• Radhika M. Bhandari

Energy consumption monitoring has become essential for improving energy efficiency and identifying usage patterns in both residential and industrial sectors. With the increasing availability of printed electricity bills and the absence of standardized digital formats, automated data extraction has become a major challenge. Recent advancements in Optical Character Recognition (OCR), Machine Learning (ML), and Deep Learning (DL) have enabled efficient digitization, pattern analysis, and forecasting of energy consumption trends using image-based datasets. This review paper presents a comprehensive study of OCR techniques, document preprocessing methods, clustering algorithms, and forecasting models such as Random Forest, ARIMA, LSTM, and hybrid architectures. It also highlights major research contributions in electricity bill image processing, comparative consumption analysis, and consumer segmentation. The paper identifies existing challenges such as bill format variability, noise, inconsistent image quality, and the lack of unified long-term datasets. The review concludes that integrating OCR with ML and DL models offers a robust solution for automated energy pattern analysis and provides significant potential for future smart grid applications, real-time monitoring, and data-driven energy management systems.

OCR, Machine Learning, Deep Learning, Electricity Bills, Time-Series Forecasting, LSTM, ARIMA, Random Forest, Image Processing, Consumer Segmentation, K-Means Clustering, Document Digitization, Residential Load, Industrial Energy Pattern, Computer Vision.

" A Review on Machine Learning-Based Energy Consumption Pattern Analysis Using Image-Based Electricity Bills", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 11, page no.f44-f48, November-2025, Available :http://www.jetir.org/papers/JETIR2511527.pdf

" A Review on Machine Learning-Based Energy Consumption Pattern Analysis Using Image-Based Electricity Bills", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 11, page no. ppf44-f48, November-2025, Available at : http://www.jetir.org/papers/JETIR2511527.pdf