FINESCE (Future INtErnet Smart Utility ServiCEs) Enablers

a)     Component Composition Framework DSE

http://finesce.github.io/DSE.html?id=Component_Composition_Framework_DSE

The CCF is a plugin-based framework (developed in .NET, C#) that allows you to inject your own implemented components into a composition service that manages and connects all running components dynamically.

In addition the CCF comes with an NGSI9 producer component that any other component can hook itself up against, allowing the CCF to send NGSI9 context updates without requiring the developer knowing the NGSI9 protocol.

Why to get it

A quick way to make existing components NGSI9 enabled using the already implemented NGSI9-producer component.

 

b)    Contract Information (ContractInformation2Orion **)

http://finesce.github.io/DSE.html?id=Contract_Information_(ContractInformation2Orion_**)_

ContractInformation2Orion is a REST service (developed in Java) which allows clients (e.g. Retailers) to register data about cost of energy produced from the DERs, costs of transmission system and power plants, energy costs into an instance of ORION Context Broker GE.

This component can be eventually re-used and customized to fulfill the general need of having a REST service which is connected to the ORION GE.

Why to get it

A reference implementation of a REST service which is connected to the ORION GE.

 

c)     Contract Tailor Processor (ContractManager **)

http://finesce.github.io/DSE.html?id=Contract_Tailor_Processor_(ContractManager_**)_

ContractManger is a REST service (developed in Java) which

translates the incentives included in an IncentivePlan (issued by a Retailer) into a new Contract proposal that will be saved into an ORION Context Broker GE instance,

updates an existing contract with content coming from a contract proposal "approved" by the final customer.

This component can be eventually re-used and customized to fulfill the general need of having a REST service which is connected to the ORION GE.

Why to get it

A reference implementation of a REST service which is connected to the ORION GE.

 

d)    Event Sink (EvSi) DSE

http://finesce.github.io/DSE.html?id=Event_Sink_(EvSi)_DSE

Main purpose of the DSE Event Sink is keeping a local copy of all the information forwarded to the FIWARE LAB cloud infrastructure.

Why to get it

This is because of the current demand of manufacturing companies to have all data and information on premise, as they perceive cloud storage as potential security breach. EvSi therefore allows for storing the gathered information locally as well. However, for running a full-powered application it is recommended to use FIWARE's full capability in the cloud. EvSi therefore should rather be seen as an on-premise backup and logging module.

 

e)     FINESCE API Mediator (FAM) DSE

http://finesce.github.io/DSE.html?id=FINESCE_API_Mediator_(FAM)_DSE

A consolidated approach for interacting with the various FINESCE trial infrastructures, using a simple, RESTful API.

Why to get it

It enables seamless, interoperable interaction with all the FINESCE trial infrastructures, in a transparent, homogeneous manner.

 

f)      Generation Schedule Manager DSE

http://finesce.github.io/DSE.html?id=Generation_Schedule_Manager_DSE

In work package 3 of the Finesce Project a virtual supply unit is set up. Renewable energy sources (DERs) such as wind power, solar power and biomass are virtually interconnected and used as one supply unit, as a Virtual Power Plant. A Demand-Side-Management is developed in a partnership with a smart factory as a sample consumer. This management system is used to find the optimal balance between energy generation and demand.

To be able to develop the applications and services for the management system, the data for the energy generation side is needed. The balancing between the energy generation of the VPP and the consumption by the Smart Factory is done based on the forecasted energy generation of the VPP for the day-ahead and a variation of energy profiles for the production of the factory.

Other applications like the monitoring are based on the measured data for the energy generation. For the energy generation the historical data and the actual and forecasted data are provided.

 

g)     Hybrid Cloud Data Management (HCDM) DSE

http://finesce.github.io/DSE.html?id=Hybrid_Cloud_Data_Management_(HCDM)_DSE

Hybrid Cloud Data Management is a REST service which provides to users transparent access to the Hybrid Cloud (distributed local storage or cloud storage system) infrastructure for the data management of the "Software Define Utility”, combining and integrating functionalities from Object Storage GE local instances and in FIWARE Lab; authentication through Identity Management Keyrock GE; and additional encryption functionalities.

Why to get it

A reference implementation of a REST service which is allows a secure management of data, privately (although allowing replication among different regions), and publicly (storing objects in FIWARE Lab through Object Storage GE).

 

h)    Issue Detector Processor (Cosmos2SCILAB + INP-SCILAB + SCILAB2Orion **)

http://finesce.github.io/DSE.html?id=Issue_Detector_Processor_(Cosmos2SCILAB___INP-SCILAB___SCILAB2Orion_**)_

Issue Detector Processor consists of:

Why to get it

 

i)      Metering (Metering2Orion **)

http://finesce.github.io/DSE.html?id=Metering_(Metering2Orion_**)_

Metering2Orion consists of:

Why to get it

A reference implementation of a client + a REST service by which smart meter readings can be "delivered" to an instance of ORION.

 

j)      Production Schedule Manager DSE

http://finesce.github.io/DSE.html?id=Production_Schedule_Manager_DSE

In the trial of the B2B energy ecosystem a factory and a Virtual Power Plant representing respectively the energy consumer and energy generation side are combined. The target of this setup is to balance the use of energy by the factory with the generation of energy from renewable sources. In this approach an energy-flexible factory is adapted to volatile energy supply and prices which leads to an energy driven production planning. The balancing between the energy generation and consumption is done by matching the profiles of energy generation and consumption, based on a variation of energy profiles for the factory and the production as well as the forecasted data of the VPP.

The Production Schedule Managers is designed for the energy consumption to provide the profiles which enable the applications to balance the energy generation and consumption.

 

k)    Protocol Adapter AMM (Sensor2AMI **)

http://finesce.github.io/DSE.html?id=Protocol_Adapter_AMM_(Sensor2AMI_**)_

The Protocol Adapter Sensor2AMI is an implementation of a solution for reading Landis+Gyr E350 DLMS/COSEM compatible electric energy meters and publishing application-specific power and energy profiles to Device Backend Management IDAS GE into the FIWARE architecture.
It self gathers metering data from a locally configured network of a/m energy meters, computes load and energy profiles according two independent integration periods and posts those data to an instance of IDAS GE according to ETSI M2M (SensorML 1.0) specifications.

Why to get it

Should you look for the DLMS/COSEM compliant implementation of the Automated Meter Reading (AMR) and the ETSI M2M interoperability with the Advanced Metering Infrastructures (AMI), this Sensor2AMI Protocol Adapter is the exact answer because
- You could use this DSE to interface Landis+Gyr E350 DLMS/COSEM compatible energy meters to IDAS GE, encapsulating the meter reading protocol complexity.
- You could use this DSE when the compatibility with the DLMS/COSEM and SensorML is required.

 

l)      Scene Manager DSE

http://finesce.github.io/DSE.html?id=Scene_Manager_DSE

Scene Manager DSE is a module which allows client applications (e.g. mobile apps for facility managers developed by third parties) to subscribe to customized alerts associated to the parameters of a building energy management/monitoring system. Alerts are triggered based on predefined ‘scenes’ which can be created by the client. These scenes are a combination of ranges of values for different parameters of the building system. If the conditions set in the scene are fulfilled, then an alert is triggered.

The reference implementation provided shows the application of the DSE for managing the subscription to customized alerts associated to the parameters of different systems and services integrated in the FINESCE Madrid trial, namely:

Why to get it

A reference implementation of a module for managing subscriptions to customized alerts associated to building system parameters handled through the Publish/Subscribe Context Broker GE. The current implementation is based on Context Awareness Platform, but it can be modified to use it with Orion.

 

m)  Social Events Interface (Social2Orion **)

http://finesce.github.io/DSE.html?id=Social_Events_Interface_(Social2Orion_**)_

Social2Orion is a REST service (developed in Java) which allows clients (e.g. Social Events Information Providers) to register data about social events (affecting consumption/production in a specific area such as a concert or a football match) into an instance of ORION Context Broker GE.

This component can be eventually re-used and customized to fulfill the general need of having a REST service which is connected to the ORION GE.

Why to get it

A reference implementation of a REST service which is connected to the ORION GE.

 

n)    Temporal Consistency DSE

http://finesce.github.io/DSE.html?id=Temporal_Consistency_DSE

Temporal Consistency DSE is a module which can be used by client applications (e.g. Building Energy Management Systems) for applying processing algorithms to data streams (e.g. weather forecasts, sensor measurements) that are used as inputs by those clients, in order to detect erroneous/inconsistent data in those streams, and replace them with alternative values, calculated through an algorithm based on historical data from the same data stream.

The ultimate goal of this DSE is to help ensure that data inputs in energy management systems are accurate enough for its use in different applications. An example of such applications can be the calculation of: building models, energy control set points, or energy savings achieved through energy efficiency projects.
The reference implementation provided shows the application of the DSE for processing weather forecasts collected from an internet web service, in order to use the refined data as an input to a building remote energy control system.

Why to get it

A reference implementation of a module for processing data streams handled by Big Data Analysis – Cosmos GE and Orion Context Broker GE.

 

o)     Weather Condition Interface (WeaFor2Orion **)

http://finesce.github.io/DSE.html?id=Weather_Condition_Interface_(WeaFor2Orion_**)_

WeaFor2Orion consists of:

Why to get it

A reference implementation of a TIMER client which retrieves weather conditions and predictions from an external provider.
A reference implementation of a REST service which is connected to the ORION GE.