbon Emissions for a Low-Carbon Energy Transition,.

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Energy Requirements and Carbon Emissions for a Low-Carbon Energy Transition,

Reference 1

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This paper cites an unresolved cited work.

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Unresolved cited work

Reference 2

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Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Unresolved cited work

Reference 3

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Observation c0cfdd4f-44ed-4be6-8e59-a57a8adc69ea · outbound

This paper cites Emissions of Carbon Dioxide in the Electric Power Sector,.

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Emissions of Carbon Dioxide in the Electric Power Sector,

Reference 4

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Observation c6e86e29-d9a9-4be5-a289-4a5fc58e2cc7 · outbound

This paper cites Resilience of renewable power systems under climate risks,.

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Resilience of renewable power systems under climate risks,

Reference 5

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Observation 85829fb4-4e57-44b8-b53c-b43cb1e665ad · outbound

This paper cites Carbon-Oriented Operational Planning in Coupled Electricity and Emission Trading Markets,.

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Carbon-Oriented Operational Planning in Coupled Electricity and Emission Trading Markets,

Reference 6

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Observation 83b6b5db-b2b4-4e98-b755-da410dd07904 · outbound

This paper cites Calculating Probabilistic Carbon Emission Flow: An Adaptive Regression- Based Framework,.

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Calculating Probabilistic Carbon Emission Flow: An Adaptive Regression- Based Framework,

Reference 7

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Observation ddd19b5f-8424-4703-be7b-4b20915e1c4a · outbound

This paper cites A Market-Clearing-Based Sensitivity Model for Locational Marginal and Average Carbon Emission,.

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework A Market-Clearing-Based Sensitivity Model for Locational Marginal and Average Carbon Emission,

Reference 8

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Observation 431b0f1d-fe09-47b2-8323-abcd783e03ea · outbound

This paper cites Generation Expansion Planning Considering the Rehabilitation of Aging Generating Units,.

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Generation Expansion Planning Considering the Rehabilitation of Aging Generating Units,

Reference 9

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Observation 8e81b2ab-39d7-4823-a144-d63429ef672c · outbound

This paper cites Reliability Evaluation for Integrated Power- Gas Systems with Power-to-Gas and Gas Storages,.

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Reliability Evaluation for Integrated Power- Gas Systems with Power-to-Gas and Gas Storages,

Reference 10

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Observation 5f3cff96-fe9c-47e0-8b59-d2806cfc4821 · outbound

This paper cites Bi-Level Carbon Trading Model on Demand Side for Integrated Electricity- Gas System,.

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Bi-Level Carbon Trading Model on Demand Side for Integrated Electricity- Gas System,

Reference 11

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Observation 7d1ce1bc-23db-4f7d-8f20-8f4410e9fdbb · outbound

This paper cites Coordinated Low-Carbon Dispatching on Source-Demand Side for Integrated Electricity-Gas System Based on Integrated Demand Response Exchange,.

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Coordinated Low-Carbon Dispatching on Source-Demand Side for Integrated Electricity-Gas System Based on Integrated Demand Response Exchange,

Reference 12

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Observation d8251279-ab44-438e-a041-618747770da1 · outbound

This paper cites Carbon Emission Flow from Generation to Demand: A Network-Based Model,.

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Carbon Emission Flow from Generation to Demand: A Network-Based Model,

Reference 13

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Observation 24b4bc91-87b6-4c9a-9dd9-9a3a407977b7 · outbound

This paper cites Key Scientific Problems and Resear ch Framework for Carbon Perspective Research of New Power Systems,.

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Key Scientific Problems and Resear ch Framework for Carbon Perspective Research of New Power Systems,

Reference 14

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Observation af9c1b70-6a37-48c2-b9fa-a906f0694cf1 · outbound

This paper cites Carbon-Aware Computing for Datacenters.

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Carbon-Aware Computing for Datacenters

Reference 15

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Observation 36a78687-cb68-4481-b938-851a18812785 · outbound

This paper cites Automated Emissions Reduction (AER) methodology,.

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Automated Emissions Reduction (AER) methodology,

Reference 16

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Observation 43f6a708-db2c-4595-ba2c-43a9dfbc7a54 · outbound

This paper cites Optimal Power Scheduling Using Data-Driven Carbon Emission Flow Modelling for Carbon Intensity Control,.

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Optimal Power Scheduling Using Data-Driven Carbon Emission Flow Modelling for Carbon Intensity Control,

Reference 17

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Observation 48641dd6-854e-40ab-ba93-e6698b055250 · outbound

This paper cites Energy Forecasting: A Review and Outlook,.

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Energy Forecasting: A Review and Outlook,

Reference 18

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Observation 8d6a5c10-4979-46d8-a99f-44490238e1df · outbound

This paper cites Intelligent Wireless Tool Wear Monitoring System Based on Chucked Tool Condition Monitoring Ring and Deep Learning,.

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Intelligent Wireless Tool Wear Monitoring System Based on Chucked Tool Condition Monitoring Ring and Deep Learning,

Reference 19

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This paper cites A Novel Algorithm for Tool Wear Monitoring Utilizing Model and Knowledge-Guided Multi-Expert Weighted Adversarial Deep Transfer Learning,.

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework A Novel Algorithm for Tool Wear Monitoring Utilizing Model and Knowledge-Guided Multi-Expert Weighted Adversarial Deep Transfer Learning,

Reference 20

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This paper cites Carboncast: Multi-Day Forecasting of Grid Carbon Intensity,.

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Carboncast: Multi-Day Forecasting of Grid Carbon Intensity,

Reference 21

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Observation d85feb11-2a84-46ae-8f8b-b3696b15d1c6 · outbound

This paper cites Innovative approach to daily carbon dioxide emission forecast based on ensemble of quantile regression and attention BILSTM,.

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Innovative approach to daily carbon dioxide emission forecast based on ensemble of quantile regression and attention BILSTM,

Reference 22

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This paper cites Carbon emissions forecasting based on a new hybrid model under carbon reduction target: a case study of eastern region in China,.

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Carbon emissions forecasting based on a new hybrid model under carbon reduction target: a case study of eastern region in China,

Reference 23

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Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework GPT-4 Technical Report

Reference 24

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This paper cites LLaMA: Open and Efficient Foundation Language Models.

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework LLaMA: Open and Efficient Foundation Language Models

Reference 25

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This paper cites Large Language Model for Low- Carbon Energy Transition: Roles and Challenges,.

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Large Language Model for Low- Carbon Energy Transition: Roles and Challenges,

Reference 26

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This paper cites CarbonGPT: meta causal graph-enhanced large language models for carbon emission forecasting in large-scale power distribution networks,.

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework CarbonGPT: meta causal graph-enhanced large language models for carbon emission forecasting in large-scale power distribution networks,

Reference 27

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This paper cites Large Language Model-Based Bidding Behavior Agent and Market Sentiment Agen t-Assisted Electricity Price Prediction,.

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Large Language Model-Based Bidding Behavior Agent and Market Sentiment Agen t-Assisted Electricity Price Prediction,

Reference 28

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Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Machine Learning: Trends, Perspectives, and Prospects,

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Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework YOLOv4: Optimal Speed and Accuracy of Object Detection

Reference 30

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Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Deep learning for cybersecurity in sm art grids: review and perspectives,

Reference 31

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Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Application of deep learning image recognition for lithium battery state of health assessment,

Reference 32

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Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction

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This paper cites Distribution Network Planning Towards a Low-Carbon Transition: A Spatial-Temporal Carbon Response Method,.

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Distribution Network Planning Towards a Low-Carbon Transition: A Spatial-Temporal Carbon Response Method,

Reference 34

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Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Aggregated Model of Data Network for the Provision of Demand Response in Generation and Transmission Expansion Planning,

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Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Incentive-Compatible Demand Response for Spatially Coupled Internet Data Centers in Electricity Markets,

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Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Utility-Scale Portable Energy Storage Systems,

Reference 37

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Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Available: http://www.aemo.com.au/

Reference 38

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Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Available: http://www.bom.gov.au/

Reference 39

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Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework Flexible Integrated Network Planning Considering Echelon Utilization of Second Life of Used Electric Vehicle Batteries,

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Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework itransformer: Inverted transformers are effective for time series forecasting,

Reference 41

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