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

• Published in: Energy and buildings 2017-07, Vol.147, p.77-89
• Main Authors: Ahmad, Muhammad Waseem, Mourshed, Monjur, Rezgui, Yacine
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Back propagation networks; Buildings; Computer simulation; Data mining; Decision trees; Energy consumption; Energy efficiency; Energy management; Ensemble algorithms; High resolution; HVAC; HVAC systems; Mathematical models; Neural networks; Power efficiency; Prediction models; Random forest; Root-mean-square errors; Tuning; Utilities
• ISSN: 0378-7788; 1872-6178
• DOI: 10.1016/j.enbuild.2017.04.038

•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. 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.