## 4 Methodology
### Synthetic Load Profile Generation  
In order to obtain a wide variety of load profiles based on the occupancy rate and seasonality, we developed a synthetic load profile generator that considers basic mathematical models of individual appliances to generate a set of load profiles programmatically. Initially, the model includes a load profile of different households with varying factors, such as the type of residence and number of people, as shown in Tables 3-8. Figure 4 provided a visual representation of the individual load profiles considered in the study. The synthetically generated household load profile models consist of a randomised algorithm that creates different appliance loads and the usage time. The load profiles are constrained to schedule the total amount of power consumption in kWh randomly and the electricity cost per day of the load profile to closely resemble real-world usage. The synthetic load profile generator is later used to generate a data sample of 100,000 data points (load profiles) for each type of occupancy rate, and these data are used by the proposed forecasting model to train and make predictions based on the limited number of inputs. The mathematical models are provided with a pre-defined set of constraints to generate the different non-linear load profiles replicating the real-time load profiles. Inferences from the power consumption studies on households are used as the basic input for the models to systematically generate numerous number of random samples of load profiles. These generated load profiles are then used to train a novel prediction algorithm to forecast the load profile at higher accuracy. The following factors are taken into consideration:  
* The hourly electricity usage data used in the study were obtained from residential buildings with a different number of occupants in Victoria, Australia. * Electricity bills and statistics provided by energy providers and distributors were used to identify the average daily usage, which was then considered as a reference point to fine-tune the load profiles to represent the daily usage in Victoria, Australia in 2020, and it was programmatically generated using the synthetic load profile generator. Figure 3: Research methodology. * The power ratings for all of the appliances present in the load profile table were taken from the existing appliances and from the Daftlogic website, which provides the typical power consumption list of households [22]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type of Household** & **No. of Occupants** & **Load** & **Wattage (W)** & **Amount of Time per Day (h)** & **Prefer Usage Time** & **Energy Consumption (Wh)** & **Cost per Day (AUD)** \\ \hline Household 1: University student & 1 & Fridge & 80 & 24 & 00:00–23:59 & 1920 \\  & & Washing machine & 800 & 0.1 & 10:00–11:00 & 80 \\  & & Fan & 200 & 10 & 13:00–23:00 & 2000 \\  & & Microwave & 800 & 0.1 & 12:00–13:00 & 80 \\  & & Heater & 2000 & 0 & - & - \\  & & Chargeable devices & 150 & 8 & 22:00–6:00 & 1200 \\  & & Rice cooker & 830 & 0.5 & 11:00–12:00 & 415 \\  & & Toaster & 850 & 0.05 & 7:00–8:00 & 42.5 \\  & & TV & 150 & 4 & 18:00–22:00 & 600 \\  & & Gaming console & 150 & 4 & 18:00–22:00 & 600 \\  & & Other & & & & \\  & chargeable devices & 200 & 6 & 10:00–16:00 & 1200 \\ \hline  & & & & & Average & & \\  & & & & & S = 7.5, & & 8137.5 & 3.2924425 \\  & & & & W = 10.4 kWh & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Initial load profile 1 created for the project (summer).