Construction of A Big Data-Driven Risk Identification, Assessment, and Precise Control System for Electricity Bill Accounting
Main Article Content
Abstract
Ensuring the security of electricity revenue and improving operational efficiency are critical issues in modern power systems, particularly under increasingly complex electromagnetic energy transmission environments and data-intensive grid operations. To address the lagging response and passive management characteristics of traditional electricity bill accounting risk control, this study proposes a big data-driven “identification–evaluation–control” framework for electricity bill accounting. By integrating multi-source operational data, machine learning techniques, and process reengineering strategies, the proposed framework transforms conventional accounting management into a proactive and high-precision risk control system. The study clarifies the core architecture, technical support mechanisms, and implementation pathway of the system, emphasizing data fusion, risk identification, quantitative assessment, precise intervention, and dynamic optimization. Furthermore, the framework enables accurate prediction, scientific evaluation, and efficient control of electricity bill accounting risks through continuous data-driven decision-making. The proposed approach provides a practical solution for power enterprises facing increasingly diversified settlement mechanisms and operational uncertainties, while offering methodological references for intelligent risk management and reliable operation in data-centric power and energy systems.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
M. Mazzucco and D. Dyachuk, “Balancing electricity bill and performance in server farms with setup costs,” Future Generation Computer Systems, vol. 28, no. 2, pp. 415-426, 2011, doi: 10.1016/j.future.2011.04.015.
V. Reddy K S S and M. Kumari S, “Prediction-Based Optimal Sizing of Battery Energy Storage Systems in PV Integrated Microgrids for Electricity Bill Minimization,” Journal of The Institution of Engineers (India): Series B, vol. 103, no. 5, pp. 1733-1745, 2022, doi: 10.1007/s40031-022-00778-8.
Y. Liu, H. Hou, K. Zhou, et al., “Research on Construction of Management and Control System of Electricity Bill Risks in Power Grid Enterprises under Energy Conservation and Emission Reduction Principle,” International Journal of Business and Management, vol. 6, no. 8, pp. 287, 2011, doi: 10.5539/ijbm.v6n8p287.
M. S. J., H. R. E. H. B., Huadong M., et al., “Optimization of electric spring operational strategy to minimize electricity bill,” Electric Power Systems Research, vol. 201, 2021, doi: 10.1016/j.epsr.2021.107540.
Loria F, Brinca P, Iskrev N I.On Identification Issues in Business Cycle Accounting Models[J].SSRN Electronic Journal, 2025, doi: 10.2139/ssrn.5119365.
A.E, “N, Alok V K,” Cloud Automation: New Era of Electricity Bill Automation using GPRS and Web Interface. International Journal of Computer Applications, vol. 29, no. 11, pp. 38-41, 2011, doi: 10.5120/3686-5111.
Kumar K R, Shreyaa R R, Thiyaneswaran K M,et al.Smart Run Time Electricity Bill Calculation using LabVIEW[J].International Journal of Innovative Technology and Exploring Engineering, 2020, doi: 10.35940/ijitee.d1855.039520.
T. Gerpott J and I. Mahmudova, “Determinants of price mark-up tolerance for green electricity - lessons for environmental marketing strategies from a study of residential electricity customers in Germany,” Business Strategy and the Environment, vol. 19, no. 5, pp. 304-318, 2010, doi: 10.1002/bse.646.
W. Zhao, “Application of Principal Component Analysis Algorithm in Abnormal Diagnosis of Electricity Bill Reading and Receiving Data,” in State Grid Henan Electric Power Company Marketing Service Center. 2024, doi: 10.4108/eai.8-12-2023.2344734.
M. Fikru G, “Estimated electricity bill savings for residential solar photovoltaic system owners: Are they accurate enough? Applied Energy,” 2019; 253:113501, doi: 10.1016/j.apenergy.2019.113501.
M. Fikru G, “Electricity bill savings and the role of energy efficiency improvements: A case study of residential solar adopters in the USA,” Renewable and Sustainable Energy Reviews, vol. 106, pp. 124-132, 2019, doi: 10.1016/j.rser.2019.02.028.
Y. Wang, X. Wang, and Y. Zhang, “Leveraging thermal storage to cut the electricity bill for datacenter cooling,” in University of Tennessee, Knoxville, University of Tennessee, Knoxville and The Ohio State University, University of Tennessee, Knoxville. 2011, doi: 10.1145/2039252.2039260.
Reddy M P, Gorain R, Ganguly G.Drive Cycle Type Identification Using Data Driven Methodologies[J].SAE International, 2026, doi: 10.4271/2026-26-0649.
Chen J, Cai Y.A Study of Big Data Prediction in Accounting Management Risks[J].Applied Mathematics and Nonlinear Sciences, 2024, 9(1), doi: 10.2478/amns-2024-0913.
M. Weng and Weng D.Discuss the Accounting Information Risks and Preventive Measures Based on Big Data[J], “2021,”, doi: 10.2991/assehr.k.210728.022.
D. Eryilmaz and S. Gafford, “Can a daily electricity bill unlock energy efficiency? Evidence from Texas,” The Electricity Journal, vol. 31, no. 3, pp. 7-11, 2018, doi: 10.1016/j.tej.2018.03.009.
A. M. Khatib, I. M. Mahable, S. S. Maniyar, et al., “IoT based Automatic Electricity bill generation with theft detection,” International Journal of Trend in Scientific Research and Development, vol. 2, no. 3, pp. 2586-2589, 2018, doi: 10.31142/ijtsrd11416.
S. José B H, M. Ascensión M C G, and S. David H, “Clear and transparent design proposal for the regulated electricity bill in Spain,” grafica, vol. 7, no. 13, pp. 11-11, 2018, doi: 10.5565/rev/grafica.125.
E. @ A F N, A. Pauzi M, H. Yusri M, et al., “Disaggregated Electricity Bill Base on Utilization factor and Time-of-use (ToU) Tariff,” International Journal of Electrical and Computer Engineering (IJECE), vol. 7, no. 3, pp. 1498-1505, 2017, doi: 10.11591/ijece.v7i3.pp1498-1505.
Cui H.A DBSCAN Anomaly Data Identification Method Driven by Data Distribution[J].Applied and Computational Engineering, 2025, 134(1):73-79, doi: 10.54254/2755-2721/2025.20861.
K. Muralitharan, R. Sakthivel, and Y. Shi, “Multiobjective optimization technique for demand side management with load balancing approach in smart grid,” Neurocomputing, vol. 177, pp. 110-119, 2016, doi: 10.1016/j.neucom.2015.11.015.
S. Huh, J. Woo, S. Lim, et al., “What do customers want from improved residential electricity services? Evidence from a choice experiment,” Energy Policy, vol. 85, pp. 410-420, 2015, doi: 10.1016/j.enpol.2015.04.029.
J. Octavio R B, G. Miguel J L, B. Emilio E, et al., “IoT-Based Electricity Bill for Domestic Applications,” Sensors (Basel, Switzerland), vol. 20, no. 21, pp. 6178, 2020, doi: 10.3390/s20216178.
S. Vipin and K. Sudhir S, “Modeling and Design of Data Cube for Electricity Bill Deposit System,” International Journal of Computer Applications, vol. 80, no. 11, pp. 11-14, 2013, doi: 10.5120/13904-1736.
C. Sibiya A, K. Kusakana, and B. Numbi P, “TRU Energy Monitoring for Potential Cost Saving in Electricity Bills for Cathodic Protection Units: South African Case,” International Journal of Electrical and Electronic Engineering & Telecommunications, vol. 10, no. 2, pp. 145-150, 2021, doi: 10.18178/ijeetc.10.2.145-150.
A. Zaman U and A. Mahbub A O, “Modeling Electricity Bill with the Reflection of CO2 Emissions and Methods of Implementing AMI for Smart Grid in Bangladesh,” International Journal of Intelligent Systems and Applications (IJISA), vol. 14, no. 5, pp. 14-21, 2022, doi: 10.5815/ijisa.2022.05.02.
J. Miota C, S. Khadem, and M. Bahloul, “Deep reinforcement learning based electricity bill minimization strategy for residential prosumer,” Mathematics and Computers in Simulation, vol. 238, pp. 296-305, 2025, doi: 10.1016/j.matcom.2025.06.011.
K. Li, N. Zhou, L. Wang, et al., “A Customer-Focused Electricity Bill Pay Points Siting Model,” Advanced Materials Research, vol. 588-589, pp. 2064-2068, 2012, doi: 10.4028/www.scientific.net/AMR.588-589.2064.
C. Cárdenas C., E. Gaytán A. A., and T. Ozuna, “The crucial role of electricity bill comprehension in consumer intention of solar panel systems adoption,” Energy Policy, vol. 210, Art. no. 115064, 2026, doi: 10.1016/j.enpol.2025.115064.
V. Kumar A., E., and N. A., “Secure Electricity Bill Automation Using GPRS and Web Interface in Conjunction with Elliptic Curve Crypto-Stegano Scheme,” International Journal of Computer Theory and Engineering, vol. 4, pp. 510-513, 2012, doi: 10.7763/ijcte.2012.v4.521.