Workspace Energy Control Using Occupancy Prediction
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Solution Overview
Problem
Workplaces and recreational facilities face inefficiencies in energy consumption due to unused spaces being heated, cooled, and lit, despite reduced occupancy levels from hybrid working models and staggered scheduling, leading to unnecessary electrical energy consumption.
Innovation Solution
An energy optimization engine that allocates workspaces based on an energy conservation strategy, automatically controls environmental conditions, and adjusts equipment usage to minimize energy consumption by grouping users and optimizing thermal and electronic equipment usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If workspaces are maintained with standard environmental conditions (heating, cooling, lighting) to ensure readiness for use, then user comfort and workspace availability are improved, but energy consumption increases unnecessarily when spaces are unused
Solution Approach 1:
The patent implements dynamic adjustment of environmental conditions based on real-time occupancy detection. When a workspace is detected as unoccupied, the system automatically modifies thermal and lighting conditions to reduce energy consumption. When occupancy is detected, the system restores standard environmental conditions, ensuring user comfort is maintained only when necessary.
Solution Approach 2:
The system employs automated occupancy detection through sensors that trigger environmental adjustments without manual intervention. The workspace environment self-regulates based on detected occupancy status, eliminating the need for manual control while optimizing energy usage.
2Productivity
If multiple users are assigned to dispersed workspaces to provide individual dedicated spaces, then user productivity and comfort are improved, but the number of active thermal zones and equipment usage increases, leading to higher energy consumption
Solution Approach 1:
The patent implements a hot-desking system where multiple users share common workspaces rather than having dedicated individual spaces. This consolidation reduces the total number of active thermal zones and equipment instances required, as workspaces are occupied by different users at different times rather than being continuously active for multiple users simultaneously.
Solution Approach 2:
The system dynamically changes workspace assignment parameters based on real-time occupancy and usage patterns. By flexibly reassigning workspaces to different users throughout the day, the system maintains individual user productivity while reducing the overall footprint of actively used spaces, thereby lowering energy consumption.
Data Source
AI summary
Some examples relate to optimizing energy efficiency associated with workspaces in a building. In one specific example, a system can execute a trained machine-learning model to generate a predicted activity pattern associated with a workspace in a building, the predicted activity pattern being a forecast of workspace activity associated with the workspace over a future time window. The system can, based on the predicted activity pattern, generate at least one control signal for at least one control system associated with the workspace. And the system can transmit the at least one control signal to the at least one control system, the at least one control system being configured to receive the at least one control signal and responsively adjust at least one environmental condition associated with the workspace from a first setting to a second setting.