Equipment control device, equipment control method, and computer readable medium
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Solution Overview
Problem
Existing building energy management technologies struggle to balance energy consumption reduction with stakeholder demands for comfort, air quality, sound environment, productivity, and CO2 emission constraints, failing to effectively control equipment beyond lighting and air conditioning.
Innovation Solution
An equipment control device that simulates candidate settings for building equipment, calculates satisfaction and constraint levels, optimizes settings based on these levels, and applies optimized settings to equipment, integrating multiple stakeholder demands and operational constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If energy consumption is reduced more than necessary, then energy saving increases, but the comfort of the residents deteriorates
Solution Approach 1:
The system changes operational parameters of building equipment (lighting, air conditioning, etc.) based on simulated scenarios that balance energy consumption with comfort requirements. Multiple candidate settings are evaluated with different parameter combinations to find optimal solutions that satisfy both energy saving and comfort constraints.
Solution Approach 2:
The system uses simulation results as feedback to iteratively optimize equipment settings. By calculating satisfaction levels and constraint levels from simulation data, the system adjusts candidate settings to improve both energy efficiency and comfort, creating a closed-loop optimization process.
2Loss of energy
If equipment settings are optimized for energy saving, then energy consumption decreases, but stakeholder demands for comfort, air quality, and other constraints may not be met
Solution Approach 1:
The simulation unit evaluates multiple objectives simultaneously (energy consumption, thermal comfort, illuminance, air quality, sound environment, productivity, CO2 emissions) using a unified multi-objective optimization framework. This allows the system to balance competing stakeholder demands across different functional requirements.
Solution Approach 2:
The system adds the dimension of simulation-based prediction to the optimization process, evaluating candidate settings across multiple performance dimensions before selection. This transforms the problem from simple energy optimization to multi-dimensional performance optimization including comfort, quality, and environmental constraints.
3Device complexity
If existing technologies control only lighting and air conditioning equipment, then implementation is simple, but energy consumption reduction opportunities from other equipment are missed
Solution Approach 1:
The equipment control device is designed to universally manage multiple types of building equipment (lighting, air conditioning, ventilation, elevators, etc.) through a common optimization platform. This multi-functional approach enables comprehensive energy management across diverse equipment while maintaining a unified control architecture.
Solution Approach 2:
The system segments the building equipment portfolio into controllable units, allowing individual optimization of each equipment type while coordinating them through overall energy management. This enables granular control of energy consumption across different equipment categories.
4Reliability
If building equipment is controlled to meet strict operational constraints, then compliance with standards is improved, but flexibility in meeting stakeholder demands decreases
Solution Approach 1:
The system dynamically adjusts equipment settings based on real-time conditions and stakeholder preferences while maintaining compliance with operational constraints. The optimization process continuously adapts candidate settings to balance constraint satisfaction with stakeholder demand fulfillment, creating a dynamic rather than static control approach.
Solution Approach 2:
The system changes operational parameters within constraint boundaries to optimize stakeholder satisfaction. By adjusting parameters like temperature, illuminance, and equipment operation schedules within allowable ranges, the system maintains compliance while adapting to different stakeholder requirements and environmental conditions.
Data Source
AI summary
A result acquisition unit (130) obtains, for each candidate setting, a simulation result of a case where a candidate setting is applied to the one or more units of equipment. A satisfaction level calculation unit (140) calculates, for each candidate setting, a satisfaction level on a basis of the simulation result. A constraint level calculation unit (150) calculates, for each candidate setting, a constraint level on a basis of the simulation result. A candidate optimization unit (160) judges superiority/inferiority of a plurality of candidate settings on a basis of the satisfaction level of each candidate setting and the constraint level of each candidate setting, and optimizes the plurality of candidate settings on a basis of a judgment result, thereby generating a plurality of new candidate settings. A selection unit selects an applicable setting from the plurality of new candidate settings. A control unit applies the applicable setting to equipment.