RT Journal Article
T1 Modelling Electricity Consumption During the COVID19 Pandemic: 												Datasets, Models, Results and a Research Agenda
A1 Khan, Zulfiqar Ahmad
A1 Hussain, Tanveer
A1 Ullah, Amin
A1 Ullah, Waseem
A1 Del Ser, Javier
A1 Muhammad, Khan
A1 Sajjad, Muhammad
A1 Baik, Sung Wook
AB The COVID19 pandemic has impacted the global economy, social activities, and Electricity Consumption (EC), affecting the performance of historical data-based Electricity Load Forecasting (ELF) algorithms. This study thoroughly analyses the pandemic's impact on these models and develop a hybrid model with better prediction accuracy using COVID19 data. Existing datasets are reviewed, and their limited generalization potential for the COVID19 period is highlighted. A dataset of 96 residential customers, comprising 36 and six months before and after the pandemic, is collected, posing significant challenges for current models. The proposed model employs convolutional layers for feature extraction, gated recurrent nets for temporal feature learning, and a self-attention module for feature selection, leading to better generalization for predicting EC patterns. Our proposed model outperforms existing models, as demonstrated by a detailed ablation study using our dataset. For instance, it achieves an average reduction of 0.56% & 3.46% in MSE, 1.5% & 5.07% in RMSE, and 11.81% & 13.19% in MAPE over the pre- and post-pandemic data, respectively. However, further research is required to address the varied nature of the data. These findings have significant implications for improving ELF algorithms during pandemics and other significant events that disrupt historical data patterns.
SN 0378-7788
YR 2023
FD 2023-09-01
LK https://hdl.handle.net/11556/5082
UL https://hdl.handle.net/11556/5082
LA eng
NO Khan , Z A , Hussain , T , Ullah , A , Ullah , W , Del Ser , J , Muhammad , K , Sajjad , M & Baik , S W 2023 , ' Modelling Electricity Consumption During the COVID19 Pandemic : Datasets, Models, Results and a Research Agenda ' , Energy and Buildings , vol. 294 , 113204 . https://doi.org/10.1016/j.enbuild.2023.113204
NO Publisher Copyright: © 2023 Elsevier B.V.
NO This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2019M3F2A1073179).
DS TECNALIA Publications
RD 28 sept 2024