Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4911553 | Building and Environment | 2017 | 16 Pages |
Abstract
This paper proposes intelligent controls of mass and temperature simultaneously for heating air supply. The Fuzzy Inference System (FIS) and Artificial Neural Network (ANN) algorithms are utilized to develop six control models, and the models are tested to evaluate both control and energy efficiency during the winter season in five climate zones (from climate zone 2 through 6; i.e., Houston, Dallas, Raleigh, Chicago, and Detroit, respectively). Results include the energy consumption, control errors, and control signals in comparison to the baseline on/off control, which confirms the fact that the ANN simultaneous controls of mass and temperature is more effective than the other controllers for control accuracy and energy savings by 71.3% and 0.3%, respectively. The effectiveness of the ANN controller can contribute to maintaining room temperature accompanying the reduction of energy consumption, which is directly related to improve human comfort and reduce environmental impacts in various climate zones.
Related Topics
Physical Sciences and Engineering
Energy
Renewable Energy, Sustainability and the Environment
Authors
Jonghoon Ahn, Dae Hun Chung, Soolyeon Cho,