Load Forecasting for a Campus University Using Ensemble Methods Based on Regression Trees

María Del Carmen Ruiz-Abellón, Antonio Gabaldón and Antonio Guillamón
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María Del Carmen Ruiz-Abellón: Department of Applied Mathematics and Statistics, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
Antonio Gabaldón: Department of Electrical Engineering, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
Antonio Guillamón: Department of Applied Mathematics and Statistics, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain

Energies, 2018, vol. 11, issue 8, 1-22

Abstract: 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: Electricity Markets; load forecasting models; regression trees; ensemble methods; direct market consumers (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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