Research Article Open Access

Applications of Machine Learning in Building Energy Prediction and Savings

Priyan Rai1, Dr. Nabil Nassif1, Kevin Eaton1 and Alexander Rodrigues1
  • 1 University of Cincinnati, United States

Abstract

In a constantly advancing world with growing needs, buildings play in important role in the daily functioning of the society. Developing more and more advanced techniques to optimize the working of these buildings is highly important for a constant growth. Modern computational techniques have opened doors to create advanced models that can utilize efficient techniques to produce highly accurate results. This paper introduces a model that utilizes machine learning algorithms to predict energy consumption in buildings. Energy data were used from two actual and two simulated buildings to fine tune the models. The model is also compared to a baseline regression model as well as a model based on Artificial Neural Network. The results show that the proposed model performs much better than the other two compared models. The proposed model can be used for many intelligent applications such as measurement and savings verification, optimization, building-energy assessment and fault detection and diagnosis. The models were tested to predict the savings calculations for a simulated building and the results proved the proposed model to be the closest predictor to actual savings.

Energy Research Journal
Volume 10 No. 1, 2019, 1-10

DOI: https://doi.org/10.3844/erjsp.2019.1.10

Submitted On: 3 March 2019 Published On: 15 March 2019

How to Cite: Rai, P., Nassif, D. N., Eaton, K. & Rodrigues, A. (2019). Applications of Machine Learning in Building Energy Prediction and Savings. Energy Research Journal, 10(1), 1-10. https://doi.org/10.3844/erjsp.2019.1.10

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Keywords

  • Building Energy Consumption
  • Bootstrap Aggregation
  • Machine Learning Model
  • Artificial Neural Networks (ANN)