Forecasting in Blockchain-based Local Energy Markets

Arbeitspapier

Increasingly volatile and distributed energy production challenge 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 tests this assumption. First, by evaluating the forecast accuracy achievable with state-of-the-art energy forecasting techniques for individual households and, second, by assessing the effect of prediction errors on market outcomes in three different supply scenarios. The evaluation shows 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.

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• Language: Englisch

References and Relationships

• Bibliographic citation: Series: IRTG 1792 Discussion Paper ; No. 2019-014

Classification and Topics

• Classification: Wirtschaft ; Energy Forecasting ; Auctions ; Market Design ; Forecasting Models; Simulation Methods
• Subject: Blockchain ; Local Energy Market ; Smart Contract ; Machine Learning ; Household ; Energy Prediction ; Prediction Errors ; Market Mechanism

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Kostmann, Michael ; Härdle, Wolfgang Karl
• Event: Veröffentlichung
• (who): Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
• (where): Berlin
• (when): 2019

Further information

• Handle: http://hdl.handle.net/10419/230790