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

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

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

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

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

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

A fuzzy multiple attribute decision making tool for HVAC&R systems selection with considering the future probabilistic climate changes and electricity decarbonisation plans in the UK

Buildings account for 40% of total energy consumption in the UK and more than 55% of this energy is used by heating, ventilation, air-conditioning and refrigeration (HVAC&R) systems. This significant energy demand and the ascending trend in utilising HVAC&R systems together with the global need to impose energy-efficiency measures underline the importance of selecting the most appropriate HVAC&R system during the design process. This paper reviewed and classified a broad range of principal multiple attribute decision making methods. Among them, the fuzzy multiple attribute decision making approach was adopted to develop a decision making tool for HVAC&R systems selection. This was mainly due to the ability of this method to deal with the uncertainties and imprecisions of the linguistic terms involved in the decision making process. In order to make a decision on HVAC&R systems selection, 58 alternative systems, including both primary and secondary parts, were examined. The scope of this study enabled the consideration of all 18 climate regions in the UK and included the effects of climate change. In addition, the Government’s electricity decarbonisation plans were integrated within the developed decision making model for HVAC&R systems selection in office buildings in the UK. Finally, the model was transferred into a computational tool with a user-friendly interface

An investigation on energy consumption of public buildings in Chongqing, China

Global climate change is one of the most important environmental issues that human have ever faced. China is taking an active role in reducing carbon dioxide emission in order to alleviate the climate change process. Building sectors contribute for 30% of carbon emission and 27.5% of total energy consumption in China. There is an urgent need for improving building energy efficiency to achieve carbon reduction. New buildings are legislated by national standards and regulations to secure a relatively high level of energy efficiency. However, the diversity of architectural design, system operation and management make it a big challenging to achieve energy efficiency in existing buildings. Existing researches have already investigated the building retrofit technologies and strategies. However, information on the current building stocks is even more important due to its impact in decision makings of retrofit strategies. This paper investigates the energy consumption of public buildings in Chongqing, China. Building energy consumption data collected from Chongqing public building energy consumption monitoring platform was analyzed by SPSS software. The data collection and analysis are focused on governmental office, general office, hotel buildings and shopping mall. Statistical hypothesis test, using log-normal P-P plot and Shapiro–Wilk test, reveals that the annual energy consumption densities of these types of building are log-normal distributed

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