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AN INTELLIGENT PREDICTION SYSTEM FOR THE SMART EFFICIENT USE OF RESOURCES IN CITIES

Periodic Reporting for period 2 - PREDISMART (AN INTELLIGENT PREDICTION SYSTEM FOR THE SMART EFFICIENT USE OF RESOURCES IN CITIES)

Reporting period: 2018-11-01 to 2020-04-30

Big Data Analytics and Cloud Computing technologies are widely used today by many companies in the commerce sector to analyse customer behaviour, forecasts sales and to guide business strategies.
PrediSmart is an Artificial Intelligence Platform serving the performance of Financial, Procurement, Human Resources and Operations Departments. PrediSmart supports the needs of society for a more environmentally friendly and efficient use of resources (energy, water, waste) and economic efficiency of operations (field services and maintenance).
PrediSmart is aligned with the H2020 objectives for energy and CO2 emissions reduction by saving energy and resources. Its implementation supports the Energy Efficiency Directive, and the Energy Performance of Buildings Directive.
PrediSmart is the result of Datapole’s technologies on statistical data analysis, simulation and prediction for energy, operational and resource efficiency. It is an innovative software system that integrates four Expert Modules for an efficient management of energy, services (including building maintenance and facility management), water and waste.
We now aim at enhancing the product functionalities of PrediSmart, making it a complete off the shelf Big Data Analytics solution for energy, operational and resource savings unique in the European market. Our goal is to launch PrediSmart to the European markets expanding our customer base across countries in Europe. We will implement our new set up software modules and integrate them to our existing expert modules into an off the shelf complete software platform.
Since the beginning of the project, we developed a connectors’ studio for major data suppliers : BAS, CMMS, IoT Platform, …., which allow to easily create an automated collection of data through API, flat files or direct connection to databases or data lake. This work heavily relied on a native ETL functionality and a strong data analysis, preparatory work done by our data team.
We integrated DATAPOLE’ Solutions based on latest Artificial Intelligence technologies within the same platform (machine learning, neural language processing, heuristics , ...). This implementation make it easier to design an output customized to the data process expected by each end user. To do so, we migrated our former Solutions into a Platform which is now hosted by AWS. This architecture give us flexibility, efficiency and cost control for the future of our development.
This migration completed, we developed a new interface and an automated setup. A demonstrator has been being developed, with the automated data model generation and allow to quickly deploy new pilote sites with minimum efforts.
Our commercial team attended 30 events whose 15 related to smart city and facility management which led to more than 260 contacts taken with various societies of which 160 we still have relations with.
We initiated collaboration with major Service Providers in order to co design a Solution which is dedicated to serve their Design department and Operations departments. Then, PrediSmart Platform could have been deployed over 21 Pilot Sites, gathering 300 tertiary and industrial buildings for an annual volume of 300 000 work orders.
During this collaboration, DATAPOLE techniciens teams closely collaborated with services providers teams and major CMMS editors :
- we developed dedicated interfaces for Design Departments’ experts in order to benchmark the vision of experts with the feedbacks of operations.
- we developed connections to automatically collect real time activities data and push back data processes outputs into the existing CMMS used by Operations Departments.
We organised technical meetings on a weekly basis in order to validate implementation of the solution with end users.
Those technical meetings were also the opportunity :
- to validate major expectations of end users : the operations efficiency of both Design Departments (optimisation of events planification) and Operations Departments (optimisation of resources allocations), the evaluation of the carbon footprint of maintenance operations,
- to present outputs of PrediSmart and collect end users feedbacks that we integrated into our developments in order and facilitate future adoption of the solution.
The demonstration phase is still on going but the first result achieved meet the expectation of the project : PrediSmart demonstrated its ability to provide consistent preconisations thanks to the latest Artificial Intelligence technologies. For both Design Departments and Operations Department, PrediSmart is about to become a change player.
While our interlocutors do not rely on their own existing data set, PrediSmart demonstrated that an appropriated and automated data processing can be a real source of added value for both design of organisations and day to day operations.
Moreover, the solution illustrated the gap existing between Design Department preconisations, Operations Departments realities and end users needs. PrediSmart succeed to reconcile those different visions built upon different databases : expertises, reports of activities, end users feedbacks, IoT, ....
PrediSmart offers the ability to challenge both customers expectations and services providers operations in order to meet end users real needs with an efficient and agile organisation.
Illustration of PrediSmart’s outputs for the Maintenance activities
Illustration of PrediSmart’s outputs for the Maintenance activities
Illustration of PrediSmart’s outputs for the Maintenance activities