Elsevier

Energy and Buildings

Volume 147, 15 July 2017, Pages 77-89
Energy and Buildings

Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption

https://doi.org/10.1016/j.enbuild.2017.04.038Get rights and content
Under a Creative Commons license
open access

Highlights

  • Developed machine learning models for HVAC electricity consumption prediction.

  • Compared the performance of feed-forward back-propagation artificial neural network (ANN) with random forest (RF).

  • The ANN model performed marginally better than the RF model.

  • RF model can be used as a variable selection tool.

Abstract

Energy prediction models are used in buildings as a performance evaluation engine in advanced control and optimisation, and in making informed decisions by facility managers and utilities for enhanced energy efficiency. Simplified and data-driven models are often the preferred option where pertinent information for detailed simulation are not available and where fast responses are required. We compared the performance of the widely-used feed-forward back-propagation artificial neural network (ANN) with random forest (RF), an ensemble-based method gaining popularity in prediction – for predicting the hourly HVAC energy consumption of a hotel in Madrid, Spain. Incorporating social parameters such as the numbers of guests marginally increased prediction accuracy in both cases. Overall, ANN performed marginally better than RF with root-mean-square error (RMSE) of 4.97 and 6.10 respectively. However, the ease of tuning and modelling with categorical variables offers ensemble-based algorithms an advantage for dealing with multi-dimensional complex data, typical in buildings. RF performs internal cross-validation (i.e. using out-of-bag samples) and only has a few tuning parameters. Both models have comparable predictive power and nearly equally applicable in building energy applications.

Keywords

HVAC systems
Artificial neural networks
Random forest
Decision trees
Ensemble algorithms
Energy efficiency
Data mining

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