  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.
