## 1 Introduction
### Literature Review
Using pseudo-measurement models involves using some existing measurements and system models to improve the completeness of the EDN. This technology is employed to enhance the control and monitoring capabilities of the SCADA system [15]. One of the most important pseudo-measurement modelling used in the state estimation of an EDN is based on the load profiles of the EDSs. Due to the high degree of distributional variability, determining the exact load profile of a particular EDS is challenging. This process usually involves the typical load profiles for different types of consumers, such as residential, commercial, and industrial [16]. Reference [17] proposed a decentralised load estimating method integrated as support in the real-time optimisation of the voltage level based on the typical load profiles. The process is based on local data and limited information from its neighbour areas, enabling the selection and partitioning of the load profiles. It aims to limit the communication needs while ensuring the accuracy of the estimates. The weighted least squares (WLS) model has changed and improved in [18] to allow a much more generalised application of the method, assuming that different load and generation profiles may coexist behind each node of the EDN. The proposed method for establishing and analysing the status of the EEE 123-bus grid in unobservable areas has been tested in various scenarios. Unfortunately, the authors created theoretical scenarios regarding the loading and generation for the EDN while emulating the multiple behaviours of customers without considering the actual cases. Unfortunately, it is not always feasible to create pseudo-measurements for the EDNs due to the lack of established procedures. For this reason, other methods have been proposed that can address the issue without requiring the DSOs to define additional pseudo-measurement inputs. [PERSON] et al. proposed in [19] a control and prevention method for operating an EDN based on ultra-short-term load forecasting. Finally, the risk assessment results were sorted and screened before identifying the most critical incidents. [PERSON] et al. proposed in [20] a general expression used to estimate the various factors that affect the power demand of a distribution network during a restoration process. It also considers the relationship between the frequency behaviour and the distributed generation. [PERSON] et al. [21] developed a dynamic estimation method used to monitor the operation of an active distributed power network. It considered the various features of the operation scenario of the distribution network and its distribution generation. The method contains two phases associated with an adaptive estimation and an integrated multi-model algorithm. The first phase is to improve and incorporate the two standard estimators into a framework. The second phase identifies the operation modes and produces joint estimates. [PERSON] et al. developed a novel method that can estimate the baseline load for residential electric loads in different conditions [22]. It uses a deep learning approach to select the appropriate customer baseline load based on the historical data of the customers and non-participants. This method can also benefit from the concurrent load data collected from the participants in the Demand Response programme and non-participants. Reference [23] considered the various aspects of electric load, such as the control mode and the vehicle's capability to run on a network, producing a comprehensive estimate of the total energy consumption of plug-in electric vehicles. It then considers the aggregate demand of different load categories at the feeder head. The authors from reference [24] developed an optimal prediction interval estimation method used for various information regarding the feeders. It can be applied using reinforcement learning, which enables the integration of two online tasks. The hybrid approach has been proposed in [25] to provide a short-term load forecasting solution.