Comfort driven adaptive window opening behaviour and the influence of building design

It is important to understand and model the behaviour of occupants in buildings and how this behaviour impacts energy use and comfort. It is similarly important to understand how a buildings design affects occupant comfort, occupant behaviour and ultimately the energy used in the operation of the building. In this work a behavioural algorithm for window opening developed from field survey data has been implemented in a dynamic simulation tool. The algorithm is in alignment with the proposed CEN standard for adaptive thermal comfort. The algorithm is first compared to the field study data then used to illustrate the impact of adaptive behaviour on summer indoor temperatures and heating energy. The simulation model is also used to illustrate the sensitivity of the occupant adaptive behaviour to building design parameters such as solar shading and thermal mass and the resulting impact on energy use and comfort. The results are compared to those from other approaches to model window opening behaviour. The adaptive algorithm is shown to provide insights not available using non adaptive simulation methods and can assist in achieving more comfortable and lower energy buildings

Twenty first century standards for thermal comfort : fostering low carbon building design and operation

Nearly 50% of energy consumed in the developed world is consumed in buildings. Despite regulation intent, many new buildings are energy profligate. Thermal comfort standards are partly responsible for this increase in consumption. In this volume, Roaf et al. have described the evolution of current comfort standards and problems inherent in buildings they shape, and have discussed two new methods of regulating thermal comfort in buildings which recognize human adaptation and have potential for reduced energy demand. These new methods incorporate adaptation through a fixed heating and cooling threshold approach (similar to Japanese Cool-Biz) or through heating and cooling setpoints calculated based on outdoor conditions(using CEN standard equations). The impact on comfort and energy demand of these new approaches is investigated for a London office building. Variables such as future climate, future building upgrades, setback temperatures, internal gains and ventilation are also explored. Adoption of the new approaches gave a 50% reduction in heating and cooling energy for the simulated office. The new approach together with optimized setback temperatures, ventilation strategies and higher efficiency equipment gives predicted heating and cooling energy demand close to zero. Recommendations for future regulation, design and operation of buildings are proposed

Global impacts of energy demand on the freshwater resources of nations

The growing geographic disconnect between consumption of goods, the extraction and processing of resources, and the environmental impacts associated with production activities makes it crucial to factor global trade into sustainability assessments. Using an empirically validated environmentally extended global trade model, we examine the relationship between two key resources underpinning economies and human well-being—energy and freshwater. A comparison of three energy sectors (petroleum, gas, and electricity) reveals that freshwater consumption associated with gas and electricity production is largely confined within the territorial boundaries where demand originates. This finding contrasts with petroleum, which exhibits a varying ratio of territorial to international freshwater consumption, depending on the origin of demand. For example, although the United States and China have similar demand associated with the petroleum sector, international freshwater consumption is three times higher for the former than the latter. Based on mapping patterns of freshwater consumption associated with energy sectors at subnational scales, our analysis also reveals concordance between pressure on freshwater resources associated with energy production and freshwater scarcity in a number of river basins globally. These energy-driven pressures on freshwater resources in areas distant from the origin of energy demand complicate the design of policy to ensure security of fresh water and energy supply. Although much of the debate around energy is focused on greenhouse gas emissions, our findings highlight the need to consider the full range of consequences of energy production when designing policy

Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor

The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities

An investigation on the effect of driver style and driving events on energy demand of a PHEV

Environmental concerns, security of fuel supply and CO2 regulations are driving innovation in the automotive industry towards electric and hybrid electric vehicles. The fuel economy and emission performance of hybrid electric vehicles (HEVs) strongly depends on the energy management system (EMS). Prior knowledge of driving information could be used to enhance the performance of a HEV. However, how the necessary information can be obtained to use in EMS optimisation still remains a challenge. In this paper the effect of driver style and driving events like city and highway driving on plug in hybrid electric vehicle (PHEV) energy demand is studied. Using real world driving data from three drivers of very different driver style, a simulation has been exercised for a given route having city and highway driving. Driver style and driving events both affect vehicle energy demand. In both driving events considered, vehicle energy demand is different due to driver styles. The major part of city driving is reactive driving influenced by external factors and driver leading to variation in vehicle speed and hence energy demand. In free highway driving, the driver choice of cruise speed is the only factor affecting vehicle energy demand