Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2019 014

Forecasting in Blockchain-based Local Energy Markets

Michael Kostmann
Wolfgang K. Härdle

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.

Blockchain; Local Energy Market; Smart Contract; Machine Learning; Household;
Energy Prediction; Prediction Errors; Market Mechanism

JEL Classification:
Q47; D44; D47; C53