Test and Analysis of Indoor Thermal Environment of Urban Heating Residential Building in Hanzhong Area

By testing indoor and outdoor thermal environment of residential buildings that apply 4 mostused heating ways in Hantai District,Hanzhong City,this paper explored the indoor thermal environment conditions of different heating ways,to provide references for choosing a suitable heating way in the local area.

Feasibility of Hybrid Wind and Solar Energy System for Heating Residential Buildings in Amman-Jordan

A design of a solar-wind electrical hybrid system to supply space heating requirements for a 1,200 m^2 residential building in Amman-Jordan was implemented. The building heating requirements were estimated from existing heating building data based on traditional heating design already adopted by engineering firms in Jordan. The traditional heating load was transferred into electrical load to be supplied by hybrid system. The hybrid system consists of a 75 kW vertical axis windmill and 140 solar modules. Because of the high cost of land in residential buildings, the hybrid system is to be installed on the building roof. The hybrid system and the conventional systems＇ cost were found to be compatible in four years period when oil prices reach $100 per barrel. As the international price of oil rises above $100 per barrel, the proposed hybrid system becomes more economical than the already existing hot water heating system.

Comparison of life cycle performance of distributed energy system and conventional energy system for district heating and cooling in China

The distributed energy system has achieved significant attention in respect of its application for singlebuilding cooling and heating.Researching on the life cycle environmental impact of distributed energy systems(DES)is of great significance to encourage and guide the development of DES in China.However,the environmental performance of distributed energy systems in a building cooling and heating has not yet been carefully analyzed.In this study,based on the standards of ISO14040-2006 and ISO14044-2006,a life-cycle assessment(LCA)of a DES was conducted to quantify its environmental impact and a conventional energy system(CES)was used as the benchmark.GaBi 8 software was used for the LCA.And the Centre of Environmental Science(CML)method and Eco-indicator 99(EI 99)method were used for environmental impact assessment of midpoint and endpoint levels respectively.The results indicated that the DES showed a better life-cycle performance in the usage phase compared to the CES.The life-cycle performance of the DES was better than that of the CES both at the midpoint and endpoint levels in view of the whole lifespan.It is because the CES to DES indicator ratios for acidification potential,eutrophication potential,and global warming potential are 1.5,1.5,and 1.6,respectively at the midpoint level.And about the two types of impact indicators of ecosystem quality and human health at the endpoint level,the CES and DES ratios of the other indicators are greater than1 excepting the carcinogenicity and ozone depletion indicators.The human health threat for the DES was mainly caused by energy consumption during the usage phase.A sensitivity analysis showed that the climate change and inhalable inorganic matter varied by 1.3%and 6.1%as the electricity increased by 10%.When the natural gas increased by 10%,the climate change and inhalable inorganic matter increased by 6.3%and 3.4%,respectively.The human health threat and environmental damage caused by the DES could be significantly reduced by the optimization of natural gas and electricity consumption.

Optimal Thermal Insulation Thickness in Isolated Air-Conditioned Buildings and Economic Analysis

The removal building heat load and electrical power consumption by air conditioning system are proportional to the outside conditions and solar radiation intensity. Building construction materials has substantial effects on the transmission heat through outer walls, ceiling and glazing windows. Good thermal isolation for buildings is important to reduce the transmitted heat and consumed power. The buildings models are constructed from common materials with 0 - 16 cm of thermal insulation thickness in the outer walls and ceilings, and double-layers glazing windows. The building heat loads were calculated for two types of walls and ceiling with and without thermal insulation. The cooling load temperature difference method, CLTD, was used to estimate the building heat load during a 24-hour each day throughout spring, summer, autumn and winter seasons. The annual cooling degree-day, CDD was used to estimate the optimal thermal insulation thickness and payback period with including the solar radiation effect on the outer walls surfaces. The average saved energy percentage in summer, spring, autumn and winter are 35.5%, 32.8%, 33.2% and 30.7% respectively, and average yearly saved energy is about of 33.5%. The optimal thermal insulation thickness was obtained between 7 - 12 cm and payback period of 20 - 30 month for some Egyptian Cities according to the Latitude and annual degree-days.

Low-carbon building heating system coupled with semiconductor radiation heating and distributed PV:A simulation analysis in two Chinese climate zones

Building is an important scenario for achieving global carbon peak and carbon neutrality goals,accounting for approximately 37%of global energy-related CO_(2) emissions in 2020.In the meanwhile,the construction and operation of buildings was responsible for 36%of global energy consumption,of which 30%energy was used for space heating.Therefore,this paper proposes a low-carbon building heating system that is coupled to a new semiconductor radiation heating unit and distributed rooftop photovoltaic to reduce carbon emissions.To reveal its building heating characteristics,a dynamic model of heat transfer based on semiconductor low-temperature radiant heating is first established by analyzing the heat conduction,convection,and radiation models,and the uncertainty from both the distributed rooftop photovoltaic and building heating demand is considered in the building heating operation strategy.Then,a simulation model of a low-carbon building heating system is built in MATLAB/SIMULINK for two different climate zones in China(Beijing and Wuhan).When building and using the low-carbon building heating system stable for 30 years,the payback period is 5.2–8.2 years in Beijing and 6.4–11.6 years in Wuhan.Compared with the traditional grid-powered heating system,the simulation revealed that the carbon emissions of Beijing and Wuhan during the heating season are reduced by 44.9%and 44.3%,respectively,and the corresponding building heating cost is saved by 62.1%and 57.8%.

Simulation of roof snow loads based on a multi-layer snowmelt model:Impact of building heat transfer

To investigate the impact of building heat transfer on roof snow loads,roof snow loads and snow load thermal coefficients from 61 Chinese sites over a period of 50 years are simulated based on basic meteorological data such as temperature,humidity,wind speed,and precipitation,and a multi-layer snowmelt model considering the building heat transfer.Firstly,the accuracy of the multi-layer snowmelt model is validated using the data of observed ground snow load and roof snow melting tests.The relationship between meteorological conditions,snow cover characteristics,and thermal coefficients of snow loads in three representative sites is then studied.Furthermore,the characteristics of thermal coefficients in each zone are analyzed by combining them with the statistical results of meteorological data from 1960 to 2010,and the equations of thermal coefficients in different zones on indoor temperatures and roof heat transfer coefficients are fitted separately.Finally,the equations in this paper are compared with the thermal coefficients in the main snow load codes.The results indicate that the snowmelt model using basic meteorological data can effectively provide samples of roof snow loads.In the cold zone where the snow cover lasts for a long time and does not melt easily,the thermal coefficients of the snow loads on the heating buildings are lower than those in the warm zone due to the long-term influence of the heat from inside the buildings.Thermal coefficients are negatively correlated with indoor temperatures and roof heat transfer coefficients.When the indoor temperature is too low or the roof insulation is good,the roof snow load may exceed the ground snow load.The thermal coefficients for heated buildings in the main snow load codes are more conservative than those calculated in this paper,and the thermal coefficients for buildings with lower indoor temperatures tend to be smaller.

Reinforcement Learning Model for Energy System Management to Ensure Energy Efficiency and Comfort in Buildings

This article focuses on the challenges ofmodeling energy supply systems for buildings,encompassing both methods and tools for simulating thermal regimes and engineering systems within buildings.Enhancing the comfort of living or working in buildings often necessitates increased consumption of energy and material,such as for thermal upgrades,which consequently incurs additional economic costs.It is crucial to acknowledge that such improvements do not always lead to a decrease in total pollutant emissions,considering emissions across all stages of production and usage of energy and materials aimed at boosting energy efficiency and comfort in buildings.In addition,it explores the methods and mechanisms for modeling the operating modes of electric boilers used to collectively improve energy efficiency and indoor climatic conditions.Using the developed mathematical models,the study examines the dynamic states of building energy supply systems and provides recommendations for improving their efficiency.These dynamic models are executed in software environments such as MATLAB/Simscape and Python,where the component detailing schemes for various types of controllers are demonstrated.Additionally,controllers based on reinforcement learning(RL)displayed more adaptive load level management.These RL-based controllers can lower instantaneous power usage by up to 35%,reduce absolute deviations from a comfortable temperature nearly by half,and cut down energy consumption by approximately 1%while maintaining comfort.When the energy source produces a constant energy amount,the RL-based heat controllermore effectively maintains the temperature within the set range,preventing overheating.In conclusion,the introduced energydynamic building model and its software implementation offer a versatile tool for researchers,enabling the simulation of various energy supply systems to achieve optimal energy efficiency and indoor climate control in buildings.

Enhancing hourly heat demand prediction through artificial neural networks:A national level case study

Meeting the goal of zero emissions in the energy sector by 2050 requires accurate prediction of energy consumption,which is increasingly important.However,conventional bottom-up model-based heat demand forecasting methods are not suitable for large-scale,high-resolution,and fast forecasting due to their complexity and the difficulty in obtaining model parameters.This paper presents an artificial neural network(ANN)model to predict hourly heat demand on a national level,which replaces the traditional bottom-up model based on extensive building simulations and computation.The ANN model significantly reduces prediction time and complexity by reducing the number of model input types through feature selection,making the model more realistic by removing non-essential inputs.The improved model can be trained using fewer meteorological data types and insufficient data,while accurately forecasting the hourly heat demand throughout the year within an acceptable error range.The model provides a framework to obtain accurate heat demand predictions for large-scale areas,which can be used as a reference for stakeholders,especially policymakers,to make informed decisions.

The role of heat pump in heating decarbonization for China carbon neutrality

Heating decarbonization is a major challenge for China to meet its 2060 carbon neutral commitment,yet most existing studies on China’s carbon neutrality focus on supply side(e.g.,grid decarbonization,zero-carbon fuel)rather than demand side(e.g.,heating and cooling in buildings and industry).In terms of end use energy consumption,heating and cooling accounts for 50% of the total energy consumption,and heat pumps would be an effective driver for heating decarbonization along with the decarbonization on power generation side.Previous study has discussed the underestimated role of the heat pump in achieving China’s goal of carbon neutrality by 2060.In this paper,various investigation and assessments on heat pumps from research to applications are presented.The maximum decarbonization potential from heat pump in a carbon neutral China future could reach around 1532Mton and 670Mton for buildings and industrial heating respectively,which show nearly 2 billion tons CO_(2) emission reduction,20% current CO_(2) emission in China.Moreover,a region-specific technology roadmap for heat pump development in China is suggested.With collaborated efforts from government incentive,technology R&D,and market regulation,heat pump could play a significant role in China’s 2060 carbon neutrality.

Sensitivity analysis to reduce duplicated features in ANN training for district heat demand prediction

Artificial neural network(ANN)has become an important method to model the nonlinear relationships between weather conditions,building characteristics and its heat demand.Due to the large amount of training data re-quired for ANN training,data reduction and feature selection are important to simplify the training.However,in building heat demand prediction,many weather-related input variables contain duplicated features.This paper develops a sensitivity analysis approach to analyse the correlation between input variables and to detect the variables that have high importance but contain duplicated features.The proposed approach is validated in a case study that predicts the heat demand of a district heating network containing tens of buildings at a university campus.The results show that the proposed approach detected and removed several unnecessary input variables and helped the ANN model to reduce approximately 20%training time compared with the traditional methods while maintaining the prediction accuracy.It indicates that the approach can be applied for analysing large num-ber of input variables to help improving the training efficiency of ANN in district heat demand prediction and other applications.

Distribution Characteristics of Transmitted Diffuse Solar Radiation on the Indoor Surface

The transparent envelope structure has huge energy-saving potential, which is the key point to reduce building energy consumption and improve the thermal building environment. The solar radiation transmitted through the transparent envelope structure(transmitted solar radiation) is reflected, scattered and absorbed by the indoor surface, which has a significant impact on the heat gain of the building. In this paper, firstly, the diffuse radiation received by different depths of various indoor surfaces is measured by experimental tests, and the distribution function of transmitted diffuse solar radiation(TDSR) on the indoor surface is established. Secondly, the diffuse solar radiation received by the indoor and outdoor surfaces in different seasons is continuously monitored;the variation of TDSR with time is analyzed, and the distribution function of TDSR on indoor surface with time is proposed. Finally, based on the temporal and spatial distribution characteristics of diffuse radiation under different weather conditions, the variation of TDSR with the weather is studied, and the distribution function of TDSR on the indoor surface with weather changes is established. The distribution function of the TDSR on the indoor surface under different depths, time and weather conditions obtained in this study can supplement and improve the theory of building heat gain and load, and help accurate simulation of building energy consumption.

The heat-pump market in Italy:an in-depth economic study about the reasons for a still unexpressed potential

In this paper,the Italian heat-pump(HP)market is presented,with an overview over the past 10 years.In order to highlight market potential and barriers,a comparison is proposed between the economic performances of two different heating and domestic hot-water systems,air-to-water HPs and condensing boilers,based on several factors,such as energy costs,thermal loads,climatic conditions,HP-performance classes and some economic indicators such as the payback time and the interest rate.The results are presented in a parametric form,which may be profitably used for a comparative analysis with other European countries.The first part of the paper deals with the analysis of the current Italian HP market,to show its still unexpressed potential.The second part analyses the HP economic convenience with respect to the most commonly used heating technology,i.e.the gas boiler,under conditions typical of the Italian climate.The comparison is carried out in terms of two economic indicators:additional acceptable cost and net present value.The main results show that HP technology is economically competitive in most Italian climatic zones,with a strong dependence on the HP-performance class.In particular,if the best-performing class was adopted,economic gains would be guaranteed over the gas boiler,even with significant variations in the main influencing variables.Thus,the economic issue does not seem to be a limiting factor for HP technology diffusion,at least if the current incentives are maintained.Rather,some other barriers should be removed,such as the supply chain,the training of installation personnel and the final-user awareness.