150 research outputs found
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Prediction of local particle pollution level based on artificial neural network
Citizens eager to know the local pollution level to prevent from air pollution. The real-time measurement for everywhere is a very expensive way, a statistical model based on artificial neural network is applied in this research. This model can estimate particle pollution level with some influencing factors, including background pollution level, weather conditions, urban morphology and local pollution sources. The monitoring from regulatory monitoring sites is considered as the background level. The field measurements of 20 locations are conducted to feed the output layer of ANN model. The average relative error of prediction compared with measurement is 9.24% for PM10 and 18.90% for PM2.5
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A combined engineering and statistical model of UK domestic appliance electrical load profiles
The development of a combined engineering and statistical Artificial Neural Network model of UK domestic appliance load profiles is presented. The model uses diary-style appliance use data and a survey questionnaire collected from 51 suburban households and 46 rural households during the summer of 2010 and2011 respectively. It also incorporates measured energy data and is sensitive to socioeconomic, physical dwelling and temperature variables. A prototype model is constructed in MATLAB using a two layer feed forward network with back propagation training which has a 12:10:24 architecture. Model outputs include appliance load profiles which can be applied to the fields of energy planning (microrenewables and smart grids), building simulation tools and energy policy
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Indoor thermal environments in Chinese residential buildings responding to the diversity of climates
China has a diversity of climates and a unique historic national heating policy which greatly affects indoor thermal environment and the occupants’ thermal response. This paper quantitatively analyzes the data from a large-scale field study across the country conducted from 2008 to 2011 in residential buildings. The study covers nine typical cities located in the five climate zones including Severe Cold (SC), Cold (C), Hot Summer and Cold Winter (HSCW), Hot Summer and Warm Winter (HSWW) and Mild (M) zones. It is revealed that there exists a large regional discrepancy in indoor thermal environ- ment, the worst performing region being the HSCW zone. Human’s long-term climate adaptation leads to wider range of acceptable thermal comfort temperature. Different graphic comfort zones with accept- able range of temperature and humidity for the five climate zones are obtained using the adaptive Predictive Mean Vote (aPMV) model. The results show that occupants living in the poorer thermal environments in the HSCW and HSWW zones are more adaptive and tolerant to poor indoor conditions than those living in the north part of China where central heating systems are in use. It is therefore recommended to develop regional evaluation standards of thermal environments responding to climate characteristics as well as local occupants’ acclimatization and adaptation in order to meeting dual targets of energy conservation and indoor thermal environment improvement
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Exploring the “black box” of thermal adaptation using information entropy
Thermal adaptation has been interpreted well by behavioral, physiological, and psychological factors, but the mechanism and interaction between the three factors remain in the “black box”. This paper aims to apply the theory of general system and information entropy to investigate the quantitative relationships of the three thermal adaptation processes. Based on the database from the field survey and laboratory experiments conducted in the hot summer and cold winter climate zone of China, three typical adaptive indices: clothing insulation (Clo), thermal sensation votes (TSV) and sensory nerve conduction velocity (SCV) were selected to calculate Clo entropy, TSV entropy, SCV entropy and total entropy. The regression models were developed between these entropies and the indoor air temperature to quantify the weights of the three adaptive categories. The models were used to compare the differences between China and Pakistan as well as between adaptive approaches and climate chamber experiments. The thermal comfort and acceptable temperature ranges were obtained using the entropy models. Our findings propose a new perspective using entropy to quantify the behaviorally, physiologically, and psychologically adaptive approaches, which contribute to a better understanding of opening the “black box” of thermal adaptation
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Development of stochastic models of window state changes in educational buildings
How people would like to interact with surrounding environment will subsequently influence indoor thermal conditions and further impact building energy performance. In order to understand occupants' adaptive behaviours in terms of environmental control utilization from the point of view of quantification, an investigation on windows operation was carried out in non-air-conditioned educational buildings in the UK during summer time considering the effects of occupant type (active and passive) and the time of a day. Outdoor air temperature was a better predictor or window operation than indoor air temperature. Window operation was found to be time-evolving event. The purpose or criteria of adjusting window states were different at different occupancy stages. Active occupants were more willing to change windows states in response to outdoor air temperature variations. Sub-models predicting transition probabilities of window state for different occupant type and occupancy stages were developed. The results derived from this field study are helpful with improving building simulation accuracy by integrating sub-models into simulation software and further providing guideline on building energy reduction without sacrificing indoor thermal comfort
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A review of existing building benchmarks and the development of a set of reference office buildings for England and Wales
The modern built environment has become more complex in terms of building types, environmental systems and use profiles. This complexity causes difficulties in terms of optimising buildings energy design. In this circumstance, introducing a set of prototype reference buildings, or so called benchmark buildings, that are able to represent all or majority parts of the UK building stock may be useful for the examination of the impact of national energy policies on building energy consumption. This study proposes a set of reference office buildings for England and Wales based on the information collected from the Non-Domestic Building Stock (NDBS) project and an intensive review of the existing building benchmarks. The proposed building benchmark comprises 10 prototypical reference buildings, which in relation to built form and size, represent 95% of office buildings in England and Wales. This building benchmark provides a platform for those involved in building energy simulations to evaluate energy-efficiency measures and for policy-makers to assess the influence of different building energy policies
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A probabilistic prediction model for window opening during transition seasons in office building
Window operation of occupants in building has close relationship with indoor air quality, indoor thermal environment and building energy performance. The objective of this study was to understand occupants' interaction with window opening in transition seasons considering the influence of subject type (e.g. active and passive respondents) and to develop corresponding predictive models. An investigation was carried out in non-air-conditioned building in the UK covering the period from September to November. Outdoor temperature in this study was determined as good predictor for window operation. The differences in window opening probabilities between active and passive subjects were significant. Active occupants preferred to open window for fresh air or for indoor thermal condition adjustment, even though the outdoor air temperature sometimes were less than 12 °C. Proper utilization of windows in transition seasons contributed significantly to building energy saving and further improve energy efficiency in buildings
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A multidimensional model for green building assessment: a case study of a highest-rated project in Chongqing
Green building is an inevitable trend in the construction industry which deeply affects the social development of the economy, environment and a series of industries. There is practical significance for the multidimensionally balanced development of green buildings. A model for multi-objective assessment of green building is devel-oped under three dimensions: Objective, Professional and Time (OPT) according to the green building definition. The OPT coordinate system was built up based on the scoring centroid system of both the China Green Building Labelling scheme (GBL) and the Singapore Green Mark (GM) by the introduction of the Coefficient of Varia-tion and Moment of Inertia. Both these frameworks are restructured based on a case study of a practical project in Chongqing which had achieved the highest GBL and GM awards. Results show that GBL distributes its scores more evenly while GM concentrates on energy saving with greater diversity in land supply and building oper-ations (normalized coefficients of variation of 0.435 and 0.350). The project’s com-pliance coefficients are 1.27 and 0.31 under GBL and GM respectively indicating its higher degree of compliance with the GM framework. The developed model provides multitarget-oriented guidelines for green building design, assessment and stand-arddevelopment
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A method of evaluating the accuracy of human body thermoregulation models
Human Body Thermoregulation Models have been widely used in the field of human physiology or thermal comfort studies. However there are few studies on the evaluation method for these models. This paper summarises the existing evaluation methods and critically analyses the flaws. Based on that, a method for the evaluating the accuracy of the Human Body Thermoregulation models is proposed. The new evaluation method contributes to the development of Human Body Thermoregulation models and validates their accuracy both statistically and empirically. The accuracy of different models can be compared by the new method. Furthermore, the new method is not only suitable for the evaluation of Human Body Thermoregulation Models, but also can be theoretically applied to the evaluation of the accuracy of the population-based models in other research fields
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