Comparison of Machine Learning Algorithms for the Power Consumption Prediction - Case Study of Tetouan city

Predicting electricity power consumption is an important task which provides intelligence to utilities and helps them to improve their systems' performance in terms of productivity and effectiveness. Machine learning models are the most accurate models used in prediction. The goal of our study is to predict the electricity power consumption every 10 minutes, and/or every hour with the determining objective of which approach is the most successful. To this end, we will compare different types of machine learning models that recently have gained popularity: feedforward neural network with backpropagation algorithm, random forest, decision tree, and support vector machine for regression (SVR) with radial basis function kernel. The parameters associated with the comparative models are optimized based on Grid-search method in order to find the accurate performance. The dataset that is used in this comparative study is related to three different power distribution networks of Tetouan city which is located in north Morocco. The historical data used has been taken from Supervisory Control and Data Acquisition system (SCADA) every 10 minutes for the period between 2017-01-01 and 201712-31. The results indicate that random forest model achieved smaller prediction errors compared to their counterparts.