LAPSE:2018.0560
Published Article
LAPSE:2018.0560
Load Forecasting for a Campus University Using Ensemble Methods Based on Regression Trees
María del Carmen Ruiz-Abellón, Antonio Gabaldón, Antonio Guillamón
September 21, 2018
Load forecasting models are of great importance in Electricity Markets and a wide range of techniques have been developed according to the objective being pursued. The increase of smart meters in different sectors (residential, commercial, universities, etc.) allows accessing the electricity consumption nearly in real time and provides those customers with large datasets that contain valuable information. In this context, supervised machine learning methods play an essential role. The purpose of the present study is to evaluate the effectiveness of using ensemble methods based on regression trees in short-term load forecasting. To illustrate this task, four methods (bagging, random forest, conditional forest, and boosting) are applied to historical load data of a campus university in Cartagena (Spain). In addition to temperature, calendar variables as well as different types of special days are considered as predictors to improve the predictions. Finally, a real application to the Spanish Electricity Market is developed: 48-h-ahead predictions are used to evaluate the economical savings that the consumer (the campus university) can obtain through the participation as a direct market consumer instead of purchasing the electricity from a retailer.
Keywords
direct market consumers, Electricity Markets, ensemble methods, load forecasting models, regression trees
Suggested Citation
Ruiz-Abellón MDC, Gabaldón A, Guillamón A. Load Forecasting for a Campus University Using Ensemble Methods Based on Regression Trees. (2018). LAPSE:2018.0560
Author Affiliations
Ruiz-Abellón MDC: Department of Applied Mathematics and Statistics, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
Gabaldón A: Department of Electrical Engineering, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain [ORCID]
Guillamón A: Department of Applied Mathematics and Statistics, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
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Journal Name
Energies
Volume
11
Issue
8
Article Number
E2038
Year
2018
Publication Date
2018-08-06
Published Version
ISSN
1996-1073
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Original Submission
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PII: en11082038, Publication Type: Journal Article
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LAPSE:2018.0560
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doi:10.3390/en11082038
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Sep 21, 2018
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Calvin Tsay
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