Energy Forecasting in Buildings Using Deep Neural Networks

Abstract

Conventional physics-based building energy models (BEMs) consider all of the building characteristics in order to accurately simulate their energy usage, requiring an extensive, complex, and costly process, particularly for existing buildings. The purpose of this work is to present a methodology for predicting the energy consumption of buildings using deep neural networks (NNs). Three machine learning algorithms including a linear regression model, a multi-layer perceptron (MLP) NN, and a convolutional NN (CNN) model are proposed to solve an energy consumption regression problem using outside dry bulb temperature as the only input. To assess these methods, a building in Melbourne, FL is considered and modeled in EnergyPlus. Ten years of data were used as inputs to the EnergyPlus model, and the energy consumption was calculated accordingly. The input to the machine learning algorithm (average daily dry bulb temperature) and the output (daily total energy consumption) are used for training. Cross-validation was performed on the trained model using actual weather data measured on-site at the building location. The results showed that all three proposed machine learning algorithms were trained successfully and were able to solve the regression problem with high accuracy. However, the CNN model provided the best results. This work also investigates different data filtering techniques that provide the best positive correlation between inputs and outputs. The presented framework provides a readily simple model that allows accurate prediction of outputs when supplied with new inputs and can be used by a wide range of end users.