scholarly journals Analysis of the Influence Subjective Human Parameters in the Calculation of Thermal Comfort and Energy Consumption of Buildings

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1531 ◽  
Author(s):  
Roberto Robledo-Fava ◽  
Mónica C. Hernández-Luna ◽  
Pedro Fernández-de-Córdoba ◽  
Humberto Michinel ◽  
Sonia Zaragoza ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Energy Consumption ◽  
Monte Carlo Method ◽  
Numerical Model ◽  
Thermal Comfort ◽  
Energy Demand ◽  
Measured Data ◽  
Point Temperature ◽  
Set Point ◽  
Indoor Comfort

In the present work, we analyze the influence of the designer’s choice of values for the human metabolic index (met) and insulation by clothing (clo) that can be selected within the ISO 7730 for the calculation of the energy demand of buildings. To this aim, we first numerically modeled, using TRNSYS, two buildings in different countries and climatologies. Then, we consistently validated our simulations by predicting indoor temperatures and comparing them with measured data. After that, the energy demand of both buildings was obtained. Subsequently, the variability of the set-point temperature concerning the choice of clo and met, within limits prescribed in ISO 7730, was analyzed using a Monte Carlo method. This variability of the interior comfort conditions has been finally used in the numerical model previously validated, to calculate the changes in the energy demand of the two buildings. Therefore, this work demonstrated that the diversity of possibilities offered by ISO 7730 for the choice of clo and met results, depending on the values chosen by the designer, in significant differences in indoor comfort conditions, leading to non-negligible changes in the calculations of energy consumption, especially in the case of big buildings.

scholarly journals Stochastic energy-demand analyses with random input parameters for the single-family house

2021 ◽  
Author(s):  
Marcin Koniorczyk ◽  
Witold Grymin ◽  
Marcin Zygmunt ◽  
Dalia Bednarska ◽  
Alicja Wieczorek ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Energy Consumption ◽  
Monte Carlo Method ◽  
Energy Demand ◽  
Stochastic Perturbation ◽  
Random Variable ◽  
Single Family ◽  
The Monte Carlo Method ◽  
Stochastic Perturbation Method ◽  
Family House

AbstractIn the analyses of the uncertainty propagation of buildings’ energy-demand, the Monte Carlo method is commonly used. In this study we present two alternative approaches: the stochastic perturbation method and the transformed random variable method. The energy-demand analysis is performed for the representative single-family house in Poland. The investigation is focused on two independent variables, considered as uncertain, the expanded polystyrene thermal conductivity and external temperature; however the generalization on any countable number of parameters is possible. Afterwards, the propagation of the uncertainty in the calculations of the energy consumption has been investigated using two aforementioned approaches. The stochastic perturbation method is used to determine the expected value and central moments of the energy consumption, while the transformed random variable method allows to obtain the explicit form of energy consumption probability density function and further characteristic parameters like quantiles of energy consumption. The calculated data evinces a high accordance with the results obtained by means of the Monte Carlo method. The most important conclusions are related to the computational cost reduction, simplicity of the application and the appropriateness of the proposed approaches for the buildings’ energy-demand calculations.

scholarly journals Trade-off between Energy Consumption and Comfort Experience in Smart Buildings

2015 ◽  
Vol 44 (4) ◽  
pp. 420-432 ◽  
Author(s):  
Domen Zupančič ◽  
Mitja Luštrek ◽  
Matjaž Gams
Keyword(s):  
Energy Consumption ◽  
Thermal Comfort ◽  
Prediction Algorithm ◽  
Reactive Control ◽  
Indoor Temperature ◽  
Point Temperature ◽  
Set Point ◽  
Multi Agent ◽  
The Difference ◽  
Occupancy Prediction

The thermal comfort experience in conditioned environments is closely related to the indoor temperature andvaries mainly due to the dynamics of occupant state and the environmental state. The heating or cooling required to achievethe desired temperature and comfort influences the energy consumption. This article presents a multi-agent control systemthat primarily regulates thermal comfort rather than the indoor temperature. We developed this comfort regulator that basedon (i) the difference between the desired level of comfort and the current level of comfort and (ii) the difference between thecurrent temperature and the set point temperature adapts the set point temperature in order to achieve the desired comfort.An occupancy prediction algorithm and expert rules were designed to efficiently reduce unnecessary energy consumptionduring periods when the home is not occupied and the comfort experience is therefore not important. The results of experimentsare presented in a comfort/energy-consumption space. The comfort/energy-consumption space shows how the finalresult is influenced by (i) different versions of learning algorithms and (ii) different comfort threshold values. Comparingthe comfort/energy-consumption spaces for different occupancy patterns shows that the rule settings have similar impact oncontrol performance, which indicates that the rules are general. In nearly all experiments, the proposed multi-agent controlsystem assured better comfort experience with small increase of energy consumption compared to reactive control system.DOI: http://dx.doi.org/10.5755/j01.itc.44.4.10139

scholarly journals The Derivation of Cooling Set-Point Temperature in an HVAC System, Considering Mean Radiant Temperature

2019 ◽  
Vol 11 (19) ◽  
pp. 5417
Author(s):  
Jinmog Han ◽  
Jongkyun Bae ◽  
Jihoon Jang ◽  
Jumi Baek ◽  
Seung-Bok Leigh
Keyword(s):  
Thermal Comfort ◽  
Indoor Environment ◽  
Thermal Characteristics ◽  
Weather Conditions ◽  
System Control ◽  
Hvac System ◽  
Mean Radiant Temperature ◽  
Point Temperature ◽  
Set Point ◽  
Radiant Temperature

Heating, ventilation, and air-conditioning (HVAC) systems usually have a set-point temperature control feature that uses the indoor dry-bulb temperature to control the indoor environment. However, an incorrect set-point temperature can reduce thermal comfort and result in unnecessary energy consumption. This study focuses on a derivation method for the optimal cooling set-point temperature of an HVAC system used in office buildings, considering the thermal characteristics and daily changes in the weather conditions, to establish a comfortable indoor environment and minimize unnecessary energy consumption. The operative temperature is used in the HVAC system control, and the mean radiant temperature is predicted with 94% accuracy through a multiple regression analysis by applying the indoor thermal environment data and weather information. The regression equation was utilized to create an additional equation to calculate the optimal set-point temperature. The simulation results indicate that the HVAC system control with the new set-point temperatures calculated from the derived equation improves thermal comfort by 38.5% (26%p). This study confirmed that a cooling set-point temperature that considers both the thermal characteristics of a building and weather conditions is effective in enhancing the indoor thermal comfort during summer.

scholarly journals Green Roofs as Effective Tools for Improving the Indoor Comfort Levels of Buildings—An Application to a Case Study in Sicily

2020 ◽  
Vol 10 (3) ◽  
pp. 893 ◽  
Author(s):  
Laura Cirrincione ◽  
Maria La Gennusa ◽  
Giorgia Peri ◽  
Gianfranco Rizzo ◽  
Gianluca Scaccianoce ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Electric Energy ◽  
Simulation Models ◽  
Green Roofs ◽  
Energy Performance ◽  
Comfort Levels ◽  
Indoor Comfort ◽  
The Impact

In the line of pursuing better energy efficiency in human activities that would result in a more sustainable utilization of resources, the building sector plays a relevant role, being responsible for almost 40% of both energy consumption and the release of pollutant substances in the atmosphere. For this purpose, techniques aimed at improving the energy performances of buildings’ envelopes are of paramount importance. Among them, green roofs are becoming increasingly popular due to their capability of reducing the (electric) energy needs for (summer) climatization of buildings, hence also positively affecting the indoor comfort levels for the occupants. Clearly, reliable tools for the modelling of these envelope components are needed, requiring the availability of suitable field data. Starting with the results of a case study designed to estimate how the adoption of green roofs on a Sicilian building could positively affect its energy performance, this paper shows the impact of this technology on indoor comfort and energy consumption, as well as on the reduction of direct and indirect CO2 emissions related to the climatization of the building. Specifically, the ceiling surface temperatures of some rooms located underneath six different types of green roofs were monitored. Subsequently, the obtained data were used as input for one of the most widely used simulation models, i.e., EnergyPlus, to evaluate the indoor comfort levels and the achievable energy demand savings of the building involved. From these field analyses, green roofs were shown to contribute to the mitigation of the indoor air temperatures, thus producing an improvement of the comfort conditions, especially in summer conditions, despite some worsening during transition periods seeming to arise.

scholarly journals Prediction of Cooling Energy Consumption Using a Neural Network on the Example of the Hotel Building

Proceedings ◽  
2020 ◽  
Vol 58 (1) ◽  
pp. 21
Author(s):  
Marek Borowski ◽  
Klaudia Zwolińska
Keyword(s):  
Energy Consumption ◽  
Multilayer Perceptron ◽  
Energy Demand ◽  
Weather Conditions ◽  
Demand Model ◽  
Factors Affecting ◽  
Actual Energy Consumption ◽  
Indoor Comfort ◽  
The Impact ◽  
Cooling Energy Demand

The purpose of this work is to determine internal and external factors affecting the cooling energy demand of a building. During the research, the impact of weather conditions and the level of hotel occupancy on cooling energy, which is necessary to obtain indoor comfort conditions, was analyzed. The subject of research is energy consumption in the Turówka hotel located in Wieliczka (southern Poland). In the article, the designer of neural networks was used in the Statistica statistical package. To design the network, a widely used multilayer perceptron model with an algorithm with backward error propagation was used. Based on the collected input and output data, various multilayer perceptron (MLP) networks were tested to determine the relationship most accurately reflecting actual energy consumption. Based on the results obtained, factors that significantly affect the consumption of thermal energy in the building were determined, and a predictive energy demand model for the analyzed object was presented. The result of the work is a forecast of cooling energy demand, which is particularly important in a hotel facility. The prepared predictive model will enable proper energy management in the facility, which will lead to reduced consumption and thus costs related to facility operation.

Sensitivity Analysis of Manufacture Errors for Cable Dome

2010 ◽  
Vol 163-167 ◽  
pp. 822-827
Author(s):  
Jian Hua Zhang ◽  
Zhen Qing Wang ◽  
Yi Gang Zhang
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Numerical Model ◽  
Practical Problem ◽  
Structural Performance ◽  
Random Errors ◽  
Cable Length ◽  
Radial Bearing ◽  
Cable Dome

Construction control is the key technique for cable dome. During the actual engineering, however, it is difficult to ensure no difference between the real prestress distribution and the designed value due to existed construction errors. The practical problem for cable dome construction is to determine how much is allowable errors limit. To solve the problem, sensitivity analysis of manufacture errors of bearing and cable length are carried out in this paper. The random errors are simulated by Monte Carlo method. In a numerical model of cable dome with the diameter of 62m, the sensitivity of effect of bearing and cable length manufacture errors on structural performance is analyzed. Numerical results show that the manufacture errors of radial bearing, hoop cable 2, ridge cable 3, diagonal cable 3 and hoop cable 1 should be strictly controlled. Besides, the maximum allowable errors are proposed referring to the domestic existing specifications.

Effect of Indoor Thermal Environment on Building Energy Consumption

2012 ◽  
Vol 193-194 ◽  
pp. 137-141
Author(s):  
Kun Cai ◽  
Zheng Dong Chen ◽  
Xue Bin Yang ◽  
Yao Fen Zhang ◽  
Ming Xue Li
Keyword(s):  
Energy Consumption ◽  
Indoor Air ◽  
Thermal Environment ◽  
Building Energy ◽  
Indoor Environments ◽  
Air Velocity ◽  
Point Temperature ◽  
Set Point ◽  
Thermal Environments ◽  
Neutral Temperature

This study reviews some published literatures to seek the relationship between the parameters of indoor environments and the energy consumption. The indoor thermal environments are categorized and defined as different indices and variables. The building energy can be determined by indoor air temperature, occupant-area ratio and working days. Several parameters of indoor thermal environments such as air velocity, neutral temperature, predicted mean vote, indoor air quality, and set point temperature, are summarized for their influence on the energy consumption. It can be concluded that the increased local air velocity, enhanced neutral temperature, and enlarged set point temperature may be beneficial to reduce the energy consumption.

scholarly journals A Stackelberg Game Approach for Price Response Coordination of Thermostatically Controlled Loads

2018 ◽  
Vol 8 (8) ◽  
pp. 1370 ◽  
Author(s):  
Peng Wang ◽  
Suli Zou ◽  
Xiaojuan Wang ◽  
Zhongjing Ma
Keyword(s):  
Electricity Market ◽  
Energy Demand ◽  
Stackelberg Game ◽  
Electric Energy ◽  
Stackelberg Games ◽  
Control Center ◽  
Point Temperature ◽  
Set Point ◽  
Price Response ◽  
Thermostatically Controlled Loads

In this paper, we study the demand response of the thermostatically controlled loads (TCLs) to control their set-point temperatures by considering the tradeoff between the electricity payment and TCL user’s comfort preference. Based upon the dynamics of the TCLs, we set up the relationship between the set-point temperature and the energy demand. Then, we define a discomfort function with respect to the associated energy demand which represents the discomfort level of the set-point temperature. More specifically, the system is equipped with a coordinator named electric energy control center (EECC) which can buy energy resources from the electricity market and sell them to TCL users. Due to the interaction between EECC and TCL users, we formulate the specific energy trading process as a one-leader multiple-follower Stackelberg game. As the main contributions of this work, we show the existence and uniqueness of the equilibrium for the underlying Stackelberg games, and develop a DR algorithm based on the so-called Backward Induction to achieve the equilibrium. Several numerical simulations are presented to verify the developed results in this work.

scholarly journals A New Methodology for Decision-Making in Buildings Energy Optimization

2020 ◽  
Vol 10 (13) ◽  
pp. 4558 ◽  
Author(s):  
Antonio E. Masdías-Bonome ◽  
José A. Orosa ◽  
Diego Vergara
Keyword(s):  
Decision Making ◽  
Monte Carlo ◽  
Energy Consumption ◽  
Monte Carlo Method ◽  
Probability Distribution ◽  
Optimal Conditions ◽  
Iterative Calculation ◽  
Reduce Energy Consumption ◽  
The One

When designing or retrofitting a building, not too many tools let architects and engineers to define the optimal conditions to reduce energy consumption with the minimal economic investment. This is because different software resources must be employed and an iterative calculation must be done which, most of times, is not possible. The present study aims to define an original methodology that let researchers and architects to select the best option between different possibilities. To reach this objective, Monte Carlo method is employed on the ISO 13790 standard reaching the probability distribution of the energy consumption of each building after each possible modification. From main results, two mathematical models were obtained from a real case study showing the relation between annual energy consumption and economic investment of each different building retrofits. What is more, in disagreement with the expected result, the best retrofit option was not the one with the highest cost and qualities. In conclusion, this methodology can be a useful tool for researchers and professionals to improve their decision-making.

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