Journal
ENERGY
Volume 140, Issue -, Pages 893-900Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2017.09.026
Keywords
Natural gas; Henry hub; Machine learning; Feature selection algorithm; Support vector regression machines; Neural networks
Categories
Funding
- Croatian Science Foundation [IP-2013-11-2203]
Ask authors/readers for more resources
We present the results of short-term forecasting of Henry Hub spot natural gas prices based on the performance of classical time series models and machine learning methods, specifically; neural networks (NN) and strategic seasonality-adjusted support vector regression machines(SSA-SVR). We introduce several improvements to the forecasting method based on SVR. A procedure for generation of model inputs and model input selection using feature selection (FS) algorithms is suggested. The use of FS algorithms for automatic selection of model input and the use of advanced global optimization technique PSwarm for the optimization of SVR hyper parameters reduce the subjective inputs. Our results show that the machine learning results reported in the literature often over exaggerate the successfulness of these models since, in some cases, we record only slight improvements over the time series approaches. We have to emphasize that our findings apply to Henry Hub, a market which is known among traders as the widow maker. We find definite advantages of using FS algorithms to preselect the variables both in NN and SVR. Machine learning models without the preselection of variables are often inferior to time series models in forecasting spot prices and in this case FS algorithms show their usefulness and strength. (C) 2017 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available