Network based Control Methodology for Improving the Indoor Thermal Environment of Sales Shops in Traditional Markets during the Change of Seasons

Purpose: Various methods for revitalizing traditional markets have been studied in several fields such as economy, sociology, and engineering. There is a need for an advanced control model that optimizes indoor thermal conditions to improve the usability and that mitigates an increase in energy use. The aim of this research is to develop an energy effective control method without compromising the quality of indoor thermal comfort. Method: By use of a designed working hour plan, a proposed model with an adaptive process controls the amount of heating and cooling air supply. The control results after the process are input into an artificial neural network learning algorithm. Then the performance of this learning model is investigated in comparison to the control results of a conventional thermostat model. Results: The proposed model effectively maintains the consistency of thermal comfort levels by about 61%, and reduces the energy use for heating and cooling by about 3%, respectively. The performance of this model can help to improve the usability and the economy of independent shops, which can play an important role to revitalize traditional markets in urban areas.