A study on the numerical prediction method for the vertical thermal structure in the Bohai Sea and the Huanghai Sea-I.One-dimensional numerical prediction model

In this paper, on the basis of the heat conduction equation without consideration of the advection and turbulence effects, one-dimensional model for describing surface sea temperature ( T1), bottom sea temperature ( Tt ) and the thickness of the upper homogeneous layer ( h ) is developed in terms of the dimensionless temperature θT and depth η and self-simulation function θT - f(η) of vertical temperature profile by means of historical temperature data.The results of trial prediction with our one-dimensional model on T, Th, h , the thickness and gradient of thermocline are satisfactory to some extent.

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.

Surrogate modeling for long-term and high-resolution prediction of building thermal load with a metric-optimized KNN algorithm

During the pre-design stage of buildings,reliable long-term prediction of thermal loads is significant for cool-ing/heating system configuration and efficient operation.This paper proposes a surrogate modeling method to predict all-year hourly cooling/heating loads in high resolution for retail,hotel,and office buildings.16384 surrogate models are simulated in EnergyPlus to generate the load database,which contains 7 crucial building features as inputs and hourly loads as outputs.K-nearest-neighbors(KNN)is chosen as the data-driven algorithm to approximate the surrogates for load prediction.With test samples from the database,performances of five different spatial metrics for KNN are evaluated and optimized.Results show that the Manhattan distance is the optimal metric with the highest efficient hour rates of 93.57%and 97.14%for cooling and heating loads in office buildings.The method is verified by predicting the thermal loads of a given district in Shanghai,China.The mean absolute percentage errors(MAPE)are 5.26%and 6.88%for cooling/heating loads,respectively,and 5.63%for the annual thermal loads.The proposed surrogate modeling method meets the precision requirement of engineering in the building pre-design stage and achieves the fast prediction of all-year hourly thermal loads at the district level.As a data-driven approximation,it does not require as much detailed building information as the commonly used physics-based methods.And by pre-simulation of sufficient prototypical models,the method overcomes the gaps of data missing in current data-driven methods.

Does back cooling improve human thermal comfort in warm environments?A device for heat conduction by the semiconductor Peltier effect

The hot environment and the metabolic heat of commuting in summer caused individual overheating and intense thermal discomfort.Local cooling presents huge potential for optimizing thermal comfort.This study investigates the performance of a back cooling device,based on the semiconductor Peltier effect,in improving thermal comfort after summer commuting.We studied one case without cooling,and three cases with surface temperatures of the cooling device of 29,27,and 25℃using a simulated summer commute at a moderate activity level.The results showed that thermal sensation,perceived sweating rate,and skin temperature decreased markedly in the cooling cases compared to the non-cooling case,with the changes being most notable in the lower back,in contact with the cooling device.The decrease in overall thermal sensation and mean skin temperature was approximately 0.52 score and 0.31℃on average,respectively,with a 1.71 score increase in overall thermal comfort.We contend that the surface temperature of local contact cooling devices should not be lower than 22℃to minimize local overcooling.Back cooling devices present a huge potential for building energy-savings at ambient air temperature exceeding 30℃.Moreover,the functional paradigms for individual comfort predict improved comfort performance in future applications.This study contributes to the understanding on the well-being and physiological recovery of individuals after a summer commuting.