TY - GEN
T1 - Pelican Optimization Algorithm-based ANFIS for Bolstered Electricity Usage Prediction
AU - Oladipo, Stephen
AU - Sun, Yanxia
AU - Wang, Zenghui
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s).
PY - 2025/2/15
Y1 - 2025/2/15
N2 - Reliable electricity supply is essential for global prosperity, necessitating accurate electricity load forecasts from utilities and policymakers. Conventional prediction methods often fall short, driving a surge in the application of machine learning (ML)-based modeling tools. This paper aims to develop a hybrid model combining the Pelican Optimization Algorithm (POA) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for predicting electricity consumption in a southwestern Nigerian region. Meteorological data from the study area served as inputs, while electricity consumption was the output variable. Evaluated using five performance metrics, the POA-based ANFIS exhibited superior performance, achieving Root Mean Square Error (RMSE) of 1314.7, Mean Absolute Percentage Error (MAPE) of 11.1460, Mean Absolute Relative Error (MARE) of 0.1115, and Coefficient of Variation of Root Mean Square Error (CVRMSE) of 13.0144. The research showcases the promising capabilities of the suggested model as a dependable instrument for predicting energy consumption.
AB - Reliable electricity supply is essential for global prosperity, necessitating accurate electricity load forecasts from utilities and policymakers. Conventional prediction methods often fall short, driving a surge in the application of machine learning (ML)-based modeling tools. This paper aims to develop a hybrid model combining the Pelican Optimization Algorithm (POA) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for predicting electricity consumption in a southwestern Nigerian region. Meteorological data from the study area served as inputs, while electricity consumption was the output variable. Evaluated using five performance metrics, the POA-based ANFIS exhibited superior performance, achieving Root Mean Square Error (RMSE) of 1314.7, Mean Absolute Percentage Error (MAPE) of 11.1460, Mean Absolute Relative Error (MARE) of 0.1115, and Coefficient of Variation of Root Mean Square Error (CVRMSE) of 13.0144. The research showcases the promising capabilities of the suggested model as a dependable instrument for predicting energy consumption.
KW - electricity
KW - machine learning
KW - modelling
KW - pelican optimization algorithm
KW - prediction
UR - http://www.scopus.com/inward/record.url?scp=85219592738&partnerID=8YFLogxK
U2 - 10.1145/3709026.3709043
DO - 10.1145/3709026.3709043
M3 - Conference contribution
AN - SCOPUS:85219592738
T3 - CSAI 2024 - Proceedings of 2024 8th International Conference on Computer Science and Artificial Intelligence
SP - 537
EP - 543
BT - CSAI 2024 - Proceedings of 2024 8th International Conference on Computer Science and Artificial Intelligence
PB - Association for Computing Machinery, Inc
T2 - 8th International Conference on Computer Science and Artificial Intelligence, CSAI 2024
Y2 - 6 December 2024 through 8 December 2024
ER -