Building Energy Management is identified as a key part of the move towards localised energy generation and control. The significant discrepancy between building energy use as designed and during actual operation shows a need to evaluate the relationship between building occupants and energy requirements. The need to better account for the influence of occupants on building energy use has been established through post-occupancy studies, highlighting the characteristics needed for more successful building control systems. This paper provides an overview of current building control systems technology and discusses existing academic research into more advanced occupant-centric controls. The potential for application of various methods is compared. It is found that study into occupant-centred control systems covers a wide array of approaches, ranging from simple presence-based switching of lighting systems to full model predictive control. Studies suggest an optimum point balancing the complexity of a system against its potential for saving energy.
Vehicle-to-grid services draw power or curtail demand from electric vehicles when they are connected to a compatible charging station. In this paper, we investigated automated machine learning for predicting when vehicles are likely to make such a connection. Using historical data collected from a vehicle tracking service, we assessed the technique's ability to learn and predict when a fleet of 48 vehicles was parked close to charging stations and compared this with two moving average techniques. We found the ability of all three approaches to predict when individual vehicles could potentially connect to charging stations to be comparable, resulting in the same set of 30 vehicles identified as good candidates to participate in a vehicle-to-grid service. We concluded that this was due to the relatively small feature set and that machine learning techniques were likely to outperform averaging techniques for more complex feature sets. We also explored the ability of the approaches to predict total vehicle availability and found that automated machine learning achieved the best performance with an accuracy of 91.4%. Such technology would be of value to vehicle-to-grid aggregation services.
The electric vehicles (EV) market is projected to continue its rapid growth, which will profoundly impact the demand on the electricity network requiring costly network reinforcements unless EV charging is properly managed. However, as well as importing electricity from the grid, EVs also have the potential to export electricity through vehicle-togrid (V2G) technology, which can help balance supply and demand and stabilise the grid through participation in flexibility markets. Such a scenario requires a population of EVs to be pooled to provide a larger storage resource. Key to doing so effectively however is knowledge of the users, as they ultimately determine the availability of a vehicle. In this paper we introduce a machine learning model that aims to learn both a) the criteria influencing users when they decided whether to make their vehicle available and b) their reliability in following through on those decisions, with a view to more accurately predicting total available capacity from the pool of vehicles at a given time. Using a series of simplified simulations, we demonstrate that the learning model is able to adapt to both these factors, which allows the required capacity of a market event to be satisfied more reliably and using a smaller number of vehicles than would otherwise be the case. This in turn has the potential to support participation in larger and more numerous market events for the same user base and use of the technology for smaller groups of users such as individual communities.
Vehicle-to-grid (V2G) has been identified as a key technology to help reduc carbon emissions from the transport and energy sectors. However, the benefits of this technology are best achieved when multiple variables are considered in the process of charging and discharging an electric vehicle. These variables include vehicle behaviour, building energy demand, renewable energy generation, and grid carbon intensity. It is expected that the transition to electric mobility will add pressure to the energy grid. Using the batteries of electric vehicles as energy storage to send energy back to the grid during high-demand, carbon-intensive periods will help to reduce the impact of introducing electric vehicles and minimise carbon emissions of the system. In this paper, the authors present a method and propose a V2G control scheme integrating one year of historical vehicle and energy datasets, aiming towards carbon emissions reduction through increased local consumption of renewable energy, offset of vehicle charging demand to low carbon intensity periods, and offset of local building demand from peak and carbon-intensive periods through storage in the vehicle battery. The study included assessment of strategic location and the number of chargers to support a fleet of five vehicles to make the transition to electric mobility and integrate vehicle-to-grid without impacting current service provision. The authors found that the proposed V2G scheme helped to reduce the average carbon intensity per kilowatt (gCO2/kWh) in simulation scenarios, despite the increased energy demand from electric vehicles charging. For instance, in one of the tested scenarios V2G reduced the average carbon intensity per kilowatt from 223.8 gCO2/kWh with unmanaged charging to 218.9 gCO2/kWh using V2G.
Poor air quality has been linked to cognitive deficits in children, but this relationship has not been examined in the first year of life when brain growth is at its peak. We measured in-home air quality focusing on particulate matter with diameter of <2.5 μm (PM2.5) and infants’ cognition longitudinally in a sample of families from rural India. Air quality was poorer in homes that used solid cooking materials. Infants from homes with poorer air quality showed lower visual working memory scores at 6 and 9 months of age and slower visual processing speed from 6-21 months when controlling for family socio-economic status. Thus, poor air quality is associated with impaired visual cognition in the first two years of life, consistent with animal studies of early brain development. We demonstrate for the first time an association between air quality and cognition in the first year of life using direct measures of in-home air quality and looking-based measures of cognition. Because indoor air quality was linked to cooking materials in the home, our findings suggest that efforts to reduce cooking emissions should be a key target for intervention.
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