Occupancy-Based Building Energy Demand Prediction Control
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Solution Overview
Problem
Existing technologies lack accuracy in predicting electrical energy demands in buildings by accounting for the flow of people, leading to inefficiencies in energy management.
Innovation Solution
A control apparatus that acquires information on the number of people in a building, energy consumption, and those heading to the building, using cameras and meters, to estimate future energy demands through a controller and predictive algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional demand prediction methods are used without considering people flow, then the prediction system is simple, but the prediction accuracy deteriorates
Solution Approach 1:
The patent merges people flow information (from cameras or entry/exit counters) with electrical energy consumption data into a unified demand prediction model. This integration allows the system to correlate occupancy patterns with energy usage, significantly improving prediction accuracy by capturing the relationship between human activity and energy demand that traditional methods miss.
Solution Approach 2:
The control apparatus acts as an intermediary that collects and processes multiple data streams (energy consumption data and people flow information) from different sources. It synthesizes these inputs to generate accurate demand predictions, mediating between raw data collection and the final prediction output without requiring direct integration of complex sensor networks.
2Measurement precision
If multiple data sources (cameras, energy meters) are integrated, then prediction accuracy improves, but information processing complexity increases
Solution Approach 1:
The control apparatus is designed as a multi-functional system that can handle various types of input data (electrical energy consumption data from smart meters and people flow information from cameras or counters). It processes these diverse data streams through unified algorithms, making the system versatile in handling different data sources without requiring separate processing pipelines for each type of input.
3Reliability
If real-time people flow monitoring is implemented, then demand forecasting accuracy improves, but system cost and complexity increase
Solution Approach 1:
Instead of implementing complex real-time video analysis systems, the patent uses simpler copying mechanisms such as entry/exit counters or camera-based presence detection that capture essential people flow information. These simplified sensors copy the necessary occupancy data without requiring full video processing systems, maintaining forecasting reliability while reducing system complexity and cost.
Data Source
AI summary
A control apparatus includes a controller configured to: acquire first information indicating the number of people in a target building during a predetermined time period, second information indicating electrical energy consumed in the target building during the predetermined time period, and/or third information indicating the number of people heading to the target building during the predetermined time period; and estimate, based on the first information, the second information, or the third information, a demand for electrical energy for the building during a future target time period.