TY - GEN
T1 - Development and Analysis of a Blockchain-Based Energy Trading Marketplace Forecasts
AU - Boumaiza, Ameni
AU - Sanfilippo, Antonio
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The rise of distributed energy generation through solar panels in homes and businesses sparks the creation of fresh energy markets. This shift removes the old boundaries between energy suppliers and users, leading to the emergence of energy 'prosumers.' Blockchain technology enhances safe and affordable direct energy swaps within a decentralized setup, employing encryption and consensus checks. The research utilized a unique approach called 'Agent-Based Modeling (ABM) along with Geographic Information System (GIS)' to assess energy trading within the real estate sector. This process encompassed gathering and analyzing data about daily energy consumption to grasp market dynamics and construct a decentralized energy trading approach. The initial simulation involved five key stages: collecting, processing, predicting, analyzing, confirming, and evaluating performance. The primary actors in this model were individuals, consumers, energy providers, and producers. The outcomes from the experiments indicated that one could assess the distinct households' features by incorporating GIS data and an agent-centric model. Harnessing high-performance computing makes it possible to manage large-scale simulations involving multiple participants. Generally, this approach is anticipated to enhance the model's efficiency and offer a flexible environment for scrutinizing how energy blockchain impacts finance, technology, and society.
AB - The rise of distributed energy generation through solar panels in homes and businesses sparks the creation of fresh energy markets. This shift removes the old boundaries between energy suppliers and users, leading to the emergence of energy 'prosumers.' Blockchain technology enhances safe and affordable direct energy swaps within a decentralized setup, employing encryption and consensus checks. The research utilized a unique approach called 'Agent-Based Modeling (ABM) along with Geographic Information System (GIS)' to assess energy trading within the real estate sector. This process encompassed gathering and analyzing data about daily energy consumption to grasp market dynamics and construct a decentralized energy trading approach. The initial simulation involved five key stages: collecting, processing, predicting, analyzing, confirming, and evaluating performance. The primary actors in this model were individuals, consumers, energy providers, and producers. The outcomes from the experiments indicated that one could assess the distinct households' features by incorporating GIS data and an agent-centric model. Harnessing high-performance computing makes it possible to manage large-scale simulations involving multiple participants. Generally, this approach is anticipated to enhance the model's efficiency and offer a flexible environment for scrutinizing how energy blockchain impacts finance, technology, and society.
KW - Artificial Intelligence
KW - Blockchain Technology
KW - Energy
KW - Forecasting
KW - LASSO
KW - Sustainability
UR - http://www.scopus.com/inward/record.url?scp=85179515855&partnerID=8YFLogxK
U2 - 10.1109/IECON51785.2023.10312439
DO - 10.1109/IECON51785.2023.10312439
M3 - Conference contribution
AN - SCOPUS:85179515855
T3 - IECON Proceedings (Industrial Electronics Conference)
BT - IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society
PB - IEEE Computer Society
T2 - 49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023
Y2 - 16 October 2023 through 19 October 2023
ER -