Load Data Valuation in Multi-Energy Systems: An End-to-End Approach

TL;DR

This paper introduces an end-to-end framework for valuing multi-energy load data, integrating forecasting and decision-making to promote data sharing and reduce operational costs in multi-energy systems.

Contribution

It proposes a novel data valuation method combined with an incentive mechanism to encourage data sharing among sectors in multi-energy systems.

Findings

Significant reduction in operation costs using the proposed valuation approach

Enhanced forecasting accuracy through integrated data valuation and decision processes

Effective profit allocation strategy incentivizes data sharing among sectors

Abstract

Accurate load forecasting serves as the foundation for the flexible operation of multi-energy systems (MES). Multi-energy loads are tightly coupled and exhibit significant uncertainties. Many works focus on enhancing forecasting accuracy by leveraging cross-sector information. However, data owners may not be motivated to share their data unless it leads to substantial benefits. Ensuring a reasonable data valuation can encourage them to share their data willingly. This paper presents an end-to-end framework to quantify multi-energy load data value by integrating forecasting and decision processes. To address optimization problems with integer variables, a two-stage end-to-end model solution is proposed. Moreover, a profit allocation strategy based on contribution to cost savings is investigated to encourage data sharing in MES. The experimental results demonstrate a significant decrease in operation costs, suggesting that the proposed valuation approach more effectively extracts the inherent data value than traditional methods. According to the proposed incentive mechanism, all sectors can benefit from data sharing by improving forecasting accuracy or receiving economic compensation.