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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM67642
Volume:
10 |
10 |
Oct 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Computer Science and Engineering
P. R. Pote Patil College of Engineering and Management, Amravati
Computer Science and Engineering
P. R. Pote Patil College of Engineering and Management, Amravati
Computer Science and Engineering
P. R. Pote Patil College of Engineering and Management, Amravati
Computer Science and Engineering
P. R. Pote Patil College of Engineering and Management, Amravati
Computer Science and Engineering
P. R. Pote Patil College of Engineering and Management, Amravati
Computer Science and Engineering
P. R. Pote Patil College of Engineering and Management, Amravati
The increasing consumption of electrical energy at the household level has created a need for efficient monitoring and analytical systems capable of identifying consumption pat-terns and supporting energy management. Conventional electric-ity monitoring approaches often provide limited analytical capa-bilities and primarily present periodic consumption summaries. This paper proposes a cloud-based Household Electricity Con-sumption Analytics System using Google Cloud Platform (GCP) services. The proposed architecture integrates Google Cloud Storage for scalable data storage, Google Cloud Functions for automated data processing, Google BigQuery for analytical pro-cessing, and Looker Studio for interactive visualization. House-hold electricity consumption data in CSV format is uploaded to cloud storage and processed through an Extract, Transform, and Load (ETL) pipeline. The data is validated, cleaned, transformed, and subsequently loaded into BigQuery for SQL-based analysis. The system focuses on identifying consumption trends, peak usage periods, monthly consumption, and estimated electricity costs. The analytical results are presented through interactive dashboards to improve the understanding of electricity usage. The proposed approach provides a scalable and cloud-oriented framework for household electricity analytics and can serve as a foundation for future extensions involving forecasting, anomaly detection, and real-time monitoring.
KEYWORDS
This paper presented a cloud-based Household Electricity Consumption Analytics System using Google Cloud Platform services. The proposed architecture integrates Google Cloud Storage, Google Cloud Functions, Google BigQuery, and Looker Studio to provide an end-to-end pipeline for storing, processing, analyzing, and visualizing household electricity consumption data. The system performs data validation, cleaning, transforma-tion, and analytical processing before presenting the results through interactive dashboards. The proposed approach pro-vides a structured and scalable framework for understanding household electricity consumption patterns. The system can be further enhanced through machine learning-based forecasting, anomaly detection, and real-time monitoring. These extensions can provide additional analytical capabilities for supporting efficient and sustainable energy management.
[1] Google Cloud, “BigQuery Documentation,” [Online]. Available: https://cloud.google.com/bigquery/docs [2] Google Cloud, “Cloud Storage Documentation,” [Online]. Available: https://cloud.google.com/storage/docs [3] Google Cloud, “Cloud Functions Documentation,” [Online]. Available: https://cloud.google.com/functions/docs [4] Google Cloud, “Looker Studio Documentation,” [Online]. Available: https://cloud.google.com/looker/docs [5] Google Cloud, “Google Cloud Architecture Framework,” [Online]. Available: https://cloud.google.com/architecture/framework [6] A. Albert and R. Rajagopal, “Smart Meter Driven Segmentation: What Your Consumption Says About You,” IEEE Transactions on Power Systems, vol. 28, no. 4, pp. 4019–4030, Nov. 2013. [7] Y. Wang, Q. Chen, T. Hong, and C. Kang, “Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges,” IEEE Transactions on Smart Grid, vol. 10, no. 3, pp. 3125–3148, May 2019. [8] G. Strbac, “Demand Side Management: Benefits and Challenges,” En-ergy Policy, vol. 36, no. 12, pp. 4419–4426, Dec. 2008. [9] A. Botta, W. De Donato, V. Persico, and A. Pescape`, “Integration of Cloud Computing and Internet of Things: A Survey,” Future Generation Computer Systems, vol. 56, pp. 684–700, Mar. 2016. [10] P. Mell and T. Grance, “The NIST Definition of Cloud Computing,” NIST Special Publication 800-145, 2011.
Publication Date
10/3/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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