Abstract
The ability to accurately forecast the spot price of natural gas benefits stakeholders and is a valuable tool for all market participants in the competitive gas market. In this paper, we attempt to forecast the natural gas spot price 1, 3, 5, and 10 days ahead using machine learning methods: support vector machines (SVM), regression trees, linear regression, Gaussian process regression (GPR), and ensemble of trees. These models are trained with a set of 21 explanatory variables in a 5-fold cross-validation scheme with 90% of the dataset used for training and the remaining 10% used for testing the out-of-sample generalization ability. The results show that these machine learning methods all have different forecasting accuracy for every time frame when it comes to forecasting natural gas spot prices. However, the bagged trees (belonging to the ensemble of trees method) and the linear SVM models have superior forecasting performance compared to the rest of the models.
Highlights
Natural gas has been proposed as a solution to increase the security of the energy supply and to reduce environmental pollution around the world
The accurate forecasting of natural gas spot prices is of high importance, as these forecasts are used in the energy market, in power system planning and in regulatory decision making, covering both supply and demand in the natural gas market
Due to the significant economic results obtained from forecasting, many techniques have been explored and studied, especially in electric load forecasting, such as artificial neural networks (ANN), as seen in [2] and support vector machines (SVM), as seen in [3] and many other works
Summary
Natural gas has been proposed as a solution to increase the security of the energy supply and to reduce environmental pollution around the world. It is the second most widely used energy commodity after oil [1]. With the replacement of coal and the widespread use of natural gas, gas spot price forecasting has become one of the most critical issues in many sectors. The accurate forecasting of natural gas spot prices is of high importance, as these forecasts are used in the energy market, in power system planning and in regulatory decision making, covering both supply and demand in the natural gas market. Publications in the field of natural gas price forecasting are relatively rare [1]
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