Energy Management Decision Support System for Building Optimization
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Solution Overview
Problem
Current energy management systems for buildings lack comprehensive decision support tools to effectively identify and address systemic operational and cost issues, making it difficult for staff to optimize equipment performance and make informed investment decisions.
Innovation Solution
An energy management decision support system that utilizes energy usage data from smart meters and weather data to analyze and visualize energy usage variances, providing recommendations for improving building performance through data-driven insights and simulations to optimize operations and equipment upgrades.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If comprehensive energy management data collection and analysis systems are implemented, then decision support capability is improved, but system complexity and cost increase
Solution Approach 1:
The system segments energy management information into distinct categories (energy consumption data, equipment operational data, weather data, cost data) and processes each category through specialized modules. This segmentation allows comprehensive data collection while managing system complexity through modular architecture, where each module handles specific data types and analysis tasks independently.
Solution Approach 2:
The system introduces an intermediary decision support platform that sits between raw data collection and management decisions. This intermediary layer aggregates, analyzes, and presents information in actionable formats, reducing the complexity burden on both data collection infrastructure and end-users while maintaining comprehensive information availability.
2Ease of operation
If detailed energy usage analysis and visualization tools are provided, then operational staff ability to identify issues is improved, but information processing requirements and computational resources increase
Solution Approach 1:
The system implements self-service analytical capabilities that automatically process energy usage data, identify patterns, and generate visualizations without requiring intensive manual computational resources. The system autonomously performs data aggregation, anomaly detection, and trend analysis, reducing the computational burden while enhancing operational staff's ability to identify issues through pre-processed, actionable insights.
Solution Approach 2:
The system creates simplified copies and representations of complex energy usage patterns through visualizations and summary metrics. Instead of processing and displaying all raw data, the system generates condensed visual representations that capture essential information, reducing computational requirements while improving ease of operation for issue identification.
3Reliability
If the system provides comprehensive decision support including capital investment analysis, then investment decision quality is improved, but system development and implementation cost increase
Solution Approach 1:
The system is designed with multi-functionality to handle diverse decision-making needs within a single unified platform. It provides operational monitoring, energy analysis, equipment management, and capital investment analysis through integrated modules that share common data infrastructure and analytical engines. This universality improves investment decision quality by providing comprehensive support while controlling development costs through shared system components rather than separate specialized systems.
Data Source
AI summary
Disclosed herein is an energy management decision support system and methods for asset managers of buildings and facilities that can utilize energy usage data captured from meters, such as smart meters, and weather data to provide a systems-based cost reduction and optimization solution for end users. Building system components may be highly inter-dependent and changes to one system element can have substantial effects (positive and negative) upon other system elements. As described in further detail herein, system and method embodiments according to the present disclosure may apply predefined criteria to such building energy usage data to identify energy usage variances, and may graphically present to a user the identified energy usage variances. As a result, facility administrators are provided with more easily interpretable energy usage information. Such information may be applied by the administrators for adjusting operations, upgrading building equipment, or retrofitting building equipment to improve building efficiency.