Impact of Forecasting Models Errors in a Peer-to-Peer Energy Sharing Market

Luis Gomes, Hugo Morais, Calvin Gonçalves, Eduardo Gomes, Lucas Pereira and Zita Vale
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Luis Gomes: GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto, 4200-072 Porto, Portugal
Hugo Morais: INESC-ID—Instituto de Engenharia de Sistemas e Computadores-Investigação e Desenvolvimento, Department of Electrical and Computer Engineering, Instituto Superior Técnico—IST, Universidade de Lisboa, 1049-001 Lisboa, Portugal
Calvin Gonçalves: GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto, 4200-072 Porto, Portugal
Eduardo Gomes: INESC-ID—Instituto de Engenharia de Sistemas e Computadores-Investigação e Desenvolvimento, Department of Electrical and Computer Engineering, Instituto Superior Técnico—IST, Universidade de Lisboa, 1049-001 Lisboa, Portugal
Lucas Pereira: ITI/LARSyS—Interactive Technologies Institute/Laboratory of Robotics and Engineering Systems, Instituto Superior Técnico—IST, Universidade de Lisboa, 1049-001 Lisboa, Portugal
Zita Vale: GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto, 4200-072 Porto, Portugal

Energies, 2022, vol. 15, issue 10, 1-18

Abstract: The use of energy sharing models in smart grids has been widely addressed in the literature. However, feasible technical solutions that can deploy these models into reality, as well as the correct use of energy forecasts are not properly addressed. This paper proposes a simple, yet viable and feasible, solution to deploy energy management systems on the end-user-side in order to enable not only energy forecasting but also a distributed discriminatory-price auction peer-to-peer energy transaction market. This work also analyses the impact of four energy forecasting models on energy transactions: a mathematical model, a support-vector machine model, an eXtreme Gradient Boosting model, and a TabNet model. To test the proposed solution and models, the system was deployed in five small offices and three residential households, achieving a maximum of energy costs reduction of 10.89% within the community, ranging from 0.24% to 57.43% for each individual agent. The results demonstrated the potential of peer-to-peer energy transactions to promote energy cost reductions and enable the validation of auction-based energy transactions and the use of energy forecasting models in today’s buildings and end-users.

Keywords: energy auctions; energy forecast; energy management systems; energy sharing; peer-to-peer energy transactions (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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