**5. Performance Analysis**

To computationally evaluate the cloud DNSE algorithm, the simulation set contains sensitivity based on error in measurement, grid variables, DNSE efficiency, and pseudo measurements. Reading the voltages at the node from a smart meter is the initial stage in this endeavor since smart meters are not set up to determine the voltages in the LV grid under examination. If the powers and voltages at all nodes are being monitored concurrently, this phase can be avoided. Table 1 displays dispersed networks for the lowvoltage grid's resistance, reactance, and admittance by both series and shunt in accordance with their network types.


**Table 1.** Distributed Networks of LV Grid.

Table 2 gives analysis based on various circuit models. The circuit models analyzed are resistance, reactance and admittance network type in terms of power analysis, energy efficiency, QoS, accuracy, precision, and recall. The energy management system (EMS) and long short-term memory (LSTM) networks are compared.


**Table 2.** Analysis based on various circuit models.

Figure 3a–f give analysis based on resistance type circuit model. The proposed technique attained power analysis of 83%, energy efficiency of 92%, QoS of 71%, accuracy of 85%, precision of 81%, and recall of 72%, EMS achieved a 79% power analysis, an 88% energy efficiency, a 66% quality of service, an 81% accuracy, a 77% precision, and a 66% recall, and power analysis of 83%, energy efficiency of 92%, QoS of 71%, accuracy of 85%, and precision of 81% were all achieved with LSTM.

**Figure 3.** *Cont*.

(d)

**Figure 3.** *Cont*.
