## 4 Discussion
### Model Complexity and Accuracy, Key Caveats, and Limitations  
There were various methods to estimate electricity consumption in past research. However, because of the limitation of the number of datasets, we selected three MLAs, including Multiple Regression (MR), Decision Tree (DT), and Support Vector Regression (SVR), to predict the district's electricity consumption. From Figure 5, it is clear that DT's predicted values showed the closest one-to-one relationship to the actual values, which indicates that the DT model provided the best performance with the lowest MAE, RMSE, and the highest R2. On the other hand, MR and SVR were more likely to underestimate the actual values with weaker goodness of fit than the DT model. The regression method in machine learning may be challenged by the complexity of predictor variables, which could generate a high bias model. The increasing complexity is possibly caused by the decreasing of model accuracy. Thus, we need to optimize both complexity and accuracy, avoiding a more complex and over-fitting model. Figure 7 compares the trade-off between the model complexity and the error rate of MR, DT, and SVR models. The MR provided less complexity but the highest error, while DT provided the best performance with moderate complexity but the lowest error. In addition, the model parameter adjusting was a key to the predicting result. We let each model find the optimal parameters that provided the best training result. However, the prediction models may still need more adjusting in the details of each hyperparameter. The most critical hyperparameters are (1) stopping criteria and (2) pruning method, which control the balancing between model accuracy and complexity. Consequently, we need to balance complexity and accuracy, improving the model performance. In addition, DT is a complex model which tends to overfit. We can avoid this problem by setting the constraints on model parameters and pruning. The constraints on tree size should be set by fine-tuning the hyperparameters such as (1) minimum samples for a node split and a terminal node, (2) maximum depth of the tree (vertical depth), and (3) maximum feature to consider for a split. The tree pruning should be optimized between the size of the learning tree (horizontal grow) and predictive accuracy, then measured by cross-validation set for best optimizing performance. However, based on the number of data points in this study, our DT model was a small tree with a lower risk of overfitting than a large tree. The overfitting problem should be considered when applying the model to a larger dataset in further study. Figure 6: The correlation between geographical variables (remotely sensed data extraction); (**a**) BU and SL; (**b**) BU and AL. Figure 7: The optimal trade-offs between complexity and error of each estimation model. The model accuracy can be impacted by (1) the quality of the input dataset and (2) model adjusting parameters. Principally, the input data validation may be applied to improve the model accuracy. This study only validated the accuracy of remote sensing data extracting results of 2012 and 2016. We used 500 random sampling points from the national land-use dataset, verifying them with virtual ground truth from Landsat 7 and 8 images. In this work, the accuracy of extracting data for 2012 and 2016 was 78% and 85%, respectively, which was adequate for managing electricity on the district grid in EEC, Thailand, without the dwelling dataset. The more detailed validation of remote sensing data extraction should be conducted because the accuracy of the data extraction process will affect the result of the prediction model. However, if we can improve the extraction accuracy, the models should lead to more accurate predictions. In addition, we assumed that there are no invalid data in the socio-economic dataset following the standard of data source (The National Statistic Office, Thailand). However, this may not be true.