AI-based total energy management system for high energy efficiency of logistics center
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Solution Overview
Problem
Cold chain logistics centers face inefficiencies in energy management due to reliance on manual controls and lack of systematic approaches, leading to high energy consumption and operational challenges.
Innovation Solution
An AI-based total energy management system that collects real-time data to switch between different management modes for HVAC, lighting, and defrosting systems, using an EMS server and AI server to optimize energy usage based on temperature, humidity, and power consumption data, including AC/DC power supply modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by stationary object
If manual control methods are used for HVAC and lighting systems, then operational simplicity is maintained, but energy consumption increases
Solution Approach 1:
The system enables self-service through automated AI-based decision making. The AI server autonomously analyzes real-time data from sensors and controls HVAC and lighting systems without requiring manual intervention from managers, thereby reducing energy consumption while maintaining operational simplicity.
Solution Approach 2:
The system implements continuous feedback loops where sensors monitor temperature, humidity, and energy consumption, the AI server processes this data to determine optimal settings, and the control systems adjust operations accordingly. This closed-loop feedback mechanism optimizes energy efficiency dynamically.
2Productivity
If real-time AI-based management is implemented, then energy efficiency is improved, but system complexity increases
Solution Approach 1:
The AI server performs multiple functions including data collection from various sensors, real-time analysis of temperature and humidity patterns, prediction of energy consumption, and control of both HVAC and lighting systems. This multi-functionality consolidates complexity into a single intelligent platform.
Solution Approach 2:
The AI server acts as an intermediary between the physical environment (sensors and controlled systems) and the decision-making process. It translates raw sensor data into actionable control commands, managing system complexity through intelligent mediation.
3Ease of operation
If manual monitoring of temperature and operations is performed, then system simplicity is maintained, but monitoring burden on managers increases
Solution Approach 1:
The system performs self-monitoring through distributed sensors that continuously track temperature, humidity, and energy consumption. The AI server automatically analyzes this data and generates control decisions, eliminating the need for manual monitoring by managers while improving ease of operation.
Solution Approach 2:
The system replaces manual mechanical monitoring with automated electronic sensing and intelligent software analysis. Sensors substitute for human observers, and AI algorithms substitute for human decision-making, significantly reducing the monitoring burden.
4Use of energy by stationary object
If HVAC systems operate continuously at fixed settings, then operational simplicity is maintained, but energy consumption increases
Solution Approach 1:
The HVAC system transitions from static fixed settings to dynamic adaptive control. The AI server continuously adjusts temperature and humidity setpoints based on real-time environmental conditions, cargo requirements, and energy consumption patterns, optimizing HVAC operation flexibly.
Solution Approach 2:
The system dynamically changes operational parameters including temperature setpoints, humidity levels, and equipment runtime based on AI analysis of real-time data. This parameter optimization reduces energy consumption while maintaining cargo safety through adaptive control.
Data Source
AI summary
A total energy management system for high energy efficiency of a logistics center includes: an energy management system (EMS) server for collecting real-time data associated with the logistics center, wherein the EMS server processes the real-time data and generates at least one of real-time temperature data, real-time humidity data, renewable energy data, and electric power data of the logistics center; and an artificial intelligence (AI) server for outputting at least one of temperature mapping data, defrosting time determination data, and an energy operation guide in the logistics center based on at least one of the real-time temperature data, the real-time humidity data, the renewable energy data, and the electric power data received from the EMS server, wherein the EMS server switches between a plurality of management modes having different operating conditions for the logistics center based on at least one of the temperature mapping data, the defrosting time determination data, and the energy operation guide received from the AI server.