The document analyzes historical consumption and weather data from two buildings heated by district heating in Ferrara, Italy. It examines daily and seasonal profiles of consumption, temperature, and heating system usage. It also tests a heating system suggestion service that predicts optimal heating turn-on/off times using weather forecasts, evaluating its performance on past data from the two buildings. The analysis found inverse relationships between consumption and temperature as expected, and that the suggestion service accurately predicted turn-on/off times for most out-of-average temperature days.
This document describes the SUNSHINE project which aims to develop a platform and suite of services for improving energy efficiency in urban areas. It outlines 6 services the platform will provide: 1) a meter data management service, 2) a remote system management service, 3) a building efficiency pre-certification service, 4) a citizen-oriented alert and communication service, 5) a 3D web portal and apps, and 6) security and privacy enforcement. Each service has associated tasks and deliverables described in the SUNSHINE project documentation.
This document describes the components and interfaces of a lamp control service. It consists of multiple levels: a client interface, central server services for grouping and scheduling, and pilot-specific services for interacting with control devices. The document focuses on the interfaces between the client, central servers, and pilot services. It defines commands that can be sent between these levels for different use cases of long-term scheduling, exceptional scheduling, and instant commands. Interface specifics are provided for translating commands to the protocols understood by the PowerOne and Reverberi pilot systems.
This document discusses security measures for integrating new smart services with existing service infrastructures in the SUNSHINE pilot project. It covers authentication using identification and validation of identity, authorization using role-based access control and attribute-based access control, and the system architecture for enforcing security policies. The architecture uses a reverse proxy, service proxy, XACML repository and engine, and components like OpenDJ LDAP and WSO2 Identity Server and Enterprise Service Bus.
S.2.f Specifications for Data Ingestion via Green ButtonSUNSHINEProject
This document provides specifications for implementing a Green Button web service to expose utility consumption data from pilot projects to the SUNSHINE platform. It describes the Green Button initiative, actors involved, and how it relates to the SUNSHINE scenario. It also provides instructions on installing and configuring the required software components, including the DataCustodian application, database, and web services to ingest and export meter reading data using XML formats. Security is implemented using the OAuth protocol to authorize third parties to access consumption data on behalf of pilot projects.
The document describes the meter data management service of the SUNSHINE project. The service collects data from various sensors in pilot buildings, including energy meter consumption data, indoor temperature readings, and weather data. It ingests the data through different interfaces, stores it in a standardized format in a Sensor Observation Service database, and provides access to the data through web services. Key components of the service include an FTP ingestion process, a GreenButton ingestion interface, a weather data ingestion process, and sensor data access interfaces.
The document provides specifications for web and mobile interfaces to access daily heating and cooling system usage suggestions for buildings and shelters. It outlines two main use cases - accessing daily suggestions, which would display the requested comfort profile, estimated internal temperatures, and suggested on/off times; and viewing or editing comfort profiles and system thresholds. Mockups are provided for interfaces that would display this information on a web client, mobile app, or via email. It also specifies the backend RESTful and SOS services that would power the interfaces, allowing getting, putting, and modifying data like comfort profiles and thresholds.
Servizio Gestione Flussi Dati Energetici EdificiSUNSHINEProject
The document describes a Sensor Data Management service that was proposed as part of the Sunshine project. The service ingests energy consumption, indoor temperature, and weather data from various sources and stores it in a centralized database. It then visualizes the correlations between consumption and weather variables to help energy managers analyze building energy behavior. The service was demonstrated by analyzing consumption data from two pilot buildings in Ferrara, Italy.
This document describes the SUNSHINE project which aims to develop a platform and suite of services for improving energy efficiency in urban areas. It outlines 6 services the platform will provide: 1) a meter data management service, 2) a remote system management service, 3) a building efficiency pre-certification service, 4) a citizen-oriented alert and communication service, 5) a 3D web portal and apps, and 6) security and privacy enforcement. Each service has associated tasks and deliverables described in the SUNSHINE project documentation.
This document describes the components and interfaces of a lamp control service. It consists of multiple levels: a client interface, central server services for grouping and scheduling, and pilot-specific services for interacting with control devices. The document focuses on the interfaces between the client, central servers, and pilot services. It defines commands that can be sent between these levels for different use cases of long-term scheduling, exceptional scheduling, and instant commands. Interface specifics are provided for translating commands to the protocols understood by the PowerOne and Reverberi pilot systems.
This document discusses security measures for integrating new smart services with existing service infrastructures in the SUNSHINE pilot project. It covers authentication using identification and validation of identity, authorization using role-based access control and attribute-based access control, and the system architecture for enforcing security policies. The architecture uses a reverse proxy, service proxy, XACML repository and engine, and components like OpenDJ LDAP and WSO2 Identity Server and Enterprise Service Bus.
S.2.f Specifications for Data Ingestion via Green ButtonSUNSHINEProject
This document provides specifications for implementing a Green Button web service to expose utility consumption data from pilot projects to the SUNSHINE platform. It describes the Green Button initiative, actors involved, and how it relates to the SUNSHINE scenario. It also provides instructions on installing and configuring the required software components, including the DataCustodian application, database, and web services to ingest and export meter reading data using XML formats. Security is implemented using the OAuth protocol to authorize third parties to access consumption data on behalf of pilot projects.
The document describes the meter data management service of the SUNSHINE project. The service collects data from various sensors in pilot buildings, including energy meter consumption data, indoor temperature readings, and weather data. It ingests the data through different interfaces, stores it in a standardized format in a Sensor Observation Service database, and provides access to the data through web services. Key components of the service include an FTP ingestion process, a GreenButton ingestion interface, a weather data ingestion process, and sensor data access interfaces.
The document provides specifications for web and mobile interfaces to access daily heating and cooling system usage suggestions for buildings and shelters. It outlines two main use cases - accessing daily suggestions, which would display the requested comfort profile, estimated internal temperatures, and suggested on/off times; and viewing or editing comfort profiles and system thresholds. Mockups are provided for interfaces that would display this information on a web client, mobile app, or via email. It also specifies the backend RESTful and SOS services that would power the interfaces, allowing getting, putting, and modifying data like comfort profiles and thresholds.
Servizio Gestione Flussi Dati Energetici EdificiSUNSHINEProject
The document describes a Sensor Data Management service that was proposed as part of the Sunshine project. The service ingests energy consumption, indoor temperature, and weather data from various sources and stores it in a centralized database. It then visualizes the correlations between consumption and weather variables to help energy managers analyze building energy behavior. The service was demonstrated by analyzing consumption data from two pilot buildings in Ferrara, Italy.
S.2.e Specifications for Data Ingestion via Sunshine FTPSUNSHINEProject
This document provides specifications for ingesting pilot consumption and sensor data via FTP into Sunshine's data repository. It describes the required meter mapping file format and consumption/sensor data file formats, including naming conventions and metadata to include. Meter mappings relate devices to buildings and define reading types and frequencies. Data files group timestamped readings and costs into CSVs with specified naming and formatting.
S.2.g Meter and Sensor Data Management ServiceSUNSHINEProject
During the development of the SUNSHINE platform for building energy management, a Sensor Data Management Service was implemented to collect, store, and analyze various sensor data from pilot buildings. The service extends beyond traditional meter data to also manage indoor temperature, weather, and other sensor readings. It ingests data from different sources via FTP, Green Button, and WFS protocols and stores it uniformly in a PostgreSQL/PostGIS database following the Sensor Observation Service standard. Analysis of energy use data from two schools in Ferrara, Italy identified weekly and seasonal consumption patterns related to outdoor temperature variations.
SUNSHINE Project: Romain Nouvel, Jean Marie BahuSUNSHINEProject
The document discusses the development of the CityGML ADE Energy, which extends the CityGML open standard for exchanging 3D urban data to include energy-related objects and attributes. It was developed through a participatory process with international experts over multiple phases. The Energy ADE has a modular structure including cores for construction/materials, occupancy, and energy/systems. It aims to enable data exchange and interoperability between urban energy stakeholders and tools. Initial applications show its potential to support various urban energy analyses. Collaboration will continue to harmonize concepts with CityGML 3.0 and INSPIRE developments.
SUNSHINE Project: Francesco Pignatelli, Maria Teresa BorzacchielloSUNSHINEProject
The document discusses the European Union Location Framework (EULF) project and its goals of promoting effective location-enabled e-government across Europe. It outlines EULF deliverables including recommendations, guidelines and pilot studies in sectors like transport, marine and energy. One such feasibility study examines how location data can support energy efficiency policies by analyzing data requirements and identifying opportunities for geospatial technologies to increase effectiveness of stakeholders and policymaking. The document concludes by discussing plans to implement an open data platform and workshop to help advance the energy pilot.
The document discusses collaboration between OGC, bSI, and ISO TC 59 to jointly develop geospatial and BIM standards. It focuses initially on infrastructure design where civil engineering uses both geospatial and BIM concepts. It proposes a joint conceptual model between bSI and OGC to develop standards that are more easily integrated and avoid information loss when moving between models. The collaboration is important for solving business problems by reducing time spent converting data between standards.
S.1.b Building Energy Pre Certification ServiceSUNSHINEProject
In 2012, the Municipality of Ferrara, Italy signed up to the Covenant of Mayors to reduce greenhouse gas emissions by 25% by 2020 through increased energy efficiency and renewable energy. One action is the SUNSHINE project, which aims to implement an automatic building energy pre-certification service using open geodata to estimate building energy performance at large scale. The municipal departments were involved in modeling geodata on building energy properties. A mobile app was also created to check the accuracy of building properties like age, use, heights during on-site audits.
S.1.a Data Model for Energy Map Data CollectionSUNSHINEProject
This document presents a data model for collecting building energy performance data as part of the Sunshine project. It includes classes, attributes, data types and domains for representing buildings and their characteristics. The model is based on INSPIRE specifications for buildings data. It defines two groups for basic building data needed for the project and an extended model with additional optional attributes. Mandatory attributes include a building ID, construction period, height, elevation, main use and energy performance validation data.
This document describes a research project that aims to develop a web-based system to automatically calculate and visualize large-scale energy performance maps of residential buildings in a city. The system would use existing data sources, an extended version of the TABULA/EPISCOPE project for calculating building energy parameters, and CityGML and WebGL standards for data storage and visualization. Preliminary results are presented for a service that assesses building energy performance at scale and visualizes the results in an intuitive 3D interface to help citizens, governments, and organizations analyze building energy efficiency across a city.
The document discusses security and privacy strategies for the SUNSHINE project. It outlines plans to update documentation to address new attack models and standards. The project uses an identity management system with role, attribute, and consent-based access control based on XACML/SAML. Next steps include further testing the implementation, analyzing user data to define access rules, and developing new standards through partnerships.
This document describes the SUNSHINE project which aims to develop a platform and suite of services for improving energy efficiency in urban areas. It outlines 6 services the platform will provide: 1) a meter data management service, 2) a remote system management service, 3) a building efficiency pre-certification service, 4) a citizen-oriented alert and communication service, 5) a 3D web portal and apps, and 6) training materials. Each service has associated tasks and deliverables described in the SUNSHINE project. The platform will integrate data from buildings, sensors, meters and other systems to provide analytics, recommendations and alerts to citizens and operators.
The document discusses a mobile application called Map4Data that was created to conduct field surveys for building typology and energy efficiency data collection. The app allows surveyors to view buildings on a map, select individual buildings, and fill out property forms with attributes like construction period, height, use, and energy refurbishment level. It sources data from existing topographic and cadastral databases but aims to verify and supplement incomplete or inaccurate records through field surveys. The document provides instructions on using the app and an example of survey progress with over 1,000 buildings surveyed over 27 hours.
This document describes a model for calculating the energy performance of buildings based on European laws and standards. It outlines the key factors considered in the model such as heating energy needs, domestic hot water needs, and how building characteristics are determined. Validation of the model requires consideration of factors like actual building age and refurbishment. The model allows users to input location data and building characteristics to simulate energy performance.
This document discusses data models for energy efficiency projects. It explains that data models are like recipes that specify ingredients and how to present them. It provides examples of ingredients like buildings data, cadastral data, and energy resources that are relevant to energy efficiency. The document also discusses standards like INSPIRE that can be used to structure this data and notes open data sources that can provide some of these ingredients. However, it acknowledges that current data coverage is incomplete and that harmonizing various data sources is needed to fully understand energy usage and effectively target efficiency projects.
This document discusses the data needed to create an energy map as part of the SUNSHINE project. Mandatory data includes 2D building footprints and attributes like a unique ID, construction period, height, elevation, and main use. Validation data from energy certifications or real consumption is also required at the building level. Optional attributes like the nature, number of units and floors, and refurbishment level can increase accuracy. The next steps will describe providing this data to the SUNSHINE platform to generate the first energy map and more details are in the technical module.
This document introduces Scenario 1 of the SUNSHINE project, which aims to estimate the heating energy needs of residential buildings at a large scale using available spatial data and building properties. It describes using software modules to model and import open geographic data, verify data completeness and correctness, compute energy needs, and visualize/simulate results. The goal is to provide tools to facilitate energy planning and monitoring. The scenario also details estimating typical building heat transmission coefficients and energy consumption based on factors like construction period, U-values, degree days, building shape, and more.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
S.2.e Specifications for Data Ingestion via Sunshine FTPSUNSHINEProject
This document provides specifications for ingesting pilot consumption and sensor data via FTP into Sunshine's data repository. It describes the required meter mapping file format and consumption/sensor data file formats, including naming conventions and metadata to include. Meter mappings relate devices to buildings and define reading types and frequencies. Data files group timestamped readings and costs into CSVs with specified naming and formatting.
S.2.g Meter and Sensor Data Management ServiceSUNSHINEProject
During the development of the SUNSHINE platform for building energy management, a Sensor Data Management Service was implemented to collect, store, and analyze various sensor data from pilot buildings. The service extends beyond traditional meter data to also manage indoor temperature, weather, and other sensor readings. It ingests data from different sources via FTP, Green Button, and WFS protocols and stores it uniformly in a PostgreSQL/PostGIS database following the Sensor Observation Service standard. Analysis of energy use data from two schools in Ferrara, Italy identified weekly and seasonal consumption patterns related to outdoor temperature variations.
SUNSHINE Project: Romain Nouvel, Jean Marie BahuSUNSHINEProject
The document discusses the development of the CityGML ADE Energy, which extends the CityGML open standard for exchanging 3D urban data to include energy-related objects and attributes. It was developed through a participatory process with international experts over multiple phases. The Energy ADE has a modular structure including cores for construction/materials, occupancy, and energy/systems. It aims to enable data exchange and interoperability between urban energy stakeholders and tools. Initial applications show its potential to support various urban energy analyses. Collaboration will continue to harmonize concepts with CityGML 3.0 and INSPIRE developments.
SUNSHINE Project: Francesco Pignatelli, Maria Teresa BorzacchielloSUNSHINEProject
The document discusses the European Union Location Framework (EULF) project and its goals of promoting effective location-enabled e-government across Europe. It outlines EULF deliverables including recommendations, guidelines and pilot studies in sectors like transport, marine and energy. One such feasibility study examines how location data can support energy efficiency policies by analyzing data requirements and identifying opportunities for geospatial technologies to increase effectiveness of stakeholders and policymaking. The document concludes by discussing plans to implement an open data platform and workshop to help advance the energy pilot.
The document discusses collaboration between OGC, bSI, and ISO TC 59 to jointly develop geospatial and BIM standards. It focuses initially on infrastructure design where civil engineering uses both geospatial and BIM concepts. It proposes a joint conceptual model between bSI and OGC to develop standards that are more easily integrated and avoid information loss when moving between models. The collaboration is important for solving business problems by reducing time spent converting data between standards.
S.1.b Building Energy Pre Certification ServiceSUNSHINEProject
In 2012, the Municipality of Ferrara, Italy signed up to the Covenant of Mayors to reduce greenhouse gas emissions by 25% by 2020 through increased energy efficiency and renewable energy. One action is the SUNSHINE project, which aims to implement an automatic building energy pre-certification service using open geodata to estimate building energy performance at large scale. The municipal departments were involved in modeling geodata on building energy properties. A mobile app was also created to check the accuracy of building properties like age, use, heights during on-site audits.
S.1.a Data Model for Energy Map Data CollectionSUNSHINEProject
This document presents a data model for collecting building energy performance data as part of the Sunshine project. It includes classes, attributes, data types and domains for representing buildings and their characteristics. The model is based on INSPIRE specifications for buildings data. It defines two groups for basic building data needed for the project and an extended model with additional optional attributes. Mandatory attributes include a building ID, construction period, height, elevation, main use and energy performance validation data.
This document describes a research project that aims to develop a web-based system to automatically calculate and visualize large-scale energy performance maps of residential buildings in a city. The system would use existing data sources, an extended version of the TABULA/EPISCOPE project for calculating building energy parameters, and CityGML and WebGL standards for data storage and visualization. Preliminary results are presented for a service that assesses building energy performance at scale and visualizes the results in an intuitive 3D interface to help citizens, governments, and organizations analyze building energy efficiency across a city.
The document discusses security and privacy strategies for the SUNSHINE project. It outlines plans to update documentation to address new attack models and standards. The project uses an identity management system with role, attribute, and consent-based access control based on XACML/SAML. Next steps include further testing the implementation, analyzing user data to define access rules, and developing new standards through partnerships.
This document describes the SUNSHINE project which aims to develop a platform and suite of services for improving energy efficiency in urban areas. It outlines 6 services the platform will provide: 1) a meter data management service, 2) a remote system management service, 3) a building efficiency pre-certification service, 4) a citizen-oriented alert and communication service, 5) a 3D web portal and apps, and 6) training materials. Each service has associated tasks and deliverables described in the SUNSHINE project. The platform will integrate data from buildings, sensors, meters and other systems to provide analytics, recommendations and alerts to citizens and operators.
The document discusses a mobile application called Map4Data that was created to conduct field surveys for building typology and energy efficiency data collection. The app allows surveyors to view buildings on a map, select individual buildings, and fill out property forms with attributes like construction period, height, use, and energy refurbishment level. It sources data from existing topographic and cadastral databases but aims to verify and supplement incomplete or inaccurate records through field surveys. The document provides instructions on using the app and an example of survey progress with over 1,000 buildings surveyed over 27 hours.
This document describes a model for calculating the energy performance of buildings based on European laws and standards. It outlines the key factors considered in the model such as heating energy needs, domestic hot water needs, and how building characteristics are determined. Validation of the model requires consideration of factors like actual building age and refurbishment. The model allows users to input location data and building characteristics to simulate energy performance.
This document discusses data models for energy efficiency projects. It explains that data models are like recipes that specify ingredients and how to present them. It provides examples of ingredients like buildings data, cadastral data, and energy resources that are relevant to energy efficiency. The document also discusses standards like INSPIRE that can be used to structure this data and notes open data sources that can provide some of these ingredients. However, it acknowledges that current data coverage is incomplete and that harmonizing various data sources is needed to fully understand energy usage and effectively target efficiency projects.
This document discusses the data needed to create an energy map as part of the SUNSHINE project. Mandatory data includes 2D building footprints and attributes like a unique ID, construction period, height, elevation, and main use. Validation data from energy certifications or real consumption is also required at the building level. Optional attributes like the nature, number of units and floors, and refurbishment level can increase accuracy. The next steps will describe providing this data to the SUNSHINE platform to generate the first energy map and more details are in the technical module.
This document introduces Scenario 1 of the SUNSHINE project, which aims to estimate the heating energy needs of residential buildings at a large scale using available spatial data and building properties. It describes using software modules to model and import open geographic data, verify data completeness and correctness, compute energy needs, and visualize/simulate results. The goal is to provide tools to facilitate energy planning and monitoring. The scenario also details estimating typical building heat transmission coefficients and energy consumption based on factors like construction period, U-values, degree days, building shape, and more.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
3. 1.1.a - Daily profiles
Study performed on two buildings, both served by district heating:
• Scuole Poledrelli (see DailyProfiles_Poledrelli.pptx)
• Museo di Storia Naturale (see DailyProfiles_MuseoStoriaNaturale.pptx)
Four quantities plotted:
• measured consumption (red line)
• measured external temperature (blue line)
• required periods of comfort (unshaded surfaces)
• deduced heating system turn on time
4. 1.1.b - Daily profiles
Scuole Poledrelli:
• Heating system usually off in the weekend
• Heating turn on is anticipated on Mondays and Tuesdays
Museo di Storia Naturale
• Usually on all days
• very regular turn on/of interval
General remarks
• Temperature profile sometimes is unrealistic or incomplete
• As expected consumption trends are inversely proportional to external temperature, with a
delay due to thermal inertia.
• Scuole Poledrelli have a higher consumption, but a correct comparison should be done after
normalization with heated surface.
6. 1.2 - Seasonal profiles
Scuole Poledrelli:
• weekly pattern is clearly visible, with the consumption peaks on Mondays and Tuesdays
• this kind of plot triggers the question, is turning turn off the heating system during weekends
more efficient than just living it on?
• the question can be evaluated by measuring and comparing the surface of the "Monday
peaks" with that of the "weekend valleys"
• comparison of consumption and temperature curves show
• an inverse proportion on the long term trends
• the possible effect of thermal inertia in the progressive smoothing of the "Monday peak"
from one week to the following.
Museo di Storia Naturale:
• Initial peak is unrealistic, consumption scale is different with respect with Scuole Poledrelli
• clear weekly pattern is absent, even if a week-size signal seems to be present, especially on
the left part of the curve
• comparison of consumption and temperature curves show an inverse proportion on the long
term trends
9. Gas consumption data are measured with optical reader attached to the analogic gas meters:
• Data is gathered via radio in local concentrators that deliver them via GPRS to the pilot head-
end server.
• Reading frequency is hourly but often the reading fails and the measure is postpones to the
following hour.
• This is what causes the measurement jumps in the historical series.
We have analysed consumption data for one pilot building served by gas heating to verify the
quality of data.
Palazzina Energia/Patrimonio:
• Impact of measurement jumps is heavy, to the point that data is scarcely useful
• Gas consumption includes also hot water preparation, as can be derived from the non-null
consumption values outside of the heating season.
1.3 - Gas consumption profiles
12. The aim of this activity is to test the Heating System Suggestion service on the same two pilot
buildings in Ferrara:
- Scuole Elementari Poledrelli
- Museo di Storia Naturale
The Suggestion service normally takes in input the forecasted weather condition for the
following day.
However, in order to perform a test on a long baseline, for the test the suggestion service was
run on an historical series of past observed data during part of the last winter season.
2.1 - The Suggestion service
13. The suggestion service is designed to activate on days with out-of-the-average weather.
It has been determined that on 90% of cases the absolute value of the difference between the
average temperature of one day and the average temperature of the preceding day fall within
3°C for Ferrara. Days that fall outside this value are considered out-of the average.
The first plot shows the profiles of the following variables:
(temperatures on the left axis, temperature difference on the right axis)
• Maximum measured daily outside temperature (red line)
• Average measured daily outside temperature (green line)
• Minimum measured daily outside temperature (blue line)
• Out-of-the-average days (red dots)
The second plot shows the distribution of the absolute value of difference between temperature
averages. The tail of the distribution is highlighted and it represents the numerosity of the out-
of-the-average days.
2.2 - Service triggering
15. Distribution of absolute values of daily average temperature differences:
Distribuzione ΔT°
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0
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35
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0 1 2 3 4 5 6 7 8 9 10
distribution cumulative
Secondo la curva cumulativa nel periodo di riferimento circa il 18 % dei giorni hanno un ΔT ° > 3°
Questi vengono analizzati al fine di verificare l’attendibilità del servizio di suggestion, come segue :
16. The Simulation has then been run on a sub-portion of the span of the previous plot.
The outcome is shown in the following slide for both pilot buildings,:
• Scuole Poledrelli on the left and
• Museo di Storia Naturale on the right.
The top plots describe the heating system turn-on phase:
(hours on the left axis, temperatures on the right axis)
• Maximum measured daily outside temperature (red line)
• Minimum measured daily outside temperature (blue line)
• Suggested turn-on time (red triangles)
• Measured turn-on time (green triangles)
Bottom plots describe the heating system shutting down:
• Suggested shutting-down time (red diamonds)
• Measured shutting-down time (green diamonds)
2.3 - Suggested turn on/off times
18. It must be stressed that:
• measured turn-on / shut-down times have been deduced from consumption profiles with an
approximation of +/- 30 minutes
• suggested turn-on / shut-down times have been computed by the suggestion service using
the measured weather data for each day
• we have no way to verify if either the measured or suggested turn on/off profile succeeds in
achieving the desired internal comfort profile, because we have no data describing internal
temperature.
The aim of the test is instead to verify:
• how often the Suggestion service is triggered in a real scenario
• how different is the pattern of suggested turn on/off profiles with respect to what operators
do out of their experience (the measured profiles)
2.3.a - Aim of the test
19. The analysis of the plots reveals that:
• Suggested turn-on times are very sensitive to the daily minimum temperature (and much
less to the maximum), while shut-down times are almost insensitive.
• The relative dependence of suggested turn-on time with respect to external temperature
throughout the days is a significative feature to compare with measured one to evaluate if
the suggestion service is well tuned.
• On the contrary, it is not significative to compare the absolute values of suggested turn-on
times with corresponding measured ones, because, as already pointed out, we have no way
to evaluate the effectiveness in guaranteeing the required comfort of either of them.
• The same reasoning applies in principle to shut-down times, even if they do not show any
relative variation throughout the days.
2.3.b - Test analysis
20. The suggestion service computes also the expected internal temperature profile of the building
(estimated under the assumption of heating system always OFF). The profile is useful to
determine whether the effect of outside temperature and solar irradiation are enough to allow
a comfort level inside the building or if heating is necessary.
The two following picture apply to the two pilot building and describe:
• the estimated internal temperature (green line)
• the measured external temperature (blue line)
• the measured consumption (red line)
• suggested turn on and off times (dashed black line)
• required periods of comfort (unshaded surfaces)
2.4.a – Internal temperature profile
22. Suggestion:
Museo : Profilo Giornaliero stimato per una data “anomala”, con ΔT° > 3°
-12
-7
-2
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8
13
0
50
100
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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
ConsumptionkWh
Venerdì 16/01
Consumption Power ON T° T° Estimated
23. Remarks:
• test day (16/01/2015) was chosen because it was one of the few out-of-the-ordinary days
available in the test sample, however a more radical example should be tested.
• estimated internal temperatures do not vary a lot for the two pilot buildings.
• building thermal inertia is not considered (internal temperature of the previous day would
be needed).
• building occupancy is not considered.
In the last plot of the following slide shows a comparison between
• the estimated internal temperature for a contiguous number of days
• the measured external temperature for the same span of days
It is clearly visible how the estimated internal temperature trends have no delay with respect to
outside temperatures as instead you would expect due to thermal inertia of the building.
2.4.b – Internal temperature profile
26. Suggested actions:
• Compare weather data for Ferrara coming from Sensor DB with original data from ARPA to
verify if unrealistic temperature profiles derive from ingestion.
• Comparison between energy consumption for different buildings should be done after
normalization with total heated surface.
• Normalization with respect to degree days should also be used if different time periods are
considered.
• Correlations of consumption with irradiation and wind should be also evaluated.
Suggested test:
• keep heating on in the weekend for a couple of weeks, then turn it off in the weekends for
another couple of weeks.
• do this in two different periods of Winter, at the beginning of the heating season and at its
peak.
• perform the same test in buildings with different thermal inertia
• evaluate the seasonal consumption profile of the building to understand how it responds to
thermal inertia and different seasonal condition and ultimately evaluate when is more
efficient to keep the heating on during the weekends and when it is not.
Analysis of historical series
27. Suggested actions:
• Accuracy of Suggestion service will greatly benefit by adding the modelling of building's
thermal inertia. This is visible in the unrealistic relation between the series of external
temperatures and estimated internal temperatures that shows how the estimated internal
temperature is only reacting to external temperatures and not showing any signs of thermal
inertia.
• Test/validation will be more thorough if data on daily occupancy could be collected: daily
registries of school canteen users should be asked to the school.
Suggested test:
• a campaign of high-frequency (e.g. 1 hour) indoor temperature measurement has been
planned on 2015-2016 heating season for Scuole Elementari Poledrelli.
• two week-long campaigns: beginning of November; 3rd week of December or 2 week of
January;
• during the campaigns the heating system will be set with the turn on/off profiles provided by
the suggestion service.
• The absolute accuracy of the Suggestion service can be finally evaluated.
Suggestion service
28. www.sunshineproject.eu
SUNSHINE - Smart UrbaN ServIces for Higher eNergy Efficiency (GA no: 325161)
Credits
For more training material and courses visit http://www.sunshineproject.eu/solutions/training
or contact us directly at training@sunshineproject.eu
Source:www.unionegeometri.com
Thank you!
Luca Giovannini
Sinergis Srl
luca.giovannini@sinergis.it