Can cross-sector information improve multi-energy demand forecasting accuracy?

Zhou, Yangze; Cui, Xueyuan

doi:10.1016/j.egyr.2022.11.194

Cited by 5 publications

(1 citation statement)

References 29 publications

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“…M ULTI-energy systems (MES) are considered as a promising approach for enhancing energy efficiency and promoting renewable energy accommodation [1]. MES coordinates different energy sectors, such as power, cooling, heat, and gas, to satisfy multi-energy loads with high overall energy efficiency [2]. Multi-energy load forecasting is the basis for the operations of various energy converters within the MES.…”

Section: Introductionmentioning

confidence: 99%

End-to-end Distributed Video Coding

Zhou

2022

2022 Data Compression Conference (DCC)

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Accurate load forecasting serves as the foundation for the flexible operation of multi-energy systems (MES). Multienergy 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 endto-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 endto-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.

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Section: Introductionmentioning

confidence: 99%