The P2P-Ieee 14 Bus System Data Set

Sousa, Tiago; Soares, Tiago; Moret, Fabio; Baroche, Thomas; Sorin, Etienne

doi:10.5281/zenodo.1220935

Cited by 2 publications

(2 citation statements)

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“…Using a data set of 70,084 data points, this study created a P2P trading system based on LSTM-based price forecasting. Market pricing, maximum power, minimum power, and renewable generation statistics were all included in the data collection (Sousa et al, 2018). The system was tested in three communities: producers and consumers, as well as renewable energy resources.…”

Section: Simulations and Resultsmentioning

confidence: 99%

DeepResTrade: a peer-to-peer LSTM-decision tree-based price prediction and blockchain-enhanced trading system for renewable energy decentralized markets

Safari,

Gharehbagh,

Nazari-Heris

et al. 2023

Front. Energy Res.

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Intelligent predictive models are fundamental in peer-to-peer (P2P) energy trading as they properly estimate supply and demand variations and optimize energy distribution, and the other featured values, for participants in decentralized energy marketplaces. Consequently, DeepResTrade is a research work that presents an advanced model for predicting prices in a given traditional energy market. This model includes numerous fundamental components, including the concept of P2P trading systems, long-term and short-term memory (LSTM) networks, decision trees (DT), and Blockchain. DeepResTrade utilized a dataset with 70,084 data points, which included maximum/minimum capacities, as well as renewable generation, and price utilized of the communities. The developed model obtains a significant predictive performance of 0.000636% Mean Absolute Percentage Error (MAPE) and 0.000975% Root Mean Square Percentage Error (RMSPE). DeepResTrade’s performance is demonstrated by its RMSE of 0.016079 and MAE of 0.009125, indicating its capacity to reduce the difference between anticipated and actual prices. The model performs admirably in describing actual price variations in, as shown by a considerable R2 score of 0.999998. Furthermore, F1/recall scores of [1, 1, 1] with a precision of 1, all imply its accuracy.

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Section: Simulations and Resultsmentioning

confidence: 99%

DeepResTrade: a peer-to-peer LSTM-decision tree-based price prediction and blockchain-enhanced trading system for renewable energy decentralized markets

Safari,

Gharehbagh,

Nazari-Heris

et al. 2023

Front. Energy Res.

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“…On the other hand, the export price is equal to the market price minus the grid tariff. Readers interested in using the data presented in the test case for further research are directed to [85].…”

Section: Reference Test Case For P2p Marketsmentioning

confidence: 99%

Peer-to-peer and community-based markets: A comprehensive review

Sousa

Soares

Pinson

et al. 2019

Renewable and Sustainable Energy Reviews

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732

348

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The advent of more proactive consumers, the so-called "prosumers", with production and storage capabilities, is empowering the consumers and bringing new opportunities and challenges to the operation of power systems in a market environment. Recently, a novel proposal for the design and operation of electricity markets has emerged: these so-called peer-to-peer (P2P) electricity markets conceptually allow the prosumers to directly share their electrical energy and investment. Such P2P markets rely on a consumercentric and bottom-up perspective by giving the opportunity to consumers to freely choose the way they are to source their electric energy. A community can also be formed by prosumers who want to collaborate, or in terms of operational energy management. This paper contributes with an overview of these new P2P markets that starts with the motivation, challenges, market designs moving to the potential future developments in this field, providing recommendations while considering a test-case.

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The P2P-Ieee 14 Bus System Data Set

Cited by 2 publications

References 0 publications

DeepResTrade: a peer-to-peer LSTM-decision tree-based price prediction and blockchain-enhanced trading system for renewable energy decentralized markets

DeepResTrade: a peer-to-peer LSTM-decision tree-based price prediction and blockchain-enhanced trading system for renewable energy decentralized markets

Peer-to-peer and community-based markets: A comprehensive review

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