Comparison of Robust Machine-learning and Deep-learning Models for Midterm Electrical Load Forecasting

Authors : Fatma YAPRAKDAL, Fatih BAL

Pages : 102-107

Doi:10.36222/ejt.1201977

View : 10 | Download : 9

Publication Date : 2022-12-30

Article Type : Research Paper

Abstract :Electrical load forecasting insert ignore into journalissuearticles values(ELF); is gaining importance especially due to the severe impact of climate change on electrical energy usage and dynamically evolving smart grid technologies in the last decades. In this regard, medium-term load forecasting, a crucial need for power system planning insert ignore into journalissuearticles values(generation optimization and outages plan); and operation control, has become prominent in particular. Machine learning and deep learning-based techniques are currently trending approaches in electrical load estimation due to their capability to model complex non-linearity, feature abstraction and high accuracy, especially in the smart power systems environment. In this study, several load forecasting models based on machine learning methods which comprise linear regression insert ignore into journalissuearticles values(LR);, decision tree insert ignore into journalissuearticles values(DT);, random forest insert ignore into journalissuearticles values(RF);, gradient boosting, adaBoost, and deep learning techniques such as recurrent neural network insert ignore into journalissuearticles values(RNN); and long short-term memory insert ignore into journalissuearticles values(LSTM); are studied for medium-term electrical load demand forecasting at an aggregated level. Performance metric results of these analyzes are presented in detail. State-of-the-art feature selection models are examined on the dataset and their effects on these forecasting methods are evaluated. Numerical results show that forecasting performance can be significantly improved. These results are validated by the results of other studies on the subject and found to be superior.
Keywords : Medium term load forecasting, Machine learning, Deep learning, Aggregated level forecasting, Feature selection