ABSTRACT
Short-term load forecasting provides a vital tool for the power system. This study delved into applying a hybridized machine learning algorithm to improve load forecasting accuracy. It aims to investigate the accuracy of the parallel CNN-BPNN prediction model in short-term load forecasting with Philippine pandemic restriction as an added parameter and a ReLU activation function. The CNN, BPNN, and the proposed parallel CNN-BPNN models were implemented using Python. They were trained, validated, and tested using the input parameters such as historical power demand, day of weeks/ Holidays, meteorological data such as temperature, wind speed, humidity, and COVID-19 pandemic restriction. The accuracy of the three models was tested using the MAPE. Results showed that the proposed model achieved the lowest MAPE of 3.52 %, lower than that of the CNN, 4.62%, and BPNN, 3.98%. Furthermore, Pearson correlation analysis showed that the relationship between electricity usage and mobility constraints is moderately correlated with a correlation value of -0.57. © 2022 WCSE. All Rights Reserved.
ABSTRACT
With the world facing a public health emergency due to the Coronavirus disease (COVID-19) in a global pandemic, this paper provides insight about how a simulation model was used to determine the impact of headcount variability during lockdown on fab performance. To create a robust simulation model, operator loading time was introduced as one of the input parameters. An existing and well validated Discrete Event Fab simulation model was extended with operator modelling, and was used to conduct case studies, evaluating the impact of different operator availability scenarios including work disruptions for several shifts within a week. The studies provide implications for operation to derive mitigation strategies, weighing the trade-off between cost demand and speed loss due to operator resources. © 2021 IEEE.