## 4 Methodology
### Synthetic Load Profile Generation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Type of Household**} & \multirow{2}{*}{**No. of Occupants**} & \multirow{2}{*}{**Load**} & \multirow{2}{*}{**Wattage (W)**} & \multicolumn{2}{c}{**Amount of Time per Day (h)**} & \multirow{2}{*}{**Prefer Usage Time**} & \multirow{2}{*}{**Energy Consumption (Wh)**} & \multirow{2}{*}{**Cost per Day (AUD)**} \\  & & & & & & \\ \hline Household 5: & 4 & Fridge & 150 & 24 & 00:00–23:59 & 3600 \\ Family & & Washing & 800 & 0.7 & 9:00–10:00 & 560 \\  & & machine & & & & \\  & & Fan & 200 & 10 & 8:00–18:00 & 2000 \\  & & Microwave & 1000 & 0.5 & 10:00–11:00 & 500 \\  & & Heater & 2000 & 0 & - & - \\  & & Chargeable & & & & \\  & & devices & 300 & 12 & 20:00–8:00 & 3600 \\  & & TV & 150 & 3 & 19:00–22:00 & 450 \\  & & Toaster & 850 & 0.3 & 6:00–7:00 & 255 \\  & & Iron & 1200 & 0.5 & 6:00–7:00 & 600 \\  & & Vacuum & 1000 & 0.2 & 11:00–12:00 & 200 \\  & & cleaner & & & & \\  & & Dish washer & 1500 & 1 & 18:00–19:00 & 1500 \\  & & Air fryer & 1000 & 0.2 & 11:00–12:00 & 200 \\  & & Gaming & & & & \\  & console & & & & & \\  & & Coffee & & & & \\  & & machine & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Initial load profile 5 created for the project (summer). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Type of Household**} & \multirow{2}{*}{**No. of Occupants**} & \multirow{2}{*}{**Load**} & \multirow{2}{*}{**Wattage (W)**} & \multicolumn{2}{c}{**Amount of Time per Day (h)**} & \multirow{2}{*}{**Prefer Usage Time**} & \multirow{2}{*}{**Energy Consumption (Wh)**} & \multirow{2}{*}{**Cost per Day (AUD)**} \\  & & & & & & \\ \hline Household 6: & 5 & Fridge & 180 & 24 & 00:00–23:59 & 4320 \\ Big family & & Washing & 800 & 0.7 & 12:00–13:00 & 560 \\  & & machine & & & & \\  & & Fan & 200 & 12 & 12:00–24:00 & 2400 \\  & & Microwave & 1000 & 0.5 & 11:00–12:00 & 500 \\  & & Heater & 2000 & 0 & - & - \\  & & Chargeable & 300 & 12 & 20:00–8:00 & 3600 \\  & & devices & & & & \\  & & TV & 150 & 6 & 16:00–22:00 & 900 \\  & & Rice cooker & 850 & 1 & 11:00–12:00 & 850 \\  & & Toaster & 850 & 0.3 & 6:00–7:00 & 255 \\  & & Iron & 1200 & 0.5 & 19:00–20:00 & 600 \\  & & Vacuum & 1000 & 0.2 & 14:00–15:00 & 200 \\  & & cleaner & & & & \\  & & Dish washer & 1500 & 1 & 19:00–20:00 & 1500 \\  & & cleaner & & & & \\  & & Dish washer & 1500 & 1 & 18:00–19:00 & 1500 \\  & & Air fryer & 1000 & 0.2 & 11:00–12:00 & 200 \\  & Gaming & & & & & \\  & console & & & & & \\  & Coffee & & & & & \\  & machine & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Initial load profile 6 created for the project (summer). to the prediction model. In general, machine learning models require a feature (input) and a label (output) to learn and be successful in doing estimation. The randomly generated load profiles are appropriated tagged, such that it could be used to train the prediction algorithms considered in the study. The energy consumption values from the corresponding hour were then added together using Equation (1) to obtain a new version of the load profile for the different household. Hence, the hourly daily usage load profile is developed and shown in Table 9.  
\[E_{H}=\sum_{i=0}^{n}P_{i} \tag{1}\]  
where \(E_{H}\) is the energy amount in a particular hour and \(P_{i}\) is the power used in that hour by appliance \(i\). The background research indicated that the load forecasting techniques based on the occupancy rate and seasonality were niche and an area left unexplored which is considered as one of the key contribution of the proposed work. Based on which the  
Figure 4: Visual representation of all the load profiles.