Evolved Neural Networks for Building Energy Prediction

Improving buildings' energy efficiency is an essential component in the efforts for reducing the carbon footprint. The design of more accurate machine learning models for forecasting energy use in buildings can help to reach this goal since these models can be integrated as part of the management systems. A variety of machine learning algorithms have been used for different classes of building energy predictions problems. In this paper we investigate two questions related to the use of neural networks for building energy predictions: The benefits of optimized neural network configurations that include the architecture and some hyperparameters, and the impact on the performance of the amount of data available to train the networks. Our results show that combine optimization of architectures and hyperparameters can significantly improve the accuracy of the neural networks in some problems and that the availability of training data should be taken into account when deciding to apply neural networks over other machine learning methods for building energy prediction problems.