Analysis of Power Grid User Behavior Based on Data Mining Algorithms – System Design and Implementation
DOI:
https://doi.org/10.13052/dgaej2156-3306.3937Keywords:
Data mining, grid user behavior, analysis system, ZigBee, K-means algorithmAbstract
A data mining based power grid user behavior analysis system has been designed to address the issues of insufficient stability and accuracy in existing power grid user behavior analysis systems. Design the overall structure of the power grid user behavior analysis system; In terms of system hardware design, select a core controller, build and install a server as the foundation for system information transmission and logical operation; Based on ZigBee wireless communication technology, a ZigBee wireless communication protocol stack and communication expansion board were designed; In terms of system software design, Python is used to crawl user behavior data in the system data collection layer, and Python language is used to maintain the crawling program; Use the K-means algorithm to perform secondary mining and clustering on power grid user behavior data, obtain the analysis results of power grid user behavior, and transmit them to the system visualization display layer. The weight and Rand coefficient of data analysis were used as indicators to test the application effect of the method in this paper. The experimental results showed that the system can stably and accurately analyze the behavior of power grid users, and has good application effect. This research achievement has important reference significance for the research in the field of power grid user behavior analysis in the world scientific community.
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References
Real-time Market-to-Market Oscillation Management. DOI: 10.1109/ PESGM46819.2021.9638189.
Karthikeyan, S. Prabhakar, Saravanan, B., Jain, Aman, et al. A comparative study on transmission network cost allocation methodologies[C]. /2013 International conference on power, energy and control. Institute of Electrical and Electronics Engineers, 2013:145–152.
Deng S, Cai Q, Zhang Z, et al. User Behavior Analysis Based on Stacked Autoencoder and Clustering in Complex Power Grid Environment. IEEE Transactions on Intelligent Transportation Systems, 2021, 3(15):1–15.
Guo Y, Wu L, Wang C, et al. Optimal Configuration of Multi-energy Storage for Load Aggregators Considering User Behavior[C]/2020 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia). IEEE, 2020. DOI: 10.1109/ICPSAsia48933.2020.9208441.
Equilibrium Analysis of Electricity Market With Demand Response Exchange to Counterbalance Bid Deviations of Renewable Generators. DOI: 10.1109/JSYST.2019.2928042
Kapici, E., Kutluay, E., and Izadi-Zamanabadi, R. (2022). A novel intelligent control method for domestic refrigerators based on user behavior. International Journal of Refrigeration (136–), 136.
karthikeyan, Prabhakar S, Palanisamy, et al. Comparison of Intelligent Techniques to Solve Economic Load Dispatch Problem with Line Flow Constraints[C]/Advance Computing Conference, IACC, 2009 IEEE International. 2009.
Li, H., Chen, Q., Zhong, Z., Gong, R., and Han, G. (2022). E-word of mouth sentiment analysis for user behavior studies. Information Processing & Management: Libraries and Information Retrieval Systems and Communication Networks: An International Journal. 5(1), 59–63.
Lu, R., Liu, N., Li, D., Luo, X., and Fan, Y. (2021). Intelligent monitoring analysis of power grid monitoring information based on big data mining. Journal of Physics: Conference Series, 1992(3), 32132.1–032132.8.
Kaur R, Gabrijelcic D. (2022). Behavior segmentation of electricity consumption patterns: A cluster analytical approach. Knowledge-based systems, 251(5):109236.1–109236.16.
Yin L, Zhong Q. (2023). GoogLeResNet3 network for detecting the abnormal electricity consumption behavior of users. International journal of electrical power and energy systems, 145(2):108733.1–108733.11.
Baker, M. A. (2021). Household electricity load forecasting toward demand response program using data mining techniques in a traditional power grid. International Journal of Energy Economics and Policy, 11(4), 132–148.
Qi, Z. A., Hl, B., Xw, A., Tp, A., and Jw, A. (2019). Analysis of users’ electricity consumption behavior based on ensemble clustering. Global Energy Interconnection, 2(6), 479–488.
Hoendervanger, J. G., Yperen, N., Mobach, M. P., and Albers, C. J. (2022). Perceived fit and user behavior in activity-based work environments. Environment & behavior 54(1):143–169.
Suo, N., and Zhou, Z. (2021). Computer assistance analysis of power grid relay protection based on data mining. Computer-Aided Design and Applications, 18(S4), 61–71.
Hong, Z., Wei, Z., Li, J., and Han, X. (2021). A novel capacity demand analysis method of energy storage system for peak shaving based on data-driven. The Journal of Energy Storage, 39(7), 102617.
Noami, A., Kumar, B. P., and Chandrasekhar, P. (2020). Design and Implementation of a United Multi-Core Memory Controller using AXI4. Lite Interface Protocol, 4(7), 45–51.
Ravi, N., Rao, T. S., and Prasad, T. J. (2022). Pipelined c 2 mos register high speed modified, Int. J. Advanced Networking and Applications, 3(1), 1031–1034.
Helms, P., and Limmer, D. T. (2022). Stochastic thermodynamic bounds on logical circuit operation, arXiv, 11(1), 670–676.
Blasco-Arcas, L., Kastanakis, M. N., Alcaiz, M., and Reyes-Menendez, A. (2023). Leveraging user behavior and data science technologies for management: an overview. Journal of Business Research, 154(3), 3–7.
Liu Z, Wang Y, Zeng Q, et al. (2021). Research on Optimization Measures of Zigbee Network Connection in an Imitated Mine Fading Channel. Electronics, 10(2), 171–178.
Yao S, Feng L, Zhao J, et al. (2021). PatternBee: Enabling ZigBee-to-BLE Direct Communication by Offset Resistant Patterns. IEEE Wireless Communications, 28(3), 130–137.
Palate, B. O., and Vera, E. G. (2020). Optimizing Aggregators Placement in Distribution Networks Using ZigBee Technology through PSO and Graph Theory. 2020 IEEE PES Transmission & Distribution Conference and Exhibition – Latin America (T&D LA). 28(9), 2039–2045.
Oveisi, M., and Heydari, P. (2022). A study of ber and evm degradation in digital modulation schemes due to pll jitter and communication-link noise. IEEE transactions on circuits and systems, I. Regular papers: a publication of the IEEE Circuits and Systems Society, 1(8), 69–75.
Desnanjaya, I., Nugraha, I., Pranata, I., and Harianto, W. (2021). Stability data xbee s2b zigbee communication on arduino based sumo robot. International Journal of Robotics and Control, 2(3), 153–160.
Cayre, R., Galtier, F., Auriol, G., Nicomette, V., Kaaniche, M., & Marconato, G. (2021). WazaBee: attacking Zigbee networks by diverting Bluetooth Low Energy chips. Dependable Systems and Networks. IEEE, 6(8), 21–24.
Bousbaa, Z., Nowak-Brzezińska, A. (2023). Knowledge Engineering and Data Mining Electronics, 12(4), 927.
Konys, A., Sanchez-Medina, J., Bencharef, O.(2023). Financial Time Series Forecasting: A Data Stream Mining-Based System. Electronics, 12(9),2039.
Abideen, Z. U., Mazhar, T., Razzaq, A., Haq, I., Ullah, I., Alasmary, H., and Mohamed, H. G. (2023). Analysis of Enrollment Criteria in Secondary Schools Using Machine Learning and Data Mining Approach. Electronics, 12(3), 694.
Wang, X. Z., Ruan, J. J., Zhou, T. T., Peng, X. L., Deng, Y. Q., and Yang, Q. Y. (2022). Data Mining in the Vibration Signal of the Trip Mechanism in Circuit Breakers Based on VMD-PSR. Electronics, 11(22), 3700.
Chen, G. Y., Zhu, Z. Y., Yang, L., Huang, W. H., Zhang, Y. Z., Lin, G., and Zhang, S. J. (2022). Intelligent Identification and Order-Sensitive Correction Method of Outliers from Multi-Data Source Based on Historical Data Mining. Electronics, 11(18), 2819.
Envelope T F P. (2021). Research on automatic user identification system of leaked electricity based on Data Mining Technology – ScienceDirect. Energy Reports, 7(11): 1092–1100.
Sajwan K, Sharma M, Shukla A K. (2021). Performance Evaluation of Two Medium-Grade Power Generation Systems with CO2
Based Transcritical Rankine Cycle (CTRC). Distributed Generation and Alternative Energy Journal, 35(2):111–138.
Kumar P N, Chengaiah C, Rajesh P, et al. (2021). A Hybrid Technique for the Performance Optimization in the Combustion Process of a Power Plant Boiler: An Efficient ANNSSA Technique. Distributed Generation and Alternative Energy Journal, 28 (5):1561–1572.
Qadeer A, Khan M E, Alam S. (2021). Estimation of Solar Radiation on Tilted Surface by Using Regression Analysis at Different Locations in India. Distributed Generation and Alternative Energy Journal, 35(1): 1–18.
