Machine Learning Resource Allocation for Building Occupancy Prediction
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Solution Overview
Problem
Building management systems face challenges in optimizing energy and resource usage due to fluctuating occupancy levels resulting from remote work, leading to unnecessary power consumption and resource wastage.
Innovation Solution
A computer-implemented method using machine learning models to analyze real-time traffic data and dynamically configure physical resources such as HVAC, lighting, and workstations based on predicted occupancy, optimizing resource allocation and reducing consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If buildings use fixed resource allocation based on maximum capacity, then resource availability is ensured, but power consumption increases and resource wastage occurs
Solution Approach 1:
The system dynamically adjusts resource allocation based on real-time occupancy data from traffic analysis and sensor inputs. HVAC systems, lighting, and other building services are continuously adapted to match actual occupancy levels rather than operating at fixed maximum capacity, resolving the contradiction between ensuring resource availability and reducing energy waste.
Solution Approach 2:
The system implements closed-loop feedback by continuously monitoring occupancy through traffic data and sensor networks, comparing actual usage against allocated resources, and automatically adjusting resource distribution. This feedback mechanism ensures resources are available when needed while eliminating wasteful consumption during low-occupancy periods.
2Adaptability or versatility
If buildings allocate resources for maximum occupancy, then all users are accommodated, but unnecessary resource wastage occurs during low occupancy
Solution Approach 1:
Resource allocation is made dynamic and adaptive through machine learning models that predict occupancy based on traffic patterns, calendar data, and historical information. The system automatically scales resource distribution up or down based on predicted and actual occupancy, maintaining adaptability to accommodate all users when present while preventing wastage during low-occupancy periods.
Solution Approach 2:
The system performs preliminary occupancy prediction using machine learning models before actual occupancy occurs, allowing proactive resource allocation adjustments. This advance planning enables the building to prepare appropriate resource levels based on predicted attendance, avoiding both over-provisioning and under-provisioning of resources.
3Measurement precision
If machine learning models are trained continuously, then prediction accuracy improves, but computational resource usage increases
Solution Approach 1:
The system implements periodic batch training of machine learning models at scheduled intervals rather than continuous real-time training. This periodic approach allows the models to be retrained with accumulated data to improve prediction accuracy while avoiding the excessive computational overhead of continuous training, thus resolving the contradiction between accuracy and resource usage.
Solution Approach 2:
The machine learning system uses historical occupancy data and traffic patterns that are naturally generated by building operations to train and improve its own prediction models. This self-service approach allows the system to enhance its accuracy using data it already collects during normal operation, minimizing additional computational resource requirements.
Data Source
AI summary
Various systems, computer-implemented methods, and computer program products are disclosed that use improved machine learning models and techniques for allocating physical resources such as electricity and HVAC systems. These techniques may use data, such as image data, text data, location data, or other similar types of data, indicative of a movement of objects associated with a time period. A machine learning model may extract a first set of features from the data and may determine a physical resource allocation based on the first set of features and a reference dataset. A machine learning model may determine a dynamic configuration of one or more physical resources associated with a physical building space based on the physical resource allocation. These techniques may dynamically configure usage of the one or more physical resources associated with the physical building space based on the physical resource allocation.