Short-Term Load Forecasting Using a Parallel CNN-BPNN Prediction Model with COVID-19 Pandemic Restriction as an Added Input Parameter and ReLU Activation Function
2022 12th International Workshop on Computer Science and Engineering, WCSE 2022
; : 152-158, 2022.
Article
Dans Anglais
| Scopus | ID: covidwho-2025937
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.
activation function; backpropagation neural network; convolutional neural network; covid-19 pandemic; short-term load forecasting; Correlation methods; COVID-19; Electric power plant loads; Electric power utilization; Forecasting; Machine learning; Meteorology; Wind speed; Activation functions; Back-propagation neural networks; Input parameter; Load forecasting; Machine learning algorithms; Power; Prediction modelling; Short term load forecasting; Chemical activation
Texte intégral:
Disponible
Collection:
Bases de données des oragnisations internationales
Base de données:
Scopus
Type d'étude:
Étude pronostique
langue:
Anglais
Revue:
2022 12th International Workshop on Computer Science and Engineering, WCSE 2022
Année:
2022
Type de document:
Article
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