Evaluation Method of Efficient Power Marketing Strategy Based on Multi-dimensional Clustering Algorithm
DOI:
https://doi.org/10.13052/spee1048-5236.4233Keywords:
Big data, multi-dimensional clustering algorithm, power marketing, marketing strategy, target customer groupsAbstract
In the power system, power marketing is a very important work. How to evaluate whether the currently used power marketing strategy is efficient and useful has always been the most important concern of the power system. This paper proposes an efficient electricity marketing strategy evaluation method based on multi-dimensional clustering algorithm, aiming to evaluate whether a new policy has an incentive effect on enterprises through the means of big data. First, we use big data to group motivated users, and then analyze the effect of personalized marketing and key marketing on target customer groups. Based on the existing marketing methods, the influence of different target groups is analyzed, and finally the feasibility of the proposed method is verified through two experiments. In the A and B companies selected in Experiment 1, the average errors of the predicted value and the actual value are 1.4% and 1.2%, respectively. For the 12 industries in the C area selected in experiment 2, the average error between the predicted value and the actual value is 0.55%. Both experiments show that the method proposed in this paper has certain applicability. The method in this paper can be applied to the marketing process of the power supply company, and the corresponding promotion strategy is put forward, which is helpful to improve the management level of the power supply enterprise and increase the economic income of the power supply enterprise.
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References
S. K. Rathor, D. Saxena. Energy management system for smart grid: An overview and key issues. International Journal of Energy Research, 2020, 44(6), pp. 4067–4109.
Zhao J.X., Guo S., Mu D.. DouBiGRU-A: software defect detection algorithm based on attention mechanism and double BiGRU. Comput. Secur. 2021, 111, 102459.
Verdejo M A, Fernandez J. Prediction model for the electrical industry in spain-the trend toward renewable energy. Strategic Planning for Energy & the Environment, 2016, 35(3):9–28,31.
H M Hussain, N Javaid, S Iqbal et al. An efficient demand side management system with a new optimized home energy management controller in smart grid. Energies, 2018 (1):1–10.
H Qiu, A Zhou, B Hu et al. TAnalyzer: A graph database based topology analysis tool for power grid. proceedings of the 2nd International Conference on Computer Science and Application Engineering, pp. 1–5, 2018.
I J. Poolo. A Smart Grid Demand Side Management Framework Based on Advanced Metering Infrastructure, American Journal of Electrical and Electronics Engineering, 2017, 5(4):152–158.
Zhao J.X., Guo S., Mu D. Model design of artificial immune system in power cyber security protection. 2020 2nd International Conference on Applied Machine Learning (ICAML). 2020, 111, 102459.
Nguyen T N, Pham T H, Nguyen T T N, et al. Promoting energy efficiency in an emerging market: insights and intervention strategies. Strategic Planning for Energy and the Environment, 2018, 38(2):70–80.
You-Fu Y I. Research on Marketing Management and Intelligent Power Utilization Development Strategy for the Power Grid in Qinghai Province. Qinghai Electric Power, 2012.
Liu Zhangrong. Target feature data mining method for information intelligent recommendation system. Information technology, 2022 (03): 162–171.
Deng Lijuan, Zhang Bei. Students’ Behavior Data Analysis Based on the Cloud Classroom Administration Platform. Journal of Jimei University, 2019, 20(03): 63–69.
Shi Hongtao, Yan Jia, Ding Maosheng, Gao Feng, Zhang Zhifeng, Li Yixuan. Wind Power Prediction Method Based on Novel Multi-dimensional Power Trend Clustering. High voltage technology, 2022, 48(02): 430–438.
Chen Yuanyuan, Wang Zhihai. Concept Drift Detection Method for Multidimensional Data Stream Based on Clustering Partition. Computer science, 2022, 49(07): 25–30.
Zhu Haiyang, Qian Zhonghao, Yan Fan, Mao Kejin, Ying Hao Ke. An Interactive Visual Analysis Method for Multi-Dimensional Data Deduplication. Journal of Computer-Aided Design and Computer Graphics, 2022, 34(06): 841–851.
Zhao Jinxiong, Guo Sensen, Mu Dejun. DouBiGRU-A: Software defect detection algorithm based on attention mechanism and double BiGRU. Computers & Security, 2021, (111): 102459.
Zhao Hong, Shen Jianzhong, Wang Jun, Zhang Ying, Qu Qing. Prediction Model of Electric Charge Recycling Risk Based on Customer Portrait and Machine Learning Algorithm and Its Application. Micro computer application, 2020,36 (02): 93–96.
Zhu Warring States, Wang Yue. Impacts of the Multidimensional Image of GI Labels on Consumers’Online Purchase Intentions of Products with Geographical Indication—A Perceived Value Perspective. Journal of Jiangsu University, 2022, 24(02):57–69.
Liu Bin, Li Xiao. Application of Multi-label Classification Based on Digital Content Preference. Computer and modernization, 2021 (02): 45–50.
Zhao Jinxiong, Zhang Xun, Di Fuqiang. Exploring the Optimum Proactive Defense Strategy for the Power Systems from an Attack Perspective. Security and Communication Networks, 2021, 2021.
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