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

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 –
\nfor predicting the hourly HVAC energy consumption of a hotel in Madrid, Spain. Incorporating social parameters such as the…

• EC
  European Commission

  Award: 609154