Study on Ann-Based Multi-Step Prediction Model of Short-Term Climatic Variation

In the context of 1905–1995 series from Nanjing and Hangzhou, study is undertaken of estab-lishing a predictive model of annual mean temperature in 1996–2005 to come over the Changjiang (Yangtze River) delta region through mean generating function and artificial neural network in combination. Results show that the established model yields mean error of 0.45°C for their abso-lute values of annual mean temperature from 10 yearly independent samples (1986–1995) and the difference between the mean predictions and related measurements is 0.156°C. The developed model is found superior to a mean generating function regression model both in historical data fit-ting and independent sample prediction. Key words Climate trend prediction. Mean generating function (MGF) - Artificial neural network (ANN) - Annual mean temperature (AMT)

Monthly Mean Temperature Prediction Based on a Multi－level Mapping Model of Neural Network BP Type

In terms of 34-year monthly mean temperature series in 1946-1979,the multi-level maPPing model of neural netWork BP type was applied to calculate the system's fractual dimension Do=2'8,leading tO a three-level model of this type with ixj=3x2,k=l,and the 1980 monthly mean temperture predichon on a long-t6rm basis were prepared by steadily modifying the weighting coefficient,making for the correlation coefficient of 97% with the measurements.Furthermore,the weighhng parameter was modified for each month of 1980 by means of observations,therefore constrcuhng monthly mean temperature forecasts from January to December of the year,reaching the correlation of 99.9% with the measurements.Likewise,the resulting 1981 monthly predictions on a long-range basis with 1946-1980 corresponding records yielded the correlahon of 98% and the month-tO month forecasts of 99.4%.

AN IMPROVED MEAN SURFACE METHOD FOR SUBSONIC PROPELLER NOISE PREDICTION

An improved mean surtace method (IMSM) for propeller noise predictionis presented, based on solving the Ffowes Williams & Hawking (FW & H) equation intime domain. Numerical calculation results show that the IMSM needs only 50% ofCPU time and memory of subsonic full surface method (SFSM), 50%～ 60% of CPUtime and meinory of old nican surfacc method (OMSM) and 10%～ 13% of CPU timeof transonic collapse spliere method (TCSM) while the calculation accuracy can be as-sured. Thus, the IMSM given in this paper could be a good alternative method forsubsonic propeller noise prediction. especially in preliminary aeroacoustics design of anew propeller.

Revisiting Akaike’s Final Prediction Error and the Generalized Cross Validation Criteria in Regression from the Same Perspective: From Least Squares to Ridge Regression and Smoothing Splines

In regression, despite being both aimed at estimating the Mean Squared Prediction Error (MSPE), Akaike’s Final Prediction Error (FPE) and the Generalized Cross Validation (GCV) selection criteria are usually derived from two quite different perspectives. Here, settling on the most commonly accepted definition of the MSPE as the expectation of the squared prediction error loss, we provide theoretical expressions for it, valid for any linear model (LM) fitter, be it under random or non random designs. Specializing these MSPE expressions for each of them, we are able to derive closed formulas of the MSPE for some of the most popular LM fitters: Ordinary Least Squares (OLS), with or without a full column rank design matrix;Ordinary and Generalized Ridge regression, the latter embedding smoothing splines fitting. For each of these LM fitters, we then deduce a computable estimate of the MSPE which turns out to coincide with Akaike’s FPE. Using a slight variation, we similarly get a class of MSPE estimates coinciding with the classical GCV formula for those same LM fitters.

Modeling compatible single-tree aboveground biomass equations for masson pine(Pinus massoniana) in southern China

Because of global climate change, it is necessary to add forest biomass estimation to national forest resource monitoring. The biomass equations developed for forest biomass estimation should be compatible with volume equations. Based on the tree volume and aboveground biomass data of Masson pine （Pinus massoniana Lamb.） in southern China, we constructed one-, two- and three-variable aboveground biomass equations and biomass conversion functions compatible with tree volume equations by using error-in-variable simultaneous equations. The prediction precision of aboveground biomass estimates from one variable equa- tion exceeded 95%. The regressions of aboveground biomass equations were improved slightly when tree height and crown width were used together with diameter on breast height, although the contributions to regressions were statistically insignificant. For the biomass conversion function on one variable, the conversion factor decreased with increasing diameter, but for the conversion function on two variables, the conversion factor increased with increasing diameter but decreased with in- creasing tree height.

Numerical simulation study on the thermal environment in heading face

Based on κ-ε turbulence model, the distributions of the velocity field, tempera- ture field, thermal comfort index, PMV-PPD （Predicted Mean Vote-Predicted Percentage Dissatisfied）, and air quality index, mean age of air were obtained of Zhouyuanshan Coal Mine in -650 m level of a heading face by CFD（Computational Fluid Dynamics）software, Airpak 2.0. Moreover, the human thermal comfort and the air quality of the heading face were analyzed with PMV-PPD and mean age of air indices, which received an intuitive visualization and accurate evaluation results. In order to create a safe, comfortable, and economical underground operating environment, a scientific, rational, and comprehensive prediction and evaluation needed to provide a theoretical and technical basis for coal mine ventilation, cooling, heat harm treatment, and prevention. Meanwhile, from the human thermal comfort and air quality to research the underground environment, it embodied the concept of being human oriented.

Effect of horizontal radiant temperature asymmetry on thermal comfort and environmental parameters in a warm sitting area

Taiwan is a hot and humid place;in addition,the sunlight is strong and sufficient,the naturally ventilated classrooms have large-area windows for better lighting and ventilation in Taiwan.However,subjects sitting close to the large-area windows will encounter the problem of radiant temperature asymmetry,which may cause local thermal discomfort during summer.Hence,this paper is to investigate the influence of horizontal radiant temperature asymmetry on the thermal sensation of subjects and the correlative variation of environmental parameters in a warm sitting area.The evaluation results show that the predicted mean vote(PMV)index can be used to predict the overall thermal sensation of a group of sedentary subjects in a warm environment with horizontal radiant temperature asymmetry;even subjects feel local discomfort on exposed parts of their body.The measured results of environmental parameters indicate that the averaged radiant temperature difference is about 4.6 ℃ from right shoulder to left shoulder;the temperature difference does not only cause correlative variations of other environmental parameters,but also result in thermal discomfort on the right cheeks and forearms of subjects in the warm sitting area.

Energy Efficient Thermal Comfort Control for Residential Building Based on Nonlinear EMPC

For purpose of achieving the desired thermal comfort level and reducing the economic cost of maintaining the thermal comfort of green residential building,an energy efficient thermal comfort control strategy based on economic model predictive control(EMPC)for green residential buildings which adopts household heat metering is presented.Firstly,the nonlinear thermal comfort model of heating room is analyzed and obtained.A practical nonlinear thermal comfort prediction model is obtained by using an approximation method.Then,the economic cost function and optimization problem of energy-saving under the necessary thermal comfort requirements are constructed to realize the optimal economic performance of the dynamic process.The energy efficient thermal comfort MPC(EETCMPC)is designed.Finally,the comparison and analysis between EETCMPC and Double-layer Model Predictive Control(DMPC)is simulated.The simulation results reveal that when the clothing insulation is typical,the energy efficiency of EETCMPC is 8.9%and 11.6%,respectively,in the two simulation scenarios.When the clothing insulation varies with temperature,the energy efficiency of EETCMPC is 7.29%and 9.15%,respectively,and the total energy consumption is reduced by about 1.65%and 14.6%,respectively,compared with the typical clothing insulation.The economic performance is improved in the thermal comfort dynamic process of heating room.

Ensemble Prediction of Monsoon Index with a Genetic Neural Network Model

After the consideration of the nonlinear nature changes of monsoon index,and the subjective determination of network structure in traditional artificial neural network prediction modeling,monthly and seasonal monsoon intensity index prediction is studied in this paper by using nonlinear genetic neural network ensemble prediction（GNNEP）modeling.It differs from traditional prediction modeling in the following aspects： （1）Input factors of the GNNEP model of monsoon index were selected from a large quantity of preceding period high correlation factors,such as monthly sea temperature fields,monthly 500-hPa air temperature fields,monthly 200-hPa geopotential height fields,etc.,and they were also highly information-condensed and system dimensionality-reduced by using the empirical orthogonal function（EOF）method,which effectively condensed the useful information of predictors and therefore controlled the size of network structure of the GNNEP model.（2）In the input design of the GNNEP model,a mean generating function（MGF）series of predictand（monsoon index）was added as an input factor;the contrast analysis of results of predic- tion experiments by a physical variable predictor-predictand MGF GNNEP model and a physical variable predictor GNNEP model shows that the incorporation of the periodical variation of predictand（monsoon index）is very effective in improving the prediction of monsoon index.（3）Different from the traditional neural network modeling,the GNNEP modeling is able to objectively determine the network structure of the GNNNEP model,and the model constructed has a better generalization capability.In the case of identical predictors,prediction modeling samples,and independent prediction samples,the prediction accuracy of our GNNEP model combined with the system dimensionality reduction technique of predictors is clearly higher than that of the traditional stepwise regression model using the traditional treatment technique of predictors,suggesting that the GNNEP model opens up a vast range of possibilities for operational weather prediction.

Thermal performance of domed roof in air-conditioned spaces

Due to their thermal performance,domed roofs are one of the passive solutions that affect energy consumption in buildings.The thermal performance of domed roofs has been investigated in many naturally ventilated spaces.However,few studies have discussed their performance in conditioned spaces.Therefore,this study introduces a computational comparison between domed and flat roofs to investigate their impact on thermal comfort inside a conditioned mosque.At an earlier stage,field measurements were carried out inside a Bahraini mosque to acquire its indoor air conditions during the summer period of 2021,in addition to validating the computational model.The findings of this study confirm that,under mechanical cooling conditions,the flat roof offers a lower indoor temperature than the domed roof by 0.4℃and 0.1℃for open and closed doors,respectively.Similarly,the air velocity is lower by approximately 0.01 m/s for both door modes.The overall PMV values of the flat roof are also lower by 0.07 and 0.01,while the PPD values are lower by 0.20,and 0.34 for open and closed doors,respectively.Based on these small differences,it can be concluded that the thermal performance of both roofing systems behaves equally in conditioned spaces.However,the air patterns are substantially different,the overall thermal performance is similar.This similarity drives building designers to rethink the thermal performance of the domed roofs in air-conditioned spaces with such a hot climate,regardless of their aesthetic and acoustical behaviour.

A Novel Combined Trombe Wall System for Village Houses in Cold Regions of China

Trombe walls have significant energy-saving features and are therefore of great interest to researchers.However,additional research about the Trombe wall is needed to reduce indoor temperature fluctuations and to improve thermal behavior under different climatic conditions.A new Trombe wall system was proposed which uses Venetian blinds and a basement.The field tests were conducted to compare the thermal performance of four types of rooms:(ⅰ)no Trombe wall(control),(ⅱ)classical Trombe wall(TW),(ⅲ)Trombe wall with Venetian blinds(TW+VB),and(ⅳ)Trombe wall with Venetian blinds and a basement(TW+VB+B).The field measurements were conducted during the winter near Shihezi City in northwest China.The objective of this study was(ⅰ)to evaluate the thermal performance of a novel Trombe wall system under different operation conditions,and(ⅱ)to confirm the optimal angle of Venetian blinds during the heating period.The results demonstrated that the TW+VB+B system effectively reduced indoor temperature fluctuations after sunset.Furthermore,during the daytime,the average air temperatures in the test rooms were 13.6℃higher in the TW+VB+B system than in the control.The average temperature at the air outlet in the TW+VB+B system was 4.9℃higher than that in the TW+VB system during the daytime,and the average predicted mean vote(PMV)of the test room was 1.02 units greater in the TW+VB+B system than in the control.The thermal efficiency remains in the range of 40%-65%when the Venetian blind angle was set at 45°.In conclusion,the experiment results showed that the TW+VB+B system can not only reduce indoor temperature fluctuations but also improve thermal performance in winter.Both the heating energy consumption in buildings and pollutants emission in the environment were lessened through the application of this passive solar energy-saving technology.Therefore,this can provide valuable insights for improving the thermal performance of the novel Trombe Wall system in such village houses.

The effect of Urban Heat Island mitigation strategies on outdoor human thermal comfort in the city of Baghdad

Todays,most Iraqi cities suffer from extremely hot-dry climate for long periods throughout the year.However,most urban patterns that exist inside these cities are not suitable for this harsh conditions and lead to an increase in the value of the Urban Heat Island(UHI)index.Consequently,this will increase outdoor human thermal discomfort as well as energy consumption and air pollution in cities.This study attempts to evaluate the effect of UHI mitigation strategies on outdoor human thermal comfort in three different common types of urban patterns in the biggest and most populated city in Iraq,Baghdad.Three different mitigation strategies are used here-vegetation,cool materials,and urban geometry-to build 18 different scenarios.Three-dimensional numerical software ENVI-met 4.2 is utilised to analyse and assess the studied parameters.The input data for simulations process are based on two meteorological stations in Baghdad:Iraqi Meteorological Organization&Seismology,and Iraqi Agrometeorological Network.All measurements are taken in a pedestrian walkway.The results of different scenarios are compared based on their effect on human thermal comfort.Outdoor thermal comfort is assessed according to Predicted Mean Vote index,as mentioned in ISO 7730 standard.This study provides a better understanding of the role of UHI mitigation strategies on human thermal comfort in the outdoor spaces of Baghdad’s residential neighbourhoods.This can help generate guidelines of urban design and planning practices for better thermal performance in hot and dry cities.

Experimental study of mean overtopping discharge at perforated caissons for non-impulsive waves

In this study, hydraulic model tests are carried out to investigate the mean overtopping discharge at perforated caisson breakwaters for non-impulsive waves. Based on the experimental data, the mean overtopping discharges of perforated and nonperforated caissons are compared. It is found that when the relative crest freeboard is smaller than 1.6, the mean overtopping discharge of a breakwater can be reduced by at least half by using perforated caissons with 35% porosity instead of nonperforated caissons. The effects of the relative crest freeboard, the caisson porosity and perforation shape, the relative wave chamber width and the relative water depth on the mean overtopping discharge at perforated caissons are clarified. Then,predictive formulas for the mean overtopping discharge at perforated caissons are developed. The predictive formulas based on the experimental data are valid in a wide range of the relative crest freeboard and involve the effects of the caisson porosity and the relative water depth. The predictive formulas developed in this study are of significance for the hydraulic design of perforated caissons.

Residential building performance analysis at near extreme weather conditions in Hong Kong through a thermal-comfort-based strategy

The precise building performance assessment of residential housings in subtropical regions is usually more difficult than that for the commercial premises due to the much more complicated behavior of the occupants with regard to the change in indoor temperature.The conventional use of a fixed schedule for window opening,clothing insulation and cooling equipment operation cannot reflect the real situation when the occupants respond to the change in thermal comfort,thus affecting the appropriateness of the assessment results.To rectify the situation,a new modeling strategy in which the modification of the various operation schedules was based on the calculated thermal comfort(TC),was developed in this study.With this new TC-based strategy,the realistic building performances under different cooling provision scenarios applied to a high-rise residential building under the near extreme weather conditions were investigated and compared.It was found that sole provision of ventilation fans could not meet the zone thermal comfort by over 68%of the time,and air-conditioning was essential.The optimal use of ventilation fans for cooling could only help reduce the total cooling energy demand by less than 12%at best which could only be realistically evaluated by adopting the present strategy.Parametric studies were conducted which revealed that some design factors could offer opportunities for reducing the total cooling energy under the near extreme weather conditions.

Predicted Mean Vote of Subway Car Environment Based on Machine Learning

The thermal comfort of passengers in the carriage cannot be ignored.Thus,this research aims to establish a prediction model for the thermal comfort of the internal environment of a subway car and find the optimal input combination in establishing the prediction model of the predicted mean vote(PMV)index.Data-driven modeling utilizes data from experiments and questionnaires conducted in Nanjing Metro.Support vector machine(SVM),decision tree(DT),random forest(RF),and logistic regression(LR)were used to build four models.This research aims to select the most appropriate input variables for the predictive model.All possible combinations of 11 input variables were used to determine the most accurate model,with variable selection for each model comprising 102350 iterations.In the PMV prediction,the RF model was the best when using the correlation coefficients square(R2)as the evaluation indicator(R^(2):0.7680,mean squared error(MSE):0.2868).The variables include clothing temperature(CT),convective heat transfer coefficient between the surface of the human body and the environment(CHTC),black bulb temperature(BBT),and thermal resistance of clothes(TROC).The RF model with MSE as the evaluation index also had the highest accuracy(R^(2):0.7676,MSE:0.2836).The variables include clothing surface area coefficient(CSAC),CT,BBT,and air velocity(AV).The results show that the RF model can efficiently predict the PMV of the subway car environment.