Air conditioner and method for operating the air conditioner
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Solution Overview
Problem
Conventional air conditioners lack the ability to precisely manage indoor environments based on user activities and states, leading to inefficient energy consumption and inaccurate temperature control, as they rely solely on set temperatures without considering dynamic indoor conditions.
Innovation Solution
An air conditioner system that utilizes machine learning and Internet of Things (IoT) technology to collect data from various devices, including home appliances, wearable devices, and vehicles, to predict indoor temperature changes and adjust cooling or heating operations based on user activity and biometric data, using deep learning and reinforcement learning algorithms to optimize temperature settings and energy usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the air conditioner is controlled only by set temperature, then the control is simple, but the indoor environment cannot be delicately managed according to user activities and state
Solution Approach 1:
The air conditioner system incorporates multiple sensors (temperature sensor, humidity sensor, user state detector) that continuously monitor indoor conditions and user state, feeding this information back to the controller. The controller adjusts operating parameters based on this feedback, enabling delicate management of the indoor environment while maintaining automated operation.
Solution Approach 2:
The system automatically detects user activities and state through integrated sensors and detectors, then autonomously adjusts temperature and humidity settings without requiring manual user input. This self-service capability allows the system to delicately manage the indoor environment based on actual conditions while keeping the control interface simple.
2Use of energy by moving object
If the compressor is controlled by simple set temperature command, then the energy consumption is reduced, but the indoor temperature changes cannot be predicted and managed in advance
Solution Approach 1:
The system predicts future indoor temperature changes based on detected user activities (e.g., cooking, exercise) and pre-adjusts the compressor operation before the temperature actually changes. This preliminary action allows the system to maintain comfortable temperatures while optimizing energy consumption by avoiding unnecessary compressor cycling.
Solution Approach 2:
The controller acts as an intermediary between user activities and compressor operation, using detected activity information to predict temperature changes and mediate compressor control decisions. This intermediary function enables optimized energy consumption while maintaining temperature prediction and management capabilities.
3Ease of operation
If outdoor environmental information is used to control the air conditioner, then remote control is enabled, but the accuracy in determining indoor space state is low
Solution Approach 1:
The system merges outdoor environmental information from remote sources with indoor sensor data (temperature, humidity, user state) to comprehensively determine the required indoor environment. This combination maintains remote control capability while significantly improving the accuracy of indoor space state determination through multi-source data integration.
Solution Approach 2:
The control system is designed to accept and process multiple types of information sources (outdoor weather data, indoor sensor readings, user activity detection) through a universal control interface. This multi-functionality enables both remote control operation and accurate indoor state determination by appropriately weighting and integrating diverse data sources.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides a more accurate and personalized indoor environment by anticipating temperature changes, reducing unnecessary energy consumption, and improving temperature prediction accuracy over time through continuous data collection and feedback.
Implementation Method 1
A heating medium circulates through a path determined in the air conditioner and the circulating heating medium emits or absorbs thermal energy. In the air conditioner, the heating medium transmits the thermal energy and performs the heating by emitting the thermal energy and performs the cooling by absorbing the thermal energy.
Implementation Method 2
The heating medium circulates through a path determined in the air conditioner and the circulating heating medium emits or absorbs thermal energy
Data Source
AI summary
An air conditioner which is controlled to operate by executing an artificial intelligence (AI) algorithm and/or a machine learning algorithm in a 5G environment connected for the Internet of Things and an operating method of an air conditioner are provided. The air conditioner includes a communicator, a command input, an operation manipulator, and a controller. The communicator collects weather information and operation information of at least one connected devices. The command input receives a manipulation command of a user. An operation manipulator adjusts at least one of whether to operate the air conditioner for cooling/heating, a temperature, a wind volume, or a wind direction of the air conditioner. The controller derives a first customized value based on a manipulation command input by the command input and information collected by the communicator and controls the operation manipulator in accordance with the first customized value.