Journal article

A review and analysis of regression and machine learning models on commercial building electricity load forecasting

11 Feb 2017
Description

Electricity load forecasting is an important tool which can be utilized to enable effective control of commercial building electricity loads. Accurate forecasts of commercial building electricity loads can bring significant environmental and economic benefits by reducing electricity use and peak demand and the corresponding GHG emissions. This paper presents a review of different electricity load forecasting models with a particular focus on regression models, discussing different applications, most commonly used regression variables and methods to improve the performance and accuracy of the models. A comparison between the models is then presented for forecasting day ahead hourly electricity loads using real building and Campus data obtained from the Kensington Campus and Tyree Energy Technologies Building (TETB) at the University of New South Wales (UNSW). The results reveal that Artificial Neural Networks with Bayesian Regulation Backpropagation have the best overall root mean squared and mean absolute percentage error performance and almost all the models performed better predicting the overall Campus load than the single building load. The models were also tested on forecasting daily peak electricity demand. For each model, the obtained error for daily peak demand forecasts was higher than the average day ahead hourly forecasts. The regression models which were the main focus of the study performed fairly well in comparison to other more advanced machine learning models.

Highlights:

  • Review of regression models for commercial building electricity load forecasting.
  • Short term electricity load forecast analysis for 24 ahead hourly and daily peak loads.
  • Electricity load forecast analysis on a university Campus and single building level.
  • Implementation and comparison of regression and other four machine learning models.

Keywords: Short term load forecasting for commercial buildings; Review of regression models; Machine learning; Neural Networks; Support Vector Regression; Regression Trees

Publication Details
Identifiers: 
DOI: 
10.1016/j.rser.2017.02.023
ISSN: 
1104–1122
Volume: 
73
Pagination: 
1104-1122
Access Rights Type: 
Paid subscription required
Language: 
English
License Type: 
All Rights Reserved
Peer Reviewed: 
Yes
Published year only: 
2017
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