Application architecture for WPD Sample Clauses

Application architecture for WPD. The purpose of this section is to describe Smart Energy (WPD) application architecture using building blocks from BREAL generic application architecture. The first model of this section shows general business functions, application services and data objects. The second model is an example of concrete implementation in Estonia, with added business processes, application and infrastructure components. Figure 14 - WPD Application Architecture WPD application architecture model shows five BREAL business functions and ten chosen application services, that are specialized by specific work package services: Acquisition and Recording: External structured data retrieval Copy file The first step is to receive and copy data from the hub or electricity provider. Data can be made available through the secure FTP, API or on an external storage device. Acquisition and Recording: Data storing Load file to database When all necessary files are in production environment of NSI, the next step is to load this data into relational database (RDBMS). Data Representation: Structure modeling Pseudonymize personal data Anonymization of personal information - removing personal codes from data set and adding NSI specific identifiers. Data Representation: Data encoding Geographical encoding Geocoding of the data – adding NSI specific address information field to database. Data Wrangling: Data preparation, filtering & deduplication Filter data Removal of duplicates and anomalies. If company is associated with same metering point several times, then duplicate entries are being removed. Companies without an activity code and measuring points with negative consumption are also removed. Data Wrangling: Data aggregation Initial aggregation (daily and monthly) Creating daily and monthly aggregations from measurements data. Data Wrangling: Data standardization Split data Metering data is split into different categories (dwelling, business units, building, land area). Modeling and Interpretation: Data linking and enriching Weight and distribute co-consumption Distributing consumption of companies, that are related to the same measurement point. Dividing consumption by number of companies or weighted by number of employees. Modeling and Interpretation: Statistical aggregation Final aggregation (by sector) Grouping electricity consumption data of companies by XXXX codes to find the overall electricity consumption of a business sector. Shape output: Data exchange Compare with survey data Comparis...
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