Smart cities comprise several infrastructures that must be operated and coordinated through an integrated approach to maximize anticipated benefits. Electric utilities have been experiencing the benefits of new ICT technologies to improve decision making, systems security and operations functionality. However, natural gas distributors and urban irrigation utilities are just beginning to integrate these smart systems into their networks and we are just beginning to see their value. Since both natural gas and urban water distribution networks are designed and operated based on the idea of unidirectional flow, both suffer from lower service quality, reliability, sustainability and higher costs.

The AI-SMeCoT project aims to provide a unified infrastructure where data is collected from smart meters or smart meters of utilities to propose energy efficiency services. Remote meter management will allow companies to reduce field service personnel and vehicles and also to have real-time readings to propose solutions for load profiling, fraud detection, demand forecasting and predictive maintenance.

This is a Eureka project, in which I-nercya Intelligent Software will focus on the development of protocols and configuration interfaces with IoT elements, the design and development of algorithms based on deep learning for the prevention and discovery of energy fraud, and the development of an API for interconnection with any client and external system.

The project presents a series of technological innovations:

• Establish a baseline for data and data sources/devices that will allow new protocols, technologies and IoT devices to be included as developments emerge, pre-processing procedures for data from multiple sources (both historical and real-time data) will be implemented to allow them to be linked and stored in a way that analysis and predictive algorithms can more easily use them)
• The design and development of AI algorithms based on deep learning technologies that will enable the detection of energy fraud taking into account the consumption patterns obtained from Smart Meters and IoT devices in the network.

CDTI Project EXP 00149848 / IDI-20220849 in collaboration with Nimbeo Estrategia e Innovación