Data science has promising applications in business, and this includes MEP engineering. Buildings use many systems for functions such as space heating, air conditioning, water heating, electrical power distribution, ventilation and fire protection. All equipment and components that make up these systems are potential data sources when equipped with sensors. The data also has promising applications in research and development: it can be used to develop construction technologies that improve performance.
The value of data science lies in processing large volumes of information and extracting useful insights. Because humans cannot process information at the speed of computers, large amounts of raw data are of little use on their own. However, when information is processed with appropriate algorithms, it becomes useful for business decisions. Data science can be complemented with graphs and other visuals to make information even easier to understand.
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In MEP engineering, a promising application of data science is the creation of a “digital twin” of an existing building. A digital twin can be considered a more advanced version of a BIM model. Typically, a BIM model is finalized with “as-built” information when a project is completed. However, a digital twin constantly updates itself by collecting measurements from the building. This provides a constant stream of data, which can be analyzed to better manage the building.
Using data to plan building upgrades
Data collected from a building can be used to predict the effects of modifications such as energy retrofits. This way, the building owner can simulate several possible projects in a virtual model and observe their impact before making an investment decision.
In the absence of data, a building modernization must be planned with outdated documents and visual inspections. Due to the complexity of a building, important information can be lost even when the inspection is carried out by professionals. Once a building modification project begins, a lack of information can result in change orders and unplanned costs.
During a building upgrade, there are often multiple upgrade options for the same building system, and many of them are mutually exclusive. Consider the following examples:
- Heating systems can be designed to use a mixture of electricity and combustion, or electricity alone. The most economical option may vary depending on local electricity prices and the availability of natural gas and other fuels.
- HVAC systems can use different heat transfer fluids to provide or remove heat within a building. Direct expansion systems use air ducts, hydronic systems use water piping, and VRF systems use refrigerant flow. The best option may vary depending on the conditions of each project.
With so many variables to analyze, finding the best upgrades for a building is technically challenging. However, the use of data science in MEP engineering allows for a much faster decision. Comparing many designs with spreadsheets and conventional CAD software can take a lot of time, which is not always available.
Data science can also be used to analyze behaviors that are invisible to humans. For example, a promising application of data science is energy disaggregation: decomposing the electricity consumption of energy meters to estimate the individual consumption of each device. Power disaggregation allows for virtual sub-metering, using only one physically powered power meter. This information can then be analyzed, identifying the most promising opportunities to save electricity.
Data Science During the Construction Design Process
MEP engineers can apply data science even when a building doesn't yet exist. In these cases, the construction model uses only design specifications instead of measured data. However, the same principle applies: data science allows you to compare many options in a fraction of the time required with spreadsheet calculations. Simulations are more complex when there is no measured data from an existing building, as the model must be entirely physics-based. However, simulation is a powerful design tool for both planned and existing buildings.
Data science is also a powerful tool for troubleshooting when a building has performance issues. Measurements can be processed to find hidden interactions between problems, enabling faster and more effective resolution. Data can be used to avoid time-consuming inspections and consultants can focus on analyzing information and making better decisions.
