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 to retrieve and process raw timeseries data from relational databases, 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 processes data to generate weather-normalized and benchmarked energy metrics, allowing for efficient storage and retrieval of optimized timeseries data for energy management.
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, then data storage is simple and straightforward, but data retrieval and processing time are significantly delayed
Solution Approach 1:
The system performs preliminary processing of raw timeseries data by generating pre-computed views and aggregations at the time of data ingestion rather than waiting for query time. The data processing service creates normalized views, calculated fields, and aggregated summaries in advance, so that when applications request data, the results are already prepared and can be returned immediately without significant processing delay.
2Measurement precision
If data processing is performed in response to a request for timeseries data, then data freshness is maintained, but data presentation is significantly delayed
Solution Approach 1:
The system pre-computes data views, aggregations, and derived metrics at data ingestion time rather than at query time. This allows the system to maintain data freshness by processing new data immediately when it arrives, while also preparing results in advance so that subsequent queries can be answered quickly without re-processing the entire dataset.
Solution Approach 2:
The system creates different pre-computed views optimized for different query patterns and application needs. Instead of processing all data the same way, it generates specialized views with different levels of aggregation, different calculated fields, and different normalization approaches tailored to specific application requirements, allowing each view to be optimized for its particular use case.
3Adaptability or versatility
If applications are responsible for retrieving and generating views of timeseries data, then application flexibility is maintained, but system complexity increases
Solution Approach 1:
The system introduces a data processing service as an intermediary layer between the database and applications. This service handles the complexity of data retrieval, view generation, and data transformation, while applications can simply request data through standardized interfaces. The intermediary service maintains multiple pre-computed views and handles the complexity of data processing, allowing applications to remain flexible without bearing the burden of implementation complexity.
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.