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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/GF21-33574K" target="_blank" >GF21-33574K: Celoživotní strojové učení z datových proudů</a><br>

Návaznosti

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

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Applied Soft Computing

ISSN

1568-4946

e-ISSN

1872-9681

Svazek periodika

150

Číslo periodika v rámci svazku

January 2024

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

26

Strana od-do

nestránkováno

Kód UT WoS článku

001128397500001

EID výsledku v databázi Scopus

2-s2.0-85178663383