International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Sustainable Energy Solutions: Predicting Residential Electricity Consumption Using Deep Learning Techniques |
|---|---|
| ABSTRACT | Residential electricity consumption has significantly increased due to global population growth, urbanization, and the widespread use of household appliances, creating challenges in maintaining stable power supplies. This project presents a deep learning-based approach for accurately predicting residential electricity consumption, addressing these challenges by leveraging advanced techniques such as Convolutional Neural Networks (CNN), Bidirectional Gated Recurrent Units (BiGRU), and Self-Attention (SA). The model is trained on a comprehensive dataset from the UCI Machine Learning Repository, ensuring robust generalization and reliability. To enhance accuracy, data preprocessing steps like normalization technique are applied, preserving temporal dynamics and improving model efficiency. CNN layers extract crucial spatial patterns from electricity consumption data, while BiGRU layers capture long-term dependencies in both forward and backward directions, and the Self-Attention mechanism refines feature selection by emphasizing key temporal correlations. The model is rigorously evaluated using real-world electricity consumption datasets, demonstrating superior predictive performance over traditional statistical and machine learning models. By enabling precise and reliable forecasting, this approach aids in optimizing energy distribution, reducing power outages, enhancing grid stability, and contributing to sustainable energy management. |
| AUTHOR | DR. T.V.S SRIRAM, P. ESWAR, M. ANISHA Sr. Asst. Professor, Dept. of MCA, NSRIT, Visakhapatnam, AP, India Asst. Professor, Dept. of MCA, NSRIT, Visakhapatnam, AP, India Dept. of MCA, NSRIT, Visakhapatnam, AP, India |
| VOLUME | 186 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1407021 |
| pdf/21_Sustainable Energy Solutions Predicting Residential Electricity Consumption Using Deep Learning Techniques.pdf | |
| KEYWORDS | |
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