Why the current energy flow models may not work in the real world scenarios

Links table

Abstract and 1. Introduction

2. Evaluation methods

3. Review the current experiments

4. Circular and application assessments and 4.1. Prediction and response circulation

4.2. Applications for multiple -written situations and 4.3. Zero predict the ability of the application

4.4. Continuous prediction and 4.5. Normalization

5. numerical assessments and 5.1. Experience settings

5.2. Overview of the evaluation

5.3. Failure evaluation

5.4. Accuracy

5.5. Efficiency evaluation

6. Open questions

7. Conclusion

Approach a and references

3. Review the current experiments

Before conducting assessments of the methods mentioned in Table 1, we aim here to provide a detailed review of the current DPFL experiments in the literature, as shown in Table 2. This review intends to provide experimental achievements for previous DPFL studies, during the same time detection of importance and the need for a broad numerical comparison for each DPFL methods. Here are additional discussions of Table 2.

First, Table 2 indicates that transmission and distribution networks were used as testing cases to check DPFL methods. While distribution networks differ from the transmission networks in terms of symmetry and Tobology, the DPFL methods are generally applicable to both types of systems, such as methods in [15, 12, 30, 21, 11]. The reasons are two parts. First, even if the three stages in the distribution systems are unbalanced, it is still possible to implement DPFL methods by training the DPFL model for each stage [52]Or training the DPFL model in general for all variables in three stages [22, 11, 12, 24]. Note that the latter can generate a DPFL model that reflects the mutual effects between the stages. Second, from the DPFL perspective, radiology does not constitute any unique challenges compared to networkology, as the difference is only in the number of approved and independent variables. In short, while distribution networks may have unbalanced properties and radiology, these features do not bring special difficulties to DPFL studies.

Second, Table 2 reveals that many assessments depend only on the artificial data of training and testing, without considering the effects of noise and extremist establishment on data. The perfect test environment is rarely found in real life, and the resulting conclusions may not be in practice. To address this problem, it is recommended to inject artificial data with noise and extremist standing to imitate the real world’s scenarios.

Third, as shown in Table 2, a few studies are about the scope of the volatility of pregnancy used in simulations. This is worth the indication that the DPFL model accurately depends on the simulation fluctuations. For example, the range of narrow fluctuation usually leads to a higher accuracy of the DPFL model. Without this information, it is difficult to determine the high resolution of the evaluation DPFL model.

Finally, Table 2 refers to any of the DPFL methods that were evaluated in current DPFL studies. These assessments aim to implement a comparative analysis between the DPFL methods in force at the time. However, the scope of these comparisons is very narrow, with a few DPFL studies that make up against a limited number of current DPFL methods (some works conducted only comparisons with PPFL roads). Such comparisons fail to provide an in -depth understanding of the general performance of DPFL methods. Thus, it is clear that there is a clear need for a more comprehensive and comprehensive comparison through all DPFL methods, in order to show its relative advantages and restrictions comprehensively.