Low-carbon power demand forecasting models for the performance optimization of new energy robotics systems

HuiMing Zhang and CuiFang Zhang

International Journal of Low-Carbon Technologies, 2025, vol. 20, 341-352

Abstract: To improve the performance of new energy-powered robots, a method for optimizing the performance of new energy-powered robots has been proposed, based on a low-carbon power demand forecasting model. The approach advocated leveraging low-carbon power demand to optimize power system design and control strategies. Then, a model for forecasting robotic power demands was established, alongside the refinement of the power system evaluation mechanism. Results indicated a significant correlation between operational parameters linked to low-carbon power demand and system performance. The precision of our model was notably high, enabling the provision of specific performance optimization strategies tailored to diverse low-carbon contexts.

Keywords: robotics; low-carbon power; new energy; demand forecasting; performance optimization; demand forecasting (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1093/ijlct/ctaf006 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:ijlctc:v:20:y:2025:i::p:341-352.

Access Statistics for this article

International Journal of Low-Carbon Technologies is currently edited by Saffa B. Riffat

More articles in International Journal of Low-Carbon Technologies from Oxford University Press
Bibliographic data for series maintained by Oxford University Press ().