Network-based energy supply optimal system in the condition where both heating and cooling are required simultaneously in a swing season. (2nd January 2018)

• Record Type: Journal Article
• Authors: Ahn, Jonghoon; Chung, Dae Hun; Cho, Soolyeon
• Is Part Of: Intelligent buildings international. Volume 10:Number 1(2018)
• Page Start: 42
• Page End: 57
• Publication Date: 2018-01-02
• Subjects: Intelligent building control -- Artificial Neural Network -- human comfort and energy efficiency -- swing season; Intelligent buildings -- Periodicals; Buildings -- Environmental engineering -- Periodicals; 696
• DOI: 10.1080/17508975.2017.1328657
• Languages: English
• ISSNs: 1750-8975

Abstract

ABSTRACT: Recent building control models have adopted advanced algorithms to replace conventional controllers to improve the performance of plant or system levels. However, most models used to optimize fuel use or fan speed have several disadvantages to respond to, accurately and promptly, zone-scaled level. This paper proposes network-based controllers utilizing Artificial Neural Network (ANN) in space cooling and heating through the simultaneous control of the amount of supply air and its temperature. Two controllers are developed to evaluate the optimum of supply air conditions for a swing season that requires both moderate heating and cooling and they are compared to conventional thermostat on/off model by using the total control errors and energy consumption for operating damper and resistance coil. The result describes the advantage of the ANN simultaneous control that control errors reduce by 61.5% and energy consumption decreases by 3.5% in comparison with on/off controller. The ANN controller effectively optimizes the supply air conditions to reduce control errors and energy consumption, as they relate to human comfort and energy savings in a swing season.