EVERGREEN

Joint Journal of Novel Carbon Resource Sciences and Green Asia Strategy

ISSN:2189-0420 (Print until Mar 2020)
ISSN:2432-5953 (Online)

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Enhancing Electricity Consumption Forecasting using Hybrid ANN-ANFIS Models for Smart Grid Applications

Sanjeev Kumar1, Laxmi Kant Sagar2, Geeta Tiwari3, Prateek Kumar Singhal1
1Electrical Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan,, India
2Department of Computer science engineering, Sharda University, India
3Department of Computer Science and Engineering, Poornima College of Engineering, India
Evergreen, Vol. 12, Iss. 03, pp. 1589–1597, 2025
DOI: 10.5109/7388851
Creative Commons Attribution 4.0 International Creative Commons Attribution 4.0 International
Received: January 31, 2025 | Revised: July 06, 2025 | Accepted: August 20, 2025 | Published: September 2025
Abstract
Forecasting electricity consumption is a critical task for efficient energy management and for the integration of renewables. The present work uses a combination of two state-of- art techniques, Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference System (ANFIS), to produce more accurate predictions. We develop and compare several forecasting models using four years of data from an institute which employs solar, diesel generator and hydel power. The results reveal that ANFIS does better than ANN with lower Mean Absolute Percentage Error and Mean Squared Percentage Error. The research paper showed that ensemble methods boost electricity consumption prediction outcomes, and they can be by utility companies in their smart grid applications. This research is an effort to make a step forward in data-driven forecasting to meet the growing demand for energy in a sustainable manner and by applying machine learning methods which should maintain data forecasting as needed in planning efforts.
Keywords
ANFIS ; Artificial Neural Networks ; Renewable Energy Integration ; Electricity Forecasting ; Smart Grid Prediction ; Machine Learning for Energy Management
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