Building energy management system with energy analytics
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Solution Overview
Problem
Building management systems (BMS) face delays in data presentation due to the need for raw timeseries data to be retrieved and processed in response to requests, which can hinder real-time monitoring and control of building energy management.
Innovation Solution
A building energy management system that includes a data collector, analytics service, and timeseries database, which generates and stores weather-normalized data timeseries, allowing for efficient retrieval and analysis of energy usage metrics, including energy usage intensity and load ratios, to support informed decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If raw timeseries data is stored in a relational database without significant organization or processing at the time of data collection, then data storage simplicity is improved, but data presentation speed deteriorates due to processing delays at query time
Solution Approach 1:
The system performs preliminary processing of raw timeseries data by generating pre-computed views and aggregations at the time of data collection rather than at query time. The data processor creates organized data structures including time-based aggregations (daily, weekly, monthly summaries) and relationship joins between tables, so that when queries are executed, the data is already prepared and can be presented immediately without significant processing delays.
2Adaptability or versatility
If applications retrieve and process raw timeseries data in response to requests, then data processing flexibility is improved, but real-time monitoring capability deteriorates
Solution Approach 1:
The system pre-computes and stores aggregated data views at multiple time granularities (daily summaries, weekly summaries, monthly summaries) and pre-joins related tables during data collection. This allows applications to retrieve pre-processed data for real-time monitoring while maintaining the flexibility to request different time ranges and aggregations as needed, without sacrificing speed for adaptability.
Solution Approach 2:
The system adds a time aggregation dimension by creating separate data structures for different time granularities (hourly, daily, weekly, monthly views) in addition to the raw data. This allows the system to maintain raw data flexibility while providing pre-aggregated data for fast real-time monitoring at various time scales, effectively adding a temporal dimension to the data organization.
3Measurement precision
If weather effects are removed from energy consumption data through regression modeling, then energy usage metric accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system introduces a weather normalization service as an intermediary component that handles the complex regression modeling and weather effect removal. This service takes raw energy consumption data and weather data as inputs, performs the sophisticated statistical analysis to normalize energy usage for weather conditions, and returns the normalized metrics. By isolating this complex processing in a dedicated service, the overall system maintains modularity and the complexity is managed in a specialized component rather than distributed throughout the entire system.
Data Source
AI summary
A building energy management system includes building equipment, a data collector, an analytics service, a timeseries database, and an energy management application. The building equipment monitor and control one or more variables in the building energy management system and provide data samples of the one or more variables. The data collector collects the data samples from the building equipment and generates a data timeseries including a plurality of the data samples. The analytics service performs one or more analytics using the data timeseries and generates a results timeseries including a plurality of result samples indicating results of the analytics. The timeseries database stores the data timeseries and the results timeseries. The energy management application retrieves the data timeseries and the results timeseries from the timeseries database in response to a request for timeseries data associated with the one or more variables.