Spatio-temporal environmental monitoring for smart buildings

© 2017 IEEE. The paper addresses the problem of efficiently monitoring environmental fields in a smart building by the use of a network of wireless noisy sensors that take discretely-predefined measurements at their locations through time. It is proposed that the indoor environmental fields are statistically modeled by spatio-temporal non-parametric Gaussian processes. The proposed models are able to effectively predict and estimate the indoor climate parameters at any time and at any locations of interest, which can be utilized to create timely maps of indoor environments. More importantly, the monitoring results are practically crucial for building management systems to efficiently control energy consumption and maximally improve human comfort in the building. The proposed approach was implemented in a real tested space in a university building, where the obtained results are highly promising

Efficient spatio-temporal sensor deployments: A smart building application

© 2017 IEEE. The paper addresses the problem of efficiently deploying sensors in spatial environments, e.g. smart buildings, for the purpose of monitoring environmental phenomena. By modelling the environmental fields using spatio-temporal Gaussian processes, a new and efficient optimality criterion of minimizing prediction uncertainties is proposed to find the best sensor locations. Though the environmental processes spatially and temporally vary, the proposed approach of choosing sensor positions is not affected by time variations, which significantly reduces computational complexity of the optimization problem. The sensor deployment problem is then solved by a practically and feasibly polynomial algorithm, where its solutions are guaranteed. The proposed approaches were implemented in a real tested space in a university building, where the obtained results are highly promising

Efficient Sensor Deployments for Spatio-Temporal Environmental Monitoring

IEEE This paper addresses the problem of efficiently deploying sensors in spatial environments, e.g., buildings, for the purposes of monitoring spatio-temporal environmental phenomena. By modeling the environmental fields using spatio-temporal Gaussian processes, a new and efficient optimality-cost function of minimizing prediction uncertainties is proposed to find the best sensor locations. Though the environmental processes spatially and temporally vary, the proposed approach of choosing sensor positions is proven not to be affected by time variations, which significantly reduces computational complexity of the optimization problem. The sensor deployment optimization problem is then solved by a practical and feasible polynomial algorithm, where its solutions are theoretically proven to be guaranteed. The proposed method is also theoretically and experimentally compared with the existing works. The effectiveness of the proposed algorithm is demonstrated by implementation in a real tested space in a university building, where the obtained results are highly promising

Distributed Infrastructure for Multi-Energy-Systems Modelling and Co-simulation in Urban Districts

In recent years, many governments are promoting a widespread deployment of Renewable Energy Sources (RES) together with an optimization of energy consumption. The main purpose consists on decarbonizing the energy production and reducing the CO2 footprints. However, RES imply uncertain energy production. To foster this transition, we need novel tools to model and simulate Multi-Energy-Systems combining together different technologies and analysing heterogeneous information, often in (near-) real-time. In this paper, first we present the main challenges identified after a literature review and the motivation that drove this research in developing MESsi. Then, we propose MESsi, a novel distributed infrastructure for modelling and cosimulating Multi-Energy-Systems. This infrastructure is a framework suitable for general purpose energy simulations in cities. Finally, we introduce possible simulation scenarios that have different spatio-temporal resolutions. Space resolution ranges from the single dwelling up to districts and cities. Whilst, time resolution ranges from microseconds, to simulate the operational status of distribution networks, up to years, for planning and refurbishment activities

Implementing Air Pollution and Health Damage Costs in Urban Multi-Energy Systems Modelling

The growing global urbanization rate implies that the sustainability challenges are increasingly concentrated in cities. At today, around 75% of global energy is consumed in urban areas, so efforts must be addressed to transform existing urban energy systems into more sustainable systems. In this perspective, a key aspect to evolve toward a cleaner and affordable energy system is the development of Multi-Energy Systems (MES) modelling, whereby heat, electricity, fuels, transport, and other energy carriers closely interact with each other at various scales. MES can optimize technical, economic and environmental performance with respect to “traditional” independent energy systems, at both the operational and the planning stage. This paper presents a development of the existing MESsi modelling platform, consisting in the implementation of a model estimating the impacts on air quality and human health. MESsi is a novel distributed infrastructure for modelling and co-simulating Multi-Energy-Systems. It exploits modern software design patterns (i.e. microservices) to guarantee scalability, extendibility and easy maintenance of the system. Thus, MESsi is flexible in modelling and co-simulating different energy flows in a single solution made of different interoperable modules that can be deployed in a plug-and-play fashion. The module to be implemented in MESsi infrastructure is the DIATI integrated dispersion and externalities model (DIDEM). The DIDEM model is based on the impact pathway approach, linking the simulation of pollutants dispersion to the concentration-exposure-response functions provided by latest WHO recommendations. An overview of the potential integration steps in the modelling infrastructure is described in this paper. A discussion on possible application scenarios that have different spatio-temporal resolutions is also reported. The integration of DIDEM model in MESsi platform allows the inter-connection of a detailed impact assessment to a high-level energy system simulation

Fireground location understanding by semantic linking of visual objects and building information models

This paper presents an outline for improved localization and situational awareness in fire emergency situations based on semantic technology and computer vision techniques. The novelty of our methodology lies in the semantic linking of video object recognition results from visual and thermal cameras with Building Information Models (BIM). The current limitations and possibilities of certain building information streams in the context of fire safety or fire incident management are addressed in this paper. Furthermore, our data management tools match higher-level semantic metadata descriptors of BIM and deep-learning based visual object recognition and classification networks. Based on these matches, estimations can be generated of camera, objects and event positions in the BIM model, transforming it from a static source of information into a rich, dynamic data provider. Previous work has already investigated the possibilities to link BIM and low-cost point sensors for fireground understanding, but these approaches did not take into account the benefits of video analysis and recent developments in semantics and feature learning research. Finally, the strengths of the proposed approach compared to the state-of-the-art is its (semi -)automatic workflow, generic and modular setup and multi-modal strategy, which allows to automatically create situational awareness, to improve localization and to facilitate the overall fire understanding