AUTHOR=Alam S. M. Mahfuz , Ali Mohd. Hasan 

TITLE=Residential load forecasting by a PSO-tuned ANFIS2 method considering the COVID-19 influence

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 11 - 2023

YEAR=2024

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2023.1292183

DOI=10.3389/fenrg.2023.1292183

ISSN=2296-598X

ABSTRACT=The most important feature of load forecasting is enabling building management system to control and manage its loads with available resources ahead of time. The electricity usage in residential buildings has been increased during COVID-19 as compared to normal time. Therefore, the performance of forecasting methods is impacted, and further tuning of parameters is required to cope up with energy consumption change due to COVID-19. This paper proposes a new adaptive neuro fuzzy-2 inference system (ANFIS2) for energy usage forecasting in residential buildings for both normal and COVID-19 periods. The particle swarm optimization (PSO) method has been implemented for parameter optimization and subtractive clustering is used for data training for the proposed ANFIS2 system. Two modifications in terms of input and parameters of the ANFIS2 system are made to cope with the change in consumption pattern and to reduce the prediction errors during the COVID-19. Simulation results obtained by the MATLAB software validate the efficacy of the proposed ANFIS2 system in residential load forecasting during both normal and COVID-19 periods. Moreover, the performance of the proposed method is better than that of the existing ANFIS, long short-term memory (LSTM), and random forest (RF) approaches.