A Comparative Analysis of RNN and SVM : Electricity Price Forecasting in Energy Management Systems
Sammanfattning: A trend in increasing electricity consumption and technological innovation has resulted in automated energy management systems. Forecasting movement of the electricity price with machine learning plays a role in the sustainability of these systems. The aim of the report is to compare two machine learning methods, namely Recurrent Neural Network (RNN) with LSTM, and Support Vector Machine (SVM). The metric to evaluate is percentage in prediction accuracy, additionally statistical analysis is applied for further evaluation. The models are built and optimized on a single historic dataset from an Australian electricity market where the major influencing attributes are price, demand and time. The training and test set are split 80/20 whereas the training is done in 10 folds for cross validation. Results of the experiment show that the SVM-model had a slightly higher accuracy and a lower standard error of the mean. Differences were seen in sensitivity and specificity when applied to a confusion matrix. The conclusion made was that in this specific case, SVM outperformed RNN in prediction accuracy, however, there is room for improvement of both implementations of these methods which could lead to a different result. In regard to specificity and sensitivity the choice of an SVM or RNN would be highly dependent on the implementation of real-world application.
HÄR KAN DU HÄMTA UPPSATSEN I FULLTEXT. (följ länken till nästa sida)