Building Energy Analytics for Equipment-Level Consumption Tracking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for facility analytics face challenges in effectively gathering, interpreting, and utilizing data from various sources such as energy consumption, equipment performance, and maintenance information, leading to inefficiencies and missed opportunities for cost savings and proactive maintenance.
Innovation Solution
A computer-based method and system that integrates with building automation systems to collect and analyze data from electrical energy-consuming equipment, utilizing machine learning and real-time analytics to determine energy consumption, identify inefficiencies, and automate reporting and maintenance processes, while providing customizable dashboards and alerts for optimized facility management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data from multiple facility sources is gathered and integrated, then the completeness and value of facility analytics is improved, but the complexity of data gathering and interpretation increases
Solution Approach 1:
The system segments facility data into distinct categories (energy consumption, equipment performance, maintenance information) and processes each through dedicated data collection modules that interface with specific building automation system components, making the complex data gathering process manageable and systematic
Solution Approach 2:
The facility analytics system acts as an intermediary layer between building automation system data sources and users, automatically collecting, integrating, and interpreting data from multiple sources through centralized processing algorithms that reduce the complexity of data interpretation for end users
2Reliability
If real-time analytics and machine learning are implemented, then predictive maintenance and cost optimization are improved, but the computational resources and system complexity increase
Solution Approach 1:
The system performs preliminary data processing and pattern recognition through machine learning algorithms that continuously analyze historical and real-time data, pre-identifying potential equipment failures and optimization opportunities before they manifest as actual problems, thereby improving reliability through advance preparation
Solution Approach 2:
The system implements feedback loops where machine learning models continuously receive real-time data from facility operations, adjust their predictions and recommendations, and provide actionable insights back to facility managers, creating a self-improving system that enhances reliability while managing complexity through iterative learning
3Productivity
If comprehensive facility data is collected and analyzed, then operational efficiency and cost savings are improved, but the time and resources required for data processing increase
Solution Approach 1:
The system implements continuous automated data collection and analysis processes that operate without interruption, continuously monitoring facility operations and generating insights in real-time, thereby improving operational efficiency while eliminating the need for manual, time-consuming data processing intervals
Data Source
AI summary
The present disclosure advantageously provides devices, systems, and methods for facility analytics. A computer-based method for determining electrical energy consumption for a building includes receiving static data for a plurality of electrical energy-consuming equipment (EECE) associated with the building, receiving dynamic data for each EECE, determining an electrical energy consumption value for each EECE over a predetermined time period, and determining a building electrical energy consumption value over the predetermined time period. The static data includes one or more performance attributes for each EECE, and the dynamic data includes measured performance data for each EECE over the predetermined time period. The electrical energy consumption value for each EECE is determined based on the performance attributes for each EECE and the measured performance data for each EECE. The building electrical energy consumption value is determined based on the electrical energy consumption value for each EECE.