Document Type

Conference Proceeding

Language

eng

Publication Date

7-2014

Publisher

Institute of Electrical and Electronics Engineers

Source Publication

2014 IEEE PES General Meeting

Source ISSN

1540-7977

Original Item ID

doi: 10.1109/PESGM.2014.6939792

Abstract

Domain knowledge is an essential factor for forecasting energy demand. This paper introduces a method that incorporates machine learning techniques to learn domain knowledge by transforming the input features. Our approach divides the inputs into subsets and then searches for the best machine learning technique for transforming each subset of inputs. Preprocessing of the inputs is not required in our approach because the machine learning techniques appropriately transform the inputs. Hence, this technique is capable of learning where nonlinear transformations of the inputs are needed. We show that the learned data transformations correspond to energy forecasting domain knowledge. Transformed subsets of the inputs are combined using ensemble regression, and the final forecasted value is obtained. Our approach is tested with natural gas and electricity demand signals. Experimental results show how this method can learn domain knowledge, which yields improved forecasts.

Comments

Accepted version. Published as part of the proceedings of the conference, 2014 IEEE PES General Meeting, 2014. DOI. © 2014 Institute of Electrical and Electronics Engineers (IEEE). Used with permission.

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