Scalable Distributed Sensor Fault Diagnosis for Smart Buildings

The enormous energy use of the building sector and the requirements for indoor living quality that aim to improve occupants’ productivity and health, prioritize Smart Buildings as an emerging technology. The Heating, Ventilation and Air-Conditioning (HVAC) system is considered one of the most critical and essential parts in buildings since it consumes the largest amount of energy and is responsible for humans comfort. Due to the intermittent operation of HVAC systems, faults are more likely to occur, possibly increasing eventually building’s energy consumption and/or downgrading indoor living quality. The complexity and large scale nature of HVAC systems complicate the diagnosis of faults in a centralized framework. This paper presents a distributed intelligent fault diagnosis algorithm for detecting and isolating multiple sensor faults in large-scale HVAC systems. Modeling the HVAC system as a network of interconnected subsystems allows the design of a set of distributed sensor fault diagnosis agents capable of isolating multiple sensor faults by applying a combinatorial decision logic and diagnostic reasoning. The performance of the proposed method is investigated with respect to robustness, fault detectability and scalability. Simulations are used to illustrate the effectiveness of the proposed method in the presence of multiple sensor faults applied to a 83-zone HVAC system and to evaluate the sensitivity of the method with respect to sensor noise variance.      

The role of smart packaging system in food supply chain

Food supply chain is a rapidly growing integrated sector and covers all the aspects from farm to fork, including manufacturing, packaging, distribution, storing, as well as further processing or cooking for consumption. Along this chain, smart packaging could impact the quality, safety, and sustainability of food. Packaging systems have evolved to be smarter with integration of emerging electronics and wireless communication and cloud data solutions. Although there are many factors causing the loss and waste issues for foods throughout the whole supply chain of food and there have been several articles showing the recent advances and breakthroughs in developing smart packaging systems, this review integrates these conceptual frameworks and technological applications and focuses on how innovative smart packaging solutions are beneficial to the overall quality and safety of food supply by enhancing product traceability and reducing the amount of food loss and waste. We start by introducing the concept of the management for the integrated food supply chain, which is critical in tactical and operational components that can enhance product traceability within the entire chain. Then we highlight the impact of smart packaging in reducing food loss and waste. We summarize the basic information of the common printing techniques for smart packaging system (sensor and indicator). Then, we discuss the potential challenges in the manufacturing and deployment of smart packaging systems, as well as their cost-related drawbacks and further steps in food supply chain.     

基于大数据挖掘的用电行为分析

得益于智能电网的发展,电力大数据的价值与作用逐渐被挖掘。通过对电力大数据的深入挖掘与研究,可促进电网企业管理水平的进一步提升,为我国智能电网的发展提供助力。而依托于大数据挖掘的用户用电行为分析,可实现当前企业电网调控与需求侧的充分融合,为电力系统的稳定性运行提供保障。基于此,文章针对大数据挖掘的用电行为分析进行深入探析,以期为智能电网发展提供帮助。     

An improved particle swarm optimisation for unit commitment in microgrids with battery energy storage systems considering battery degradation and uncertainties

Using electric storage systems (ESSs) is known as a viable strategy to mitigate the volatility and intermittency of renewable distributed generators (DGs) in microgrids (MGs). Among different electric storage technologies, battery energy storage (BES) is considered as the best option. In unit commitment (UC) module, the set of committed dispatchable DGs along with their power, power exported to/imported from macrogrid and status and power of ESS units are determined. In this paper, BES degradation is considered in UC formulation and an efficient particle swarm optimisation with quadratic transfer function is proposed for solving UC in BES‐integrated MGs, while the uncertainties of demand, renewable generation and market price are considered and dealt with robust optimisation. UC is formulated as a multi‐objective optimisation problem whose objectives are MG operation cost and BES degradation. The resultant multi‐objective optimisation problem is converted into a single‐objective optimisation problem and the effect of weight factors on MG operation cost and BES lifecycle are investigated. The results show that by consideration of BES degradation in objective function, BES lifecycle increases from 350 to 500 and the minimum depth of charge increases from 5.5% to 34%; however, MG operation cost increases from $8717 to $8910.2. The results also show that by consideration of uncertainties, MG's operation cost increases by 8.22%.     

Smart Polymers for Microscale Machines

Microscale machines are able to perform a number of tasks like micromanipulation, drug-delivery, and noninvasive surgery. In particular, microscale polymer machines that can perform intelligent work for manipulation or transport, adaptive locomotion, or sensing are in-demand. To achieve this goal, shape-morphing smart polymers like hydrogels, liquid crystalline polymers, and other smart polymers are of great interest. Structures fabricated by these materials undergo mechanical motion under stimulation such as temperature, pH, light, and so on. The use of these materials renders microscale machines that undergo complex stimuli-responsive transformation such as from planar to 3D by combining spatial design like introducing in-plane or out-plane differences. During the past decade, many techniques have been developed or adopted for fabricating structures with smart polymers including microfabrication methods and the well-known milestone of 4D printing, starting in 2013. In this review, the existing or potential active smart polymers that could be used to fabricate active microscale machines to accomplish complex tasks are summarized.     

The reliability of inverse modelling for the wide scale characterization of the thermal properties of buildings

The reduction of energy use in buildings is a major component of greenhouse gas mitigation policy and requires knowledge of the fabric and the occupant behaviour. Hence there has been a longstanding desire to use automatic means to identify these. Smart metres and the internet-of-things have the potential to do this. This paper describes a study where the ability of inverse modelling to identify building parameters is evaluated for 6 monitored real and 1000 simulated buildings. It was found that low-order models provide good estimates of heat transfer coefficients and internal temperatures if heating, electricity use and CO₂ concentration are measured during the winter period. This implies that the method could be used with a small number of cheap sensors and enable the accurate assessment of buildings’ thermal properties, and therefore the impact of any suggested retrofit. This has the potential to be transformative for the energy efficiency industry.     

Indoor microbiome and risk of lower respiratory tract infections among children under-five years: A meta-analysis

We investigated whether exposure to microbiome within the indoor environment is associated with risk of lower respiratory tract infections (LRTI) among children under 5 years of age. Electronic scientific repositories; PubMed, Scopus, Web of Science, GreenFILE, EMBASE, and Cochrane library were searched and screened through July 2019 for published reports for inclusion in the meta-analysis. Studies were eligible for inclusion if they reported an adjusted measure of risk for LRTI associated with IM exposure, including the relative risk (RR) or odds ratio (OR) and confidence interval (CI). The pooled OR was computed using the inverse of variance method for weighting. Sensitivity analysis was used to evaluate the effect of individual studies, while heterogeneity was evaluated by I² statistics using RevMan 5.3. Seven studies were eligible for inclusion in our meta-analysis. Exposure to a higher concentration of IM was associated with an increased risk of LRTI [OR:1.20 (1.11, 1.33), P < .0001]. The risk was stronger with exposure to total fungal concentration [OR:1.27 (1.13, 1.44), P < .0001] than visible molds [OR:1.20 (1.07, 1.34, P = .001]. Under-five children exposed to higher IM concentration are likely at increased risk of LRTI. Interventions addressing IM exposure should be considered in the management of LRTI among under-five children.