## 6 Conclusions  
In this study, a predication algorithm of a residential building based on the occupancy rate was investigated. The synthetic load profile generator model proposed in this study is close to realistic model were used to generate the random load profiles which were used to train the proposed state-of-the-art deep ANN model. The computation time and the accuracy of different machine learning models were then compared, and the results indicated that the proposed deep ANN model was the most appropriate for energy consumption prediction.  
This study's main contribution was the novel predictive algorithm for load forecasting based on occupancy rate and the establishment of the finely tuned deep ANN model. Other findings from the research include:  
* MLR was the least accurate in prediction (17.2%), but it was the fastest in computation (28 s). Since the energy consumption values do not have a close linear relationship with time but instead present significant randomness in peak consumption, it was difficult for MLR to find the best fit function, and hence, accurately predict values.
* XGBoost performs better than MLR in predicting energy consumption (84.9%) but does not handle noise well and is not suitable for time series data set. Therefore, it falls in the middle range of the ranking.  
Figure 9: The graph of RMSE of estimated energy consumption value using MLR (cyan), XGBoost (blue), shallow ANN (red), and deep ANN (green).  
* Deep ANN performs better than shallow ANN and can take hours or days to train the data and create a prediction model. However, the model can be very accurate in prediction (97.5%) since it works well with random data set and can handle noise. It is at the top of the chart for its ability to accurately predicting energy consumption. In the cases where computation time is not a significant concern, deep ANN is highly recommended.  
Further research can be performed to optimise the hyper-parameters related to the ANN model, such as learning rate, momentum, epoch, iterations, and batch size. Additionally, the novel deep neural network based forecasting algorithm proposed can be later evaluated with realistic historical load profile data in the future. Overall, the authors believe that the novel synthetic load profile generator and the finely tuned deep ANN model developed in this study would be enhancing the performance of the load profile forecasting and can be used in future with wide variety of data sets. The synthetic profile generator could be of ideal use when we are not having access to historical data where the novel MG model can assist in generating the load profiles that can be used for forecasting the hourly energy consumption profile. The prediction algorithms also provide a great opportunity to a market operator to predict their customers' energy consumption with limited inputs, to help them identify the most optimal energy generation schedule.  
Conceptualisation: L.H.M.T., K.H.K.C., R.L., G.S.T., M.S. and A.S.; data curation: L.H.M.T., K.H.K.C. and R.L.; formal analysis: L.H.M.T., K.H.K.C. and R.L.; investigation: G.S.T., M.S., B.H., S.M. and A.S. methodology: L.H.M.T., K.H.K.C., R.L., G.S.T., M.S., B.H., S.M. and A.S.; project administration: L.H.M.T. and G.S.T.; resources: B.H., S.M. and A.S.; software: K.H.K.C. and G.S.T; writing--original draft: L.H.M.T., K.H.K.C., G.S.T. and R.L.; writing--review and editing: M.S., B.H., S.M. and A.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Daftlogic for the typical power consumption of households published in their website, which was the base for the synthetic data generation considered in this proposed study.  
The authors declare no conflict of interest.