2019
DOI: 10.3390/en12142718
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Abstract: Increasingly volatile and distributed energy production challenges traditional mechanisms to manage grid loads and price energy. Local energy markets (LEMs) may be a response to those challenges as they can balance energy production and consumption locally and may lower energy costs for consumers. Blockchain-based LEMs provide a decentralized market to local energy consumer and prosumers. They implement a market mechanism in the form of a smart contract without the need for a central authority coordinating the… Show more

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Cited by 13 publications
(3 citation statements)
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“…Therefore, the pricing mechanism includes auction, game, market models are usually introduced along with forecasting. The work conducted by Kostmann and Härdle 56 accurates short‐term forecasts of individual households' energy consumption and production use auction designs to match future demand and supply. Considering the uncertainty of future events, including the charging profiles of EVs arriving and future load demand in the grid, new models are developed based on energy blockchain to simulate the habits of EVs to deliver renewable energy to various regions with different power loads 57‐59 .…”
Section: Applications Of Energy Blockchainmentioning
confidence: 99%
“…Therefore, the pricing mechanism includes auction, game, market models are usually introduced along with forecasting. The work conducted by Kostmann and Härdle 56 accurates short‐term forecasts of individual households' energy consumption and production use auction designs to match future demand and supply. Considering the uncertainty of future events, including the charging profiles of EVs arriving and future load demand in the grid, new models are developed based on energy blockchain to simulate the habits of EVs to deliver renewable energy to various regions with different power loads 57‐59 .…”
Section: Applications Of Energy Blockchainmentioning
confidence: 99%
“…Pinto et al [177] use conditional kernel density estimation to generate load forecasts which feed into an optimisation that provides feasible flexibility operating trajectories that determine the storage, flexible appliances or consumer preferences. Finally, a LASSO based model for household forecasts is used in [131] to feed blockchain designed local energy markets which consider an auction process to match supply and demand.…”
Section: Flexibility Applicationsmentioning
confidence: 99%
“…Wei et al [23] combined a neural network and statistical linear model to predict wind power output. Michael et al [24] incorporated the forecasting model based on machine learning to predict the household energy consumption. Zhang et al [25] proposed a forecasting model for building demand response with random forest and ensemble learning method.…”
Section: Introductionmentioning
confidence: 99%