The requirements for data privacy are changing, which affects energy Internet price plans. A novel strategy combines quantitative analysis, data modeling, and computer technologies to properly grasp this dynamic relationship. This is another reason the study included expert interviews. Using "Dynamic Pricing Optimization," the study compares the program to six other innovative data-driven strategies. The recommended strategy emphasizes data-driven decision-making and swift adjustment. This detailed report shows how data privacy is developing worldwide. This study uses formulas for dynamic pricing, privacy risk assessment, and equity analysis; therefore, it doesn't employ standard methodologies. Compared to other methods, the presented method works wonderfully. The proposed strategy excels in data coverage, real-time flexibility, accuracy, data-driven decision-making, computational weight, and policy application. Its increased efficiency suggests it can tackle the Energy Internet's complex issues.
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Rao et al. (2024) studied this question.
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