## 1 Introduction  
The Australian energy market has been operating on a centralised generation model with state-owned power plants situated closest to fossil fuel resources such as coal, hydro, wind, and natural gas for many years. The centralised electricity generation model demonstrates several drawbacks to the environment and end-users due to the reduced efficiency caused by large transmission losses. The electricity price keeps going up due to the increased investment in distribution infrastructure required to connect households and businesses to a stabilised power supply [1]. Therefore, the government has spent the last couple of decades shifting its electricity generation model to a more decentralised approach by incorporating more renewable resources. Despite the tremendous amount of investment, there are still many transitional challenges to be solved, both in terms of political and technological readiness. It is predicted that the role of grid-supplied power will be inverted from being the primary source of energy to a backup source, having distributed renewable generation as the primary source [1]. This paper investigates a similar distributed generation model commonly known as microgrids and addresses the systems' technical barrier of accurate forecasting of load demand, aiming to enable broader adoption of the decentralised generation model. The microgrid consists of a control unit that uses a robust energy management system equipped with advanced load demand/generation capacity forecasting algorithms that aids in achieving reliable power flow control, load sharing, grid protection, stability, and smooth operation [2]. If successfully implemented, micro-grid systems promise to offer distribution system congestion relief, the postponement of new generation or delivery capacity, response to load changes, and local voltage support [2]. Accurate forecasting of load demand is crucial to a microgrid's energy management system because it ensures energy savings and improves the operational or sizing efficiency of its supply and storage. However, load forecasting can be a tricky task since the energy performance of a building is influenced by various factors, such as occupancy rate, residence behaviour, household income, number and type of appliances, and weather conditions. This study aims to develop a novel deep neural network-based energy prediction algorithm to synthesise hourly load profiles based on the occupancy rate. To achieve this, a comprehensive multi-criteria analysis (MCA) was carried out on the existing literature initially, and a novel synthetic load profile generator was formulated. The MCA aims to identify the most suitable prediction algorithm by evaluating the accuracy, usability, adaptability, computation time, randomisation, and implementation feasibility. The synthetic load profile generator developed in this study is later used to generate the stochastic load profiles. A state-of-the-art predictive microgrid model was created to generate the load profile accurately using average load consumption data, occupancy rate, and seasonality as the input. The microgrid's mathematical model consists of realistic appliances with predefined constraints that help synthetically generate the accurate load profile. The proposed state-of-art eight-layered deep neural network model accurately predicts the hourly energy consumption patterns, considering the occupancy rate and seasonality as the critical inputs. Baseline models such as extreme gradient boost, multiple linear regression, and simple/shallow artificial neural network are used to verify the proposed prediction algorithm's performance. Root mean square error (RMSE) and coefficient of determination (\(R^{2}\)) values are the metrics that the models are compared on the results indicated that the proposed deep neural network model outperformed the other models.