Improvement of the Performance Balance between Thermal Comfort and Energy Use for a Building Space in the Mid-Spring Season

Abstract

In thermal controls in buildings, recent statistical and data-driven approaches to optimize supply air conditions have been examined in association with several types of building spaces and patterns of energy consumption. However, many strategies may have some problems where high-control precision may increase energy use, or low energy use in systems may decrease indoor thermal quality. This study investigates a neural network algorithm with an adaptive model on how to control the supply air conditions reflecting learned data. During the process, the adaptive model complements the signals from the network to independently maintain the comfort level within setting ranges. Although the proposed model effectively optimizes energy consumption and supply air conditions, it achieves quite improved comfort levels about 14% more efficient than comparison models. Consequently, it is confirmed that a network and learning algorithm equipped with an adaptive controller properly responds to users’ comfort levels and system’s energy consumption in a single space. The improved performance in space levels can be significant in places where many spaces are systematically connected, and in places which require a high consistency of indoor thermal comfort. Another advantage of the proposed model is that it properly reduces an increase in energy consumption despite an intensive strategy is utilized to improve thermal comfort.

Structural Optimization and Application Research of Alkali-Activated Slag Ceramsite Compound Insulation Block Based on Finite Element Method

“…(2) The temperatures on both sides of the block are constant. (3) The main material of block and the filling material are closely connected. The material performances do not vary with the thermal environment.…”

“…Instead of limiting the demand for buildings to shelter from wind, rain, and warmth, people emphasize the need for a comfortable, energy-saving, and environmentally friendly living environment [1,2]. However, improvements in indoor thermal comfort are often accompanied by increases in building energy consumption and environmental pollution [3,4]. It is reported that buildings account for 40% of the total global energy consumption, and this proportion is still increasing [5,6].…”

Building Space Thermal Control Model Responding to Sharp Changes in Outdoor Temperature

“…Various issues of combining were found in the process of correcting and supplementing existing experimental and practical thermal models rather than creating a new one. To solve the mathematical and statistical issues, the ANN has suggested convenient ways for retrofitting by analyzing as many hidden correlations [11,12]. The ANN has shown the advantage of appropriately responding to inputs that are associated with various factors that are difficult to specify, such as regional and climatic characteristics, which must be considered in architectural models.…”

Abatement of the Increases in Cooling Energy Use during a Period of Intense Heat by a Network-Based Adaptive Controller

“…Despite of controversial for their predictive performance and handling of variables, the PMV and the PPD have been used in several fields due to their advantage of being able to quantitatively compute users' comfort levels. In this area, the refined FIS membership matrix has been frequently conducted to define effective control strategies for improving thermal comfort levels, and the ANN algorithm has been adopted in the correlation analysis to improve the performance of specific thermal sensation votes based on traditional control or tuning rules [19,20]. Several possible hidden correlations between building geometries, thermal systems, and occupant characteristics were adopted to define better quantitative results in the studies that dealt with extreme weather conditions and unexpected climate change.…”