Smart Energy Forecasting: An In-depth Study on Forecasting Methods for Electric-thermal Storage Systems (preprint)
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This is the author's pre-print. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
The final, peer-reviewed publication is accessible at: https://doi.org/10.1109/SmartGridComm60555.2024.10738097.
Smart grid developments have gained significant
attention due to their potential to optimise energy consumption
and reduce environmental impacts. For this reason, it is crucial
to forecast future state conditions such as power, temperatures,
heat, or SOC (state-of-charge) to make the most accurate and
suitable control decision depending on the context and need.
Since many processes are hard to model, the forecasting task can
be executed by exploiting the advantages of machine learning
algorithms such as LSTM, transformer, Autoformer, or CNN.
Comparing the results to previous works, we can state that our
best model also outperforms state-of-the-art and state-research
forecasting methods for continuous variables like temperatures.
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IEEE_SmartGridComm_2024-FINAL-1_PREPRINT_with_DOI.pdf
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Funding
Dates
- Accepted
-
2024-08-05The paper 1571039992 ("Smart Energy Forecasting: An In-depth Study on Forecasting Methods for Electric-thermal Storage Systems") submitted to the 2024 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) - Workshop on Data-driven Methods for Distribution Grid Monitoring, Operation and Planning has been accepted