Forecasting in Blockchain-Based Local Energy Markets

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. B...

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Main Authors: KOSTMANN, Michael, HARDLE, Wolfgang Karl
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2019
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Online Access:https://ink.library.smu.edu.sg/skbi/5
https://ink.library.smu.edu.sg/context/skbi/article/1004/viewcontent/energies_12_02718_v2.pdf
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spelling sg-smu-ink.skbi-10042021-05-20T06:41:56Z Forecasting in Blockchain-Based Local Energy Markets KOSTMANN, Michael HARDLE, Wolfgang Karl 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 market. Recently proposed blockchain-based LEMs use auction designs to match future demand and supply. Thus, such blockchain-based LEMs rely on accurate short-term forecasts of individual households’ energy consumption and production. Often, such accurate forecasts are simply assumed to be given. The present research tested this assumption by first evaluating the forecast accuracy achievable with state-of-the-art energy forecasting techniques for individual households and then, assessing the effect of prediction errors on market outcomes in three different supply scenarios. The evaluation showed that, although a LASSO regression model is capable of achieving reasonably low forecasting errors, the costly settlement of prediction errors can offset and even surpass the savings brought to consumers by a blockchain-based LEM. This shows that, due to prediction errors, participation in LEMs may be uneconomical for consumers, and thus, has to be taken into consideration for pricing mechanisms in blockchain-based LEMs. 2019-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/skbi/5 info:doi/10.3390/en12142718 https://ink.library.smu.edu.sg/context/skbi/article/1004/viewcontent/energies_12_02718_v2.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Sim Kee Boon Institute for Financial Economics eng Institutional Knowledge at Singapore Management University Finance Finance and Financial Management Power and Energy
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Finance
Finance and Financial Management
Power and Energy
spellingShingle Finance
Finance and Financial Management
Power and Energy
KOSTMANN, Michael
HARDLE, Wolfgang Karl
Forecasting in Blockchain-Based Local Energy Markets
description 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 market. Recently proposed blockchain-based LEMs use auction designs to match future demand and supply. Thus, such blockchain-based LEMs rely on accurate short-term forecasts of individual households’ energy consumption and production. Often, such accurate forecasts are simply assumed to be given. The present research tested this assumption by first evaluating the forecast accuracy achievable with state-of-the-art energy forecasting techniques for individual households and then, assessing the effect of prediction errors on market outcomes in three different supply scenarios. The evaluation showed that, although a LASSO regression model is capable of achieving reasonably low forecasting errors, the costly settlement of prediction errors can offset and even surpass the savings brought to consumers by a blockchain-based LEM. This shows that, due to prediction errors, participation in LEMs may be uneconomical for consumers, and thus, has to be taken into consideration for pricing mechanisms in blockchain-based LEMs.
format text
author KOSTMANN, Michael
HARDLE, Wolfgang Karl
author_facet KOSTMANN, Michael
HARDLE, Wolfgang Karl
author_sort KOSTMANN, Michael
title Forecasting in Blockchain-Based Local Energy Markets
title_short Forecasting in Blockchain-Based Local Energy Markets
title_full Forecasting in Blockchain-Based Local Energy Markets
title_fullStr Forecasting in Blockchain-Based Local Energy Markets
title_full_unstemmed Forecasting in Blockchain-Based Local Energy Markets
title_sort forecasting in blockchain-based local energy markets
publisher Institutional Knowledge at Singapore Management University
publishDate 2019
url https://ink.library.smu.edu.sg/skbi/5
https://ink.library.smu.edu.sg/context/skbi/article/1004/viewcontent/energies_12_02718_v2.pdf
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