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Energies 2018, 11(8), 2038; https://doi.org/10.3390/en11082038

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

1
Department of Applied Mathematics and Statistics, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
2
Department of Electrical Engineering, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
*
Author to whom correspondence should be addressed.
Received: 6 July 2018 / Revised: 1 August 2018 / Accepted: 1 August 2018 / Published: 6 August 2018
(This article belongs to the Special Issue Short-Term Load Forecasting by Artificial Intelligent Technologies)
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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. View Full-Text
Keywords: Electricity Markets; load forecasting models; regression trees; ensemble methods; direct market consumers Electricity Markets; load forecasting models; regression trees; ensemble methods; direct market consumers
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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MDPI and ACS Style

Ruiz-Abellón, M.C.; Gabaldón, A.; Guillamón, A. Load Forecasting for a Campus University Using Ensemble Methods Based on Regression Trees. Energies 2018, 11, 2038.

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