Application and Evaluation of LSTM Architectures for Energy Time-Series Forecasting

Gunay Abdullayeva
Accurate energy forecasting is a very active research field as reliable information about future electricity generation allows for the safe operation of the power grid and helps to minimize excessive electricity production. As Recurrent Neural Networks outperform most machine learning approaches in time series forecasting, they became widely used models for energy forecasting problems. In this work, the Persistence forecast and ARIMA model as baseline methods and the long short-term memory (LSTM)-based neural networks with various configurations are constructed to implement multi-step energy forecasting. The presented work investigates three LSTM based architectures:
i) Standard LSTM, ii) Stack LSTM and iii) Sequence to Sequence LSTM architecture. Univariate and multivariate learning problems are investigated with each of these LSTM architectures. The LSTM models are implemented on six different time series which are taken from publicly available data. Overall, six LSTM models are trained for each time series. The performance of the LSTM models is measured by five different evaluation metrics. Considering the results of all the evaluation metrics, the robustness of the LSTM models is estimated over six time series.
Graduation Thesis language
Graduation Thesis type
Master - Computer Science
Alan Henry Tkaczyk, Meelis Kull, Nicolas Kuhaupt
Defence year