Predicting peak energy demand for an office building using artificial intelligence (ai) approaches

Yuxuan Chen, Patrick Phelan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Due to the technological advancement in smart buildings and the smart grid, there is increasing desire of managing energy demand in buildings to achieve energy efficiency. In this context, building energy prediction has become an essential approach for measuring building energy performance, assessing energy system efficiency, and developing energy management strategies. In this study, two artificial intelligence techniques (i.e., ANN = artificial neural networks and SVR = support vector regression) are examined and used to predict the peak energy demand to estimate the energy usage for an office building on a university campus based on meteorological and historical energy data. Two-year energy and meteorological data are used, with one year for training and the following year for testing. To investigate the seasonal load trend and the prediction capabilities of the two approaches, two experiments are conducted relying on different scales of training data. In total, 10 prediction models are built, with 8 models implemented on seasonal training datasets and 2 models employed using year-round training data. It is observed that a backpropagation neural network (BPNN) performs better than SVR when dealing with more data, leading to stable generalization and low prediction error. When dealing with less data, it is found that there is no dominance of one approach over another.

Original languageEnglish (US)
Title of host publicationProceedings of the ASME 2021 Power Conference, POWER 2021
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791885109
DOIs
StatePublished - 2021
EventASME 2021 Power Conference, POWER 2021 - Virtual, Online
Duration: Jul 20 2021Jul 22 2021

Publication series

NameAmerican Society of Mechanical Engineers, Power Division (Publication) POWER
Volume2021-July

Conference

ConferenceASME 2021 Power Conference, POWER 2021
CityVirtual, Online
Period7/20/217/22/21

Keywords

  • Artificial Intelligence
  • Energy Demand
  • Office Building.
  • Peak Energy Demand

ASJC Scopus subject areas

  • Mechanical Engineering
  • Energy Engineering and Power Technology

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