Next Article in Journal
Perspectives on Resource Recovery from Bio-Based Production Processes: From Concept to Implementation
Next Article in Special Issue
How to Generate Economic and Sustainability Reports from Big Data? Qualifications of Process Industry
Previous Article in Journal
Characterizing Gene and Protein Crosstalks in Subjects at Risk of Developing Alzheimer’s Disease: A New Computational Approach
Previous Article in Special Issue
Data Visualization and Visualization-Based Fault Detection for Chemical Processes
Article Menu
Issue 3 (September) cover image

Export Article

Open AccessArticle
Processes 2017, 5(3), 46;

A Long-Short Term Memory Recurrent Neural Network Based Reinforcement Learning Controller for Office Heating Ventilation and Air Conditioning Systems

Chemical Engineering Department, University of Alberta, Edmonton, AB T6G 2R3, Canada
Author to whom correspondence should be addressed.
Received: 13 June 2017 / Revised: 10 August 2017 / Accepted: 10 August 2017 / Published: 18 August 2017
(This article belongs to the Collection Process Data Analytics)
PDF [1647 KB, uploaded 18 August 2017]


Energy optimization in buildings by controlling the Heating Ventilation and Air Conditioning (HVAC) system is being researched extensively. In this paper, a model-free actor-critic Reinforcement Learning (RL) controller is designed using a variant of artificial recurrent neural networks called Long-Short-Term Memory (LSTM) networks. Optimization of thermal comfort alongside energy consumption is the goal in tuning this RL controller. The test platform, our office space, is designed using SketchUp. Using OpenStudio, the HVAC system is installed in the office. The control schemes (ideal thermal comfort, a traditional control and the RL control) are implemented in MATLAB. Using the Building Control Virtual Test Bed (BCVTB), the control of the thermostat schedule during each sample time is implemented for the office in EnergyPlus alongside local weather data. Results from training and validation indicate that the RL controller improves thermal comfort by an average of 15% and energy efficiency by an average of 2.5% as compared to other strategies mentioned. View Full-Text
Keywords: HVAC; reinforcement learning; artificial neural networks HVAC; reinforcement learning; artificial neural networks

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Wang, Y.; Velswamy, K.; Huang, B. A Long-Short Term Memory Recurrent Neural Network Based Reinforcement Learning Controller for Office Heating Ventilation and Air Conditioning Systems. Processes 2017, 5, 46.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Processes EISSN 2227-9717 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top