# Machine Learning and Bagging to Predict Midterm Electricity Consumption in Saudi Arabia
## 2 Related Work
Furthermore, despite the fact that the dataset is heavily affected by temporal characteristics such as time, no previous study mapped the time features to represent their cyclical attributes. Hence, this study investigated prediction through normalizing the input features using min-max, transforming the temporal attributes using Fourier transformation, and mapping the inputs to their sine and cosine counterparts. In terms of how far the models predict electricity consumption, past papers predicted the consumption daily [17], monthly [3; 11; 12; 15; 17; 18], semi-monthly [10], and yearly [9; 13; 14; 15]. In terms of evaluation metrics, they varied across the literature. However, the most essential and common metric was MAPE, with almost all the past experiments utilizing it to evaluate their model. This work used CC, MAPE, RMSPE, MAE, and MAPE to evaluate the proposed machine learning models. Provided the analysis of relevant work on predicting electricity consumption in the literature review, it is clear that predicting electricity consumption using ANN was widely used due to how well the ANN performs with large datasets. Moreover, the literature implies the need to attempt electricity consumption forecasting with methods that have not been investigated before, such as utilizing instance-based machine learning methods, where there is room for investigation. Hence, this work investigates the performance of the K* algorithm in predicting solar radiation in Saudi Arabia. Additionally, in the literature, ensemble techniques were attempted for predicting electricity consumption in one study, although ensembles have been proven to provide good results. Therefore, this work investigates the effects of ensemble methods (i.e., bagging) on predicting electricity consumption in Saudi Arabia. From the literature, in Saudi Arabia, the experiments that investigated obtained their datasets from the local institutions that have physical or soft copies of the electricity consumption for said institutes [15; 17], general statistics that are shared publicly [13], or from the Saudi Consolidated Electric Company of the Eastern Province [15; 17; 18]. Most of the past literature attempted electricity consumption forecasting in the eastern region alone. However, the specific cities included were unclear and the region was treated as a unit. This work deploys a new dataset provided by the Saudi Electrical Company (SEC) for investigating the effects of machine learning techniques on predicting electricity consumption. Also, this dataset was the result of personal obtention and was not used before in any research. Furthermore, the dataset used in this work contains the electricity consumption of all areas covered by the SEC in Saudi Arabia divided by service offices, 18 of which are included in this study (i.e., the service offices that cover the same area of the tier 1 monitoring stations). Therefore, this research closely examined the performance of ensemble models and the K* model as novel models to predict the monthly electricity consumption for eighteen service offices from the Saudi Electrical Company dataset, providing experiments on a new electricity consumption dataset.