Periodic Reporting for period 1 - MODERATE (Marketable Open Data Solutions for Optimized Building-Related Energy Services)
Periodo di rendicontazione: 2022-06-01 al 2023-11-30
MODERATE project aims at increase accessibility to heterogeneous data on buildings energy performance, breaking non-interoperable data silos. To this aim, MODERATE outputs innovative methodologies and a web platform that jointly enable building owners, facility managers, energy service companies (ESCOs), utility companies, product manufacturers, and policymakers, to organize and openly share their datasets, gain insights, and make decisions while complying with privacy and confidentiality requirements.
MODERATE delivers:
• A protocol for data sharing and privacy preservation, without losing the valuable information;
• A marketplace enabling data owners to openly share their datasets profiting economically and in terms of knowledge; and
• A fully open platform for data exchange and collaboration on services, where users can manage their datasets, run custom workflows, and promote innovation.
MODERATE relies on generative Artificial Intelligence (AI), Machine Learning (ML), Distributed Ledger technologies, and the Internet of Things. On top, data-driven services supports users decision making in system management, building renovation planning and deployment of on-site renewable energy sources and energy communities. MODERATE proudly contributes to an open knowledge base on digitalization and AI in the buildings and energy domains, involving researchers, software developers, companies and EU institutions.
MODERATE delivers:
• A protocol for data sharing and privacy preservation, without losing the valuable information;
• A marketplace enabling data owners to openly share their datasets profiting economically and in terms of knowledge; and
• A fully open platform for data exchange and collaboration on services, where users can manage their datasets, run custom workflows, and promote innovation.
MODERATE relies on generative Artificial Intelligence (AI), Machine Learning (ML), Distributed Ledger technologies, and the Internet of Things. On top, data-driven services supports users decision making in system management, building renovation planning and deployment of on-site renewable energy sources and energy communities. MODERATE proudly contributes to an open knowledge base on digitalization and AI in the buildings and energy domains, involving researchers, software developers, companies and EU institutions.
Work was performed on the platform, data collection, integrity and enhancement, data quality pipelines, trust mechanisms, standard data models and interoperable data-driven services. Development followed agile and lean approach, thus progressively adjusting specifications based on user needs and feedback.
Activities focused on:
- the definition of the cloud platform architecture and a wide set of components for accessing, supporting, running and maintaining the future marketplace features, the services and users' workflows. Specifications for data integrity, data quality pipelines and trust mechanism have been drafted and a first version of public APIs have been developed along with methods for managing data transfer to the platform.
- Data collection from partners, public data sources and other projects produced a comprehensive dataset catalogue, including EPCs, energy consumption profiles, IEQ conditions, on-site renewable energy generation, economic data and maintenance costs, cadastral data and remote sensing data, from single buildings to district, cities and countries. New methodologies for data collection, as remote sensing and machine learning, have been tested in real cases and new datasets have been produced and shared on GitHub.
- Development of a data enrichment techniques for building and energy data, considering the re-identification risk on personal information.
- Data-driven services for informing target users in their decisions, in the scope of the most relevant use cases for target users.
- the co-design phase with industrial partners in charge of the demonstration, leading to factsheets describing marketplace features.
Main achievements:
- Collection of wide and various datasets on buildings and energy
- Contribution of datasets to EU Building Stock Observatory
- Synthetic datasets
- Data-driven services
Activities focused on:
- the definition of the cloud platform architecture and a wide set of components for accessing, supporting, running and maintaining the future marketplace features, the services and users' workflows. Specifications for data integrity, data quality pipelines and trust mechanism have been drafted and a first version of public APIs have been developed along with methods for managing data transfer to the platform.
- Data collection from partners, public data sources and other projects produced a comprehensive dataset catalogue, including EPCs, energy consumption profiles, IEQ conditions, on-site renewable energy generation, economic data and maintenance costs, cadastral data and remote sensing data, from single buildings to district, cities and countries. New methodologies for data collection, as remote sensing and machine learning, have been tested in real cases and new datasets have been produced and shared on GitHub.
- Development of a data enrichment techniques for building and energy data, considering the re-identification risk on personal information.
- Data-driven services for informing target users in their decisions, in the scope of the most relevant use cases for target users.
- the co-design phase with industrial partners in charge of the demonstration, leading to factsheets describing marketplace features.
Main achievements:
- Collection of wide and various datasets on buildings and energy
- Contribution of datasets to EU Building Stock Observatory
- Synthetic datasets
- Data-driven services
At this stage, the following set of results are under development:
- An open online platform serving high-quality datasets concerning buildings’ energy, indoor environmental quality, and life cycle.
- Replicable and extensible code base thanks to free and open-source commercial-friendly licenses.
- High-quality, universally accessible open datasets.
- Techniques for data anonymization, synthetization, and enhancement.
- Open-source data-driven tools based on high-quality datasets that building and infrastructure stakeholders can access, directly use or replicate to streamline their work and offered services.
- Archetypal applications of Smart Readiness Indicator.
- A protocol enabling reliable and robust data flows on the platform.
- validated user workflows.
The platform will impact:
- SMEs and stakeholders interacting with our target users (ESCOs, energy/facility managers, utilities, real estate and construction companies). Providing open datasets, validated services and example workflows, they can increase trust in digital technologies and AI, supported by the open-source community growing around the project, and by advanced consulting services offered by B2B companies on the marketplace. The protocol and the underlying tools will impact SMEs which need to share their datasets after having confidential constraints removed. Also, the SME that handles personal data will reduce costs for storage and security of these data, by only storing the needed information in a trained ML model that could reproduce the key information on demand. The validation of data-driven tools will create trust and success stories to attract early adopters among SMEs.
- Research and developers communities will benefit from the availability of open data and platform features.
- The methods for data collection and enrichment will benefit policy makers, local authorities, energy agencies and other national institutions involved in collecting and reporting data at EU level.
- As the assessment of smart readiness of buildings is foreseen in the revised EPBD only for specific building classes, the archetypal applications of SRI have the potential for filling an important gap in the knowledge of the building stock by estimating certain domains of SRI at scale. Utilities will be impacted from a more reliable estimation of baselines for demand side flexibility for buildings over an area, while local authorities and policy makers will benefit from enriched insights on the building stock.
Key needs: further research is needed on generalization of data synthetization methods, on trustworthiness in generative AI and on techniques that check re-identification risks from datasets. Services need extensive validation with real users, in order to create a trustful relation with early adopters. A growing open-source community is needed, as well as cooperation with research project and with B2B companies.
Data collection on different urban areas from different countries is needed to remove biasing in AI models. A more supportive privacy regulation for research purpose would greatly benefit the project execution, i.e. a regulatory sandbox relaxing personal data sharing options. Concerning trust mechanisms, a more supportive regulation towards the adoption of smart contracts and digital wallets for public and private organizations would be beneficial to the project.
- An open online platform serving high-quality datasets concerning buildings’ energy, indoor environmental quality, and life cycle.
- Replicable and extensible code base thanks to free and open-source commercial-friendly licenses.
- High-quality, universally accessible open datasets.
- Techniques for data anonymization, synthetization, and enhancement.
- Open-source data-driven tools based on high-quality datasets that building and infrastructure stakeholders can access, directly use or replicate to streamline their work and offered services.
- Archetypal applications of Smart Readiness Indicator.
- A protocol enabling reliable and robust data flows on the platform.
- validated user workflows.
The platform will impact:
- SMEs and stakeholders interacting with our target users (ESCOs, energy/facility managers, utilities, real estate and construction companies). Providing open datasets, validated services and example workflows, they can increase trust in digital technologies and AI, supported by the open-source community growing around the project, and by advanced consulting services offered by B2B companies on the marketplace. The protocol and the underlying tools will impact SMEs which need to share their datasets after having confidential constraints removed. Also, the SME that handles personal data will reduce costs for storage and security of these data, by only storing the needed information in a trained ML model that could reproduce the key information on demand. The validation of data-driven tools will create trust and success stories to attract early adopters among SMEs.
- Research and developers communities will benefit from the availability of open data and platform features.
- The methods for data collection and enrichment will benefit policy makers, local authorities, energy agencies and other national institutions involved in collecting and reporting data at EU level.
- As the assessment of smart readiness of buildings is foreseen in the revised EPBD only for specific building classes, the archetypal applications of SRI have the potential for filling an important gap in the knowledge of the building stock by estimating certain domains of SRI at scale. Utilities will be impacted from a more reliable estimation of baselines for demand side flexibility for buildings over an area, while local authorities and policy makers will benefit from enriched insights on the building stock.
Key needs: further research is needed on generalization of data synthetization methods, on trustworthiness in generative AI and on techniques that check re-identification risks from datasets. Services need extensive validation with real users, in order to create a trustful relation with early adopters. A growing open-source community is needed, as well as cooperation with research project and with B2B companies.
Data collection on different urban areas from different countries is needed to remove biasing in AI models. A more supportive privacy regulation for research purpose would greatly benefit the project execution, i.e. a regulatory sandbox relaxing personal data sharing options. Concerning trust mechanisms, a more supportive regulation towards the adoption of smart contracts and digital wallets for public and private organizations would be beneficial to the project.