Data-driven multi-step energy consumption forecasting with complex seasonality patterns and exogenous variables: Model accuracy assessment in change point neighborhoods

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10254657" target="_blank" >RIV/61989100:27240/24:10254657 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S1568494623011171?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1568494623011171?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.asoc.2023.111099" target="_blank" >10.1016/j.asoc.2023.111099</a>

Result language

angličtina

Original language name

Data-driven multi-step energy consumption forecasting with complex seasonality patterns and exogenous variables: Model accuracy assessment in change point neighborhoods

Original language description

Energy forecasting tools became a significant scope of application for time series modeling due to the specific challenges in energy trading - the forecast of consumption for the whole next trading day based on the limited data availability at the forecast origin. The research article addresses the scope of high-frequency time series data, multiple seasonal patterns, exogenous variables, and nonstationary properties in a multi-step forecast horizon tasks. The contribution of the research is the introduction of a machine- and deep-learning-based data-driven approach for multi-output time series forecasting and mainly an introduction of the new evaluation metric called the Change Point Neighborhood Error (CPNE). The purpose of the metric is to provide a distinctive measure of forecasting accuracy of the proposed models in parts of the time series where a change point or a data drift emerges. The experimental findings indicate a notable improvement in accuracy achieved by machine and deep learning models, resulting in a substantial reduction of the mean absolute percentage error (MAPE) by approximately 45 % compared to the optimal statistical model across both datasets used, and also in terms of change point neighborhood error in comparison to statistical models due to their requirement for stationary data input. Deep learning models may be a viable alternative to machine learning approaches; however, deep learning models require long input sequences for accurate forecasting, whereas machine learning methods require shorter input sequences and can benefit more from feature engineering. (C) 2023 Elsevier B.V.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GF21-33574K" target="_blank" >GF21-33574K: Lifelong Machine Learning on Data Streams</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Applied Soft Computing

ISSN

1568-4946

e-ISSN

1872-9681

Volume of the periodical

150

Issue of the periodical within the volume

January 2024

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

26

Pages from-to

nestránkováno

UT code for WoS article

001128397500001

EID of the result in the Scopus database

2-s2.0-85178663383