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ZENODO
Dataset . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
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EnergyTS TimeSeries Dataset with Price

Authors: Xu, Shiqi;

EnergyTS TimeSeries Dataset with Price

Abstract

We provide an evaluation benchmark for the following scenario: Day-ahead electricity price forecasting - Prediction of electricity market prices for the next day based on datetime information. This scenario contains multiple sub-datasets. The datasets are mainly generated based on a Structural Causal Model (SCM) technical framework. Centered on the operational logic of power systems, a directed acyclic graph (DAG) is used to characterize the causal relationships among load, renewable power generation, unit status, grid constraints, and locational marginal prices. Samples are then generated based on the electricity market clearing mechanism. The evaluation script corresponding to the current dataset can be accessed at https://github.com/antgroup/Energy-EVA for assessment.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average