Hybrid Modified Particle Swarm Optimization and Adaptive Neuro‐Fuzzy Inference System for Electric Power Consumption Prediction

Load forecasting is crucial in power system energy management, aiding planning and operations. The latest modified forecasting methods are important for best prediction of the electric power consumption of the load which nowadays are not practically implemented. As a result, hybrid modified particle...

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Bibliographic Details
Published in:International Journal of Photoenergy Vol. 2025; no. 1
Main Authors: Mossie, Molla Addisu, Sharew, Estifanos Abeje, Workineh, Tewodros Gera, Alemu, Yechale Amogne
Format: Journal Article
Language:English
Published: New York John Wiley & Sons, Inc 01.01.2025
Wiley
Subjects:
Accuracy
Adaptive systems
Algorithms
Business metrics
Case studies
Clustering
Deep learning
Electric power
Electric power systems
Electricity
Energy consumption
Energy management
Energy management systems
Energy use
Ethiopia
Forecasting
Forecasting techniques
Fuzzy logic
Fuzzy sets
Mathematical optimization
Mean square errors
Neural networks
Optimization techniques
Particle swarm optimization
Performance evaluation
Smart grid technology
Time series
Ethiopia
ISSN:1110-662X, 1687-529X
Online Access:Get full text
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Summary:Load forecasting is crucial in power system energy management, aiding planning and operations. The latest modified forecasting methods are important for best prediction of the electric power consumption of the load which nowadays are not practically implemented. As a result, hybrid modified particle swarm optimization (MPSO)–adaptive neuro‐fuzzy inference system (ANFIS) is applied in this work to predict the electric power consumption of the case study area. This research also focuses on using artificial neural network (ANN), ANFIS, and MPSO‐ANFIS to model Hossana City’s power consumption in Ethiopia, forecasting beyond available data for the next decade. Addressing the challenges of complex and widely distributed power systems, the study incorporates demographic factors. The performance evaluations show MPSO‐ANFIS found as the superior model, achieving a mean absolute percentage error (MAPE) of 1.2% and root mean square error (RMSE) of 0.87, due to MPSO. This model also boosts a high R 2 value of 0.97. The model demonstrates effective load forecasting based on previous data training, promising reliable future predictions. So, the hybrid MPSO‐ANFIS is recommended for electric power consumption prediction.
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ISSN:1110-662X
1687-529X
DOI:10.1155/ijph/4715427