Fossil fuels represent the highest share of the world's energy sources for thermal energy generation (IEA Report, 2019), of which a fraction is used in District Heating Networks (DHN). Therefore, decarbonising these district heating sectors would diminish \chemform{CO_2} emissions. This research tackles this issue by considering energy efficiency improvement, which can be effectively accomplished by optimising DHNs. Though there is an increase in the utilisation of renewable sources in district heating thermal energy generation, optimisation is still paramount to minimise energy consumption and cost, and extend the life span of the generational unit components. Several optimisation approaches can be applied to District Heating Systems. However, an efficient optimisation technique is Dynamic Real-time Optimisation (DRTO) due to the need to consider real-time control and operation of DHNs, for design or operation improvement purposes. An initial stage of the DRTO is the planning phase to obtain the optimal design parameters, optimal trajectories of the operating parameters and topology of the network. The planning phase cannot be efficiently realised without the foundational models of the DHN. This research is part of the RESEAUDATA project, which aims to improve heat network management through machine learning and dynamic optimisation techniques. The machine learning agent must be trained with Big Data from a process simulation to derive a black box model which fits real-time optimisation techniques due to its low computational time. This paper focuses on modelling the DHN to accomplish the optimal planning phase of DRTO and simulating the network to generate the Big Data to form and train the machine learning model. A transient model is developed from the principle of general mass and energy balance around the pipes and the unit components in the considered District Heating System, giving a Partial Differential Equation (PDE) after eliminating the algebraic equations. The space domain of the partial differential equation is discretised using the Finite Element Backward Difference Approach, and the time domain is retained in its differential form to give a system of Ordinary Differential Equations. A case study of ten (10) consumers in Pau, France, was considered. These consumers include a residence, Large office, Hospital, Hotel, Strip Mall, Restaurant, Super Market, Apartment, Secondary School and a Warehouse with varying heat demands over time. The supply temperature and mass flow rate of the heat transfer fluid at each consumer were simulated with time alongside the network temperature during the forward and return flow distribution. The simulation of the hydraulic parameters in the time domain was also considered. The general principle for building and training efficient machine learning models that could represent the knowledge-based model of a District Heating Network was explicitly explained.