The study of energy consumption habits in consumers is useful to save money, energy and decrease pollution. Nowadays, the big amount of data managed in market statistics requires a certain grade of automation to process the information within an acceptable period of time.
In this regard, automated recognition in images can greatly help to perform energy market studies where visual inspection is required. The application of camera-based systems has exponentially grown in recent years due to the improvements in camera features and computer vision techniques. Besides, the rise of deep learning in the last decade has greatly enhanced the effectiveness of computer vision systems, which can automatically compute and extract very relevant information from images.
According to the previous considerations, the goal of the proposed project is to research on innovative computer vision and deep learning algorithms with the aim of contributing in the automation of energy consumption market studies based on information obtained from images.
The goal of the proposed research line is to develop an automated parking detection system based on computer vision techniques distributed in a mobile application across several users, together with a centralized server that maps the city parking slots and their availability by using user’s data. The server would construct a complete map view of the street parking slots in the city by making use of the processed camera information from the user’s phones, which is geolocated to the user’s location by using GPS and visual odometry. The server would continuously feed the built map and information about available spots to the users, so they can take advantage of this data to find a parking slot much faster. Such a system could significantly lower the time that drivers spend looking for parking slots, directly reducing the associated costs of parking and congestion in cities.
Intelligent vehicles technologies require a good understanding of human driver behaviors to guarantee safe, adjust to drivers’ needs and meet their preferences. This research line try to answer these questions monitoring driving behaviors by using the emerging deep learning approach, based on big data, which is revolutionizing the classical machine learning techniques, getting a breakthrough in the performance of complex classification and decision-making problems. Our proposal is to monitor driver behavior in real-time and to carry out decision making tasks, by using deep learning techniques, in order to implement a safe switching system between manual and automatic mode in the future V2U (Vehicle to User) interfaces for autonomous vehicles.
Global control of smart grids through integration of Automatic Metering Infrastructure in Advanced Distribution Management Systems
Power network analysis have been mostly based on HV and MV signals, while today availability of smart meters data in (near) real-time is disclosing new possibilities for optimal control. Moreover, the increasing number of dispersed generators is focusing the objective on low voltage grid monitoring and control.
This research activity will be focused on integrate the smart metering signals and data from different utility metering infrastructures, into a unified platform able to ingest data with different models and protocols (DLMS/COSEM, PLC Prime, M-Bus, etc.). The expected result is the capacity to acquire and expose data from different utilities (i.e. energy carriers) into a unique centralized solution.
A bi-lateral optimization through novel Artificial Intelligence applications, based on High Energy Performance approach in buildings
This research activity will be focused on the creation of novel tools and services through a High Energy Performance approach. The main goal is to develop an AI service for optimal control of energy considering behind the meter resources (management of energy sources at user level), with local (building or district) overall efficiency.
In indoor scenarios, such as meeting rooms, exhibitions, museums or, even, homes, a huge demand on communications and location-based services (LBS) is expected in near future, mainly motivated by the emerge of the Internet of Things (IoT) era. One of the technologies called to play an important role is the one based on visible light, that is leveraged on the technological advances in Light Emitting Diodes (LED).
LED devices have evolved steadily in recent times, so in addition to lighting allow other interesting applications. For example, one of the most important objectives is to reach complete and cheaper communication through them. Currently this is known as lifi, and some prototypes perform this functionality. the LiFi communication can come to play an importat rol to different market as the automotive sector, users position inside of buildings, industry 4.0, etc. This proposal aims to develop communication and positioning systems to several sectors of industry, transport, etc..
The GOT-Energy-TALENT proposal hereby presented by GEINTRA Group involves different tasks related to the monitoring and analysis of different indicators in the context of home and city surveillance in order to optimize the smart cities capabilities in terms of energy management and security. Works hereby focused thus involve two different important aims in the actual and future society: security and energy.
This proposal in the GOT-Energy-Talent call is oriented to support the transition to a reliable, sustainable and competitive smart city, within the Smart Cities & Communities area: sustainable development of urban areas is a challenge of key importance. It requires new, efficient, and user-friendly technologies and services, in which surveillance applications for people monitoring and analysis, is already a well-developed field in some other advanced societies such as the Asian one. The proposal hereby presented tackles the attainment of a commercial-scale solution with a high market potential that analyzes energy, transport and communications resources and use in which people audio-visual surveillance is the main key.
The GOT-Energy-TALENT proposal hereby presented by GEINTRA Group involves tasks related to the monitoring and analysis of different indicators in the context of home and city surveillance in order to optimize the smart cities capabilities in terms of energy management and security. The works under this proposal are thus focused in two relevant aims in technology supporting current and future societies: security and energy. This proposal in the GOT-Energy-Talent call is oriented to support the transition to a reliable, sustainable and competitive smart city, within the Energy Efficiency topic: being a no-regret option for Europe, energy consumption is needed to progressively decrease by 2020 to 2030.