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

By Muhammad Waseem Ahmad, Monjur M. Mourshed, Yacine Rezgui. 2017

926 citations recorded by the source corpus.

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.

Subfields: Building and Construction, Electrical and Electronic Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/310a37f3628cdc8343215417efc0be75e6b9c968

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Citations in the corpus

Forecasting: theory and practice (2020, background): “ systems with a support vector machine. Predicting energy demand on a building/household level from smart meter data is an important research topic not only for energy savings. In the building space, Ahmad et al. (2017), Touzani et al. (2018), and Wang et al. (2018b) predict building energy consumpt”
A review of the-state-of-the-art in data-driven approaches for building energy prediction (2020, background): “[53] concluded that previous hour’s electricity consumption was more important than meteorological”