Transformer-based few-shot learning for modeling Electricity Consumption Profiles with minimal data across thousands of domains
arXiv:2408.08399v3 Announce Type: replace Abstract: Electricity Consumption Profiles (ECPs) are crucial for operating and planning power distribution systems, especially with the increasing number of low-carbon technologies such as solar panels and electric vehicles. Traditional ECP modeling methods typically assume the availability of sufficient ECP data. However, in practice, the accessibility of ECP data is limited due to privacy issues or the absence of metering devices. Few-shot learning (FSL) has emerged as a promising solution for ECP modeling in data-scarce scenarios. Nevertheless, sta
The proliferation of distributed low-carbon technologies and the increasing demand for grid stability are driving the need for more granular and data-efficient ECP modeling methods, which few-shot learning addresses.
Accurate, data-minimal ECP modeling is critical for managing modern power grids, especially with the growth of renewables and EVs, and this research offers a scalable solution for thousands of domains.
Traditional ECP modeling, which relies on extensive data, will be augmented or replaced by few-shot learning approaches, enabling better grid management in data-scarce environments and improved energy efficiency.
- · Smart grid operators
- · Renewable energy companies
- · Utilities managing distributed energy resources
- · AI/ML providers for energy sector
- · Companies relying on traditional, data-intensive ECP modeling
- · Energy efficiency initiatives without advanced analytical tools
More efficient and reliable management of electricity grids, particularly in areas with high renewable penetration or limited metering data.
Accelerated adoption of low-carbon technologies like solar panels and electric vehicles due to improved grid integration and stability.
Reduced energy waste and more resilient power distribution systems, contributing to broader energy security and sustainability goals globally.
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