Deep learning versus traditional machine learning methods for aggregated energy demand prediction

N.G. Paterakis, Elena Mocanu, M. Gibescu, B. Stappers, W. van Alst

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

20 Citations (Scopus)

Abstract

In this paper the more advanced, in comparison with traditional machine learning approaches, deep learning methods are explored with the purpose of accurately predicting the aggregated energy consumption. Despite the fact that a wide range of machine learning methods have been applied to probabilistic energy prediction, the deep learning ones certainly represent the state-of-the-art artificial intelligence methods with remarkable success in a spectrum of practical applications. In particular, the use of Multi Layer Perceptrons, recently enhanced with deep learning capabilities, is proposed. Furthermore, its performance is compared with the most commonly used machine learning methods, such as Support Vector Machines, Gaussian Processes, Regression Trees, Ensemble Boosting and Linear Regression. The analysis of the day-ahead energy prediction demonstrates that different prediction methods present significantly different levels of accuracy in the case of a challenging dataset that comprises an interesting mix of consumers, wind and solar generation. The results show that Multi Layer Perceptrons outperform all the eight methods used as a benchmark in this study.
Original languageEnglish
Title of host publication2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)
PublisherIEEE
ISBN (Electronic)978-1-5386-1953-7
DOIs
Publication statusPublished - 18 Jan 2018
Externally publishedYes
EventIEEE PES Innovative Smart Grid Technologies Conference Europe 2017 - Torino, Italy
Duration: 26 Sep 201729 Sep 2017
Conference number: 7

Conference

ConferenceIEEE PES Innovative Smart Grid Technologies Conference Europe 2017
Abbreviated titleISGT Europe 2017
CountryItaly
CityTorino
Period26/09/1729/09/17

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