Experimental Investigation of Variational Mode Decomposition and Deep Learning for Short-Term Multi-horizon Residential Electric Load Forecasting

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00359655" target="_blank" >RIV/68407700:21230/22:00359655 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.apenergy.2022.119963" target="_blank" >https://doi.org/10.1016/j.apenergy.2022.119963</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.apenergy.2022.119963" target="_blank" >10.1016/j.apenergy.2022.119963</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Experimental Investigation of Variational Mode Decomposition and Deep Learning for Short-Term Multi-horizon Residential Electric Load Forecasting

Popis výsledku v původním jazyce

With the booming growth of advanced digital technologies, it has become possible for users as well as distributors of energy to obtain detailed and timely information about the electricity consumption of households. These technologies can also be used to forecast the household’s electricity consumption (a.k.a. the load). In this paper, Variational Mode Decomposition and deep learning techniques are investigated as a way to improve the accuracy of the load forecasting problem. Although this problem has been studied in the literature, selecting an appropriate decomposition level and a deep learning technique providing better forecasting performance have garnered comparatively less attention. This study bridges this gap by studying the effect of six decomposition levels and five distinct deep learning networks. The raw load profiles are first decomposed into intrinsic mode functions using the Variational Mode Decomposition in order to mitigate their non-stationary aspect. Then, day, hour, and past electricity consumption data are fed as a three-dimensional input sequence to a four-level Wavelet Decomposition Network model. Finally, the forecast sequences related to the different intrinsic mode functions are combined to form the aggregate forecast sequence. The proposed method was assessed using load profiles of five Moroccan households from the Moroccan buildings’ electricity consumption dataset (MORED) and was benchmarked against state-of-the-art time-series models and a baseline persistence model.

Název v anglickém jazyce

Experimental Investigation of Variational Mode Decomposition and Deep Learning for Short-Term Multi-horizon Residential Electric Load Forecasting

Popis výsledku anglicky

With the booming growth of advanced digital technologies, it has become possible for users as well as distributors of energy to obtain detailed and timely information about the electricity consumption of households. These technologies can also be used to forecast the household’s electricity consumption (a.k.a. the load). In this paper, Variational Mode Decomposition and deep learning techniques are investigated as a way to improve the accuracy of the load forecasting problem. Although this problem has been studied in the literature, selecting an appropriate decomposition level and a deep learning technique providing better forecasting performance have garnered comparatively less attention. This study bridges this gap by studying the effect of six decomposition levels and five distinct deep learning networks. The raw load profiles are first decomposed into intrinsic mode functions using the Variational Mode Decomposition in order to mitigate their non-stationary aspect. Then, day, hour, and past electricity consumption data are fed as a three-dimensional input sequence to a four-level Wavelet Decomposition Network model. Finally, the forecast sequences related to the different intrinsic mode functions are combined to form the aggregate forecast sequence. The proposed method was assessed using load profiles of five Moroccan households from the Moroccan buildings’ electricity consumption dataset (MORED) and was benchmarked against state-of-the-art time-series models and a baseline persistence model.

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

—

Návaznosti

R - Projekt Ramcoveho programu EK

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Applied Energy

ISSN

0306-2619

e-ISSN

1872-9118

Svazek periodika

326

Číslo periodika v rámci svazku

November

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

15

Strana od-do

—

Kód UT WoS článku

000862876900011

EID výsledku v databázi Scopus

2-s2.0-85138343844