Energy Consumption Analysis for Coupling Air Conditioners and Cold Storage Showcase Equipment in a Convenience Store

Abstract

The energy use intensity (EUI) of convenience stores was substantially higher than that of office buildings and hotels, due to a compact footprint but a high density of equipment yielded a higher EUI. As a result, it is critical to assess and maintain the state of the convenience store in order to obtain a lower EUI and reduce energy consumption. This study utilizes a convenience store to evaluate energy consumption and perform a CFD simulation to see how the environment influences by cold storage showcase (CSS) equipment. On the basis of field testing and on-site web-based monitoring data, a survey of baseline information through data collecting and energy benchmarking data has been provided and extensively examined. According to energy monitoring, the convenience store’s highest electricity use is 23,055 kWh in June, and the lowest power consumption is 15,216 kWh in February. The CFD simulation results revealed that the temperature near the CSS can be 3–5 °C lower than in other regions. The temperature nearby return air will be lower as a result of the low-temperature air impacts from CSS. The AC sensor detects that the environment has met the indoor requirements and performs the load reduction operation. After adjusting the AC temperature, it is discovered that the unit is unable to attain the appropriate temperature. Energy consumption can be reduced, resulting in more energy-efficient AC and CSS operations. Furthermore, the CSS’s cold air effect might be taken advantage of by raising the AC-2 temperature set point to generate energy savings.

Advanced Data Systems for Energy Consumption Optimization and Air Quality Control in Smart Public Buildings Using a Versatile Open Source Approach

“…The first step in managing electric load consumption is to know the electricity consumption profile versus time. Electrical power consumption generally varies due to both user behaviors [3] and ambient conditions [4], which leads to the demand of continuous control. According to data analytics, data mining techniques are used for energy data classification, clustering and prediction [5].…”