Publications and deliverables
Deliverable 1.1
Title: | System specification |
Due date: | 2016-03-31 |
Executive summary:
The aim of this document is to define a system specification of an advanced data analytics framework for energy efficiency. This includes use case description and requirements on advances data analytics based on state of the art analysis and technology survey.
State of the art analysis and technology survey in the areas of CAFM, BEMS, data analytics, and decision support systems is presented.
Use cases are identified based on stakeholder and market needs, state of the art, and literature research. System requirements are identified and described which facilitates the defined use cases. Both functional and non-functional requirements are described and should be used for the implementation within the project.
The goal of ADA-EE is to provide methods and knowledge for automatic recommendation for improvement of energy efficiency in buildings by applying data analytics. The solution offers an automatic evaluation of monitoring data, prediction of future energy needs and prescribing measures to reduce energy usage. By continuously collecting real-time monitoring data, the algorithms can automatically improve prediction accuracy and prescribe better decision options. Methods such as data mining of monitoring data, forecasting and simulations are used to create decision recommendations for optimization of building energy efficiency. The system uses the up-to-date information collected from the property and data stored in the history of the property, together with weather data, as well as semantic data about the building (such as location, function, equipment type) which are analysed to detect outliers or identify patterns and trends (descriptive analytics). Patterns are verified, correlations are detected and forecasts for the future are created by extrapolating the patterns (predictive analytics). The results of the previous steps are used to prescribe the optimal actions for improvement (prescriptive analytics). For this, first candidate actions are generated. They are then translated into a set of simulation input parameters. The parameters are used as an input into a baseline simulation, which includes automatic building model generation. The set of simulation results are the predicted potential consequences of the actions. Based on the results, a comparison is performed to recommend the actions with highest value (based on assessment criteria). The suggestions are formalized so that they can be used to generate automatic configuration files. For manual maintenance measures that should be done by maintenance personnel, manuals and human-readable instructions could be automatically generated.
The system design enables flexible and modularized development. The specification enables effective development of the system. Existing components that can be included are identified. Functional requirements such as expected inputs and outputs, data needs, etc., as well as the non-functional requirements will also be defined. The structure of the system and connectivity between modules is presented. This defines how data will be acquired, stored and accessed as well as how communication between different modules will take place. Input as well as output interfaces are specified to decouple the abstract algorithms from their environment. The data analytics framework comprises descriptive, predictive and prescriptive algorithms.