The use of simplified weather data to estimate thermal loads of non-residential buildings

Westphal, Fernando Simon; Lamberts, Roberto

doi:10.1016/j.enbuild.2004.01.007

Cited by 37 publications

(17 citation statements)

References 2 publications

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“…In the United Kingdom, the building sector consumes about 50% of all the country's energy [9]. In Brazil, 48% of the national energy is consumed in buildings [10], while in China, building sector currently accounts for 23% of the country total energy use [11]. The same situation can be seen in the UAE.…”

Section: Energy Consumption and Co 2 Emissionssupporting

confidence: 55%

On the Effect of Global Warming and the UAE Built Environment

Radhi¹

2010

Global Warming

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Section: Energy Consumption and Co 2 Emissionssupporting

confidence: 55%

On the Effect of Global Warming and the UAE Built Environment

Radhi¹

2010

Global Warming

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“…1.0249 0.0185 load out wind solar Q T = (10) where the square correlation coefficient 2 R between the sample data and estimated data is about 0.8616 ( Fig. 9).…”

Section: Multiple Regression Model Based On Weather Factorsmentioning

confidence: 93%

“…In 2004, Fernando Simon Westphal and Roberto Lamberts [10] used different weather cycles and weather data to estimate the non-residential buildings' heat load in a DHS in Brazil, they found that the heat load's estimated results were mainly influenced by the selections of different weather cycles and data, and the mean relative error was about 18%.…”

Section: Introductionmentioning

confidence: 99%

Multiple Regression Model Based on Weather Factors for Predicting The Heat Load of A District Heating System in Dalian, China—A Case Study

Cai¹,

Wang²,

Wang³

2015

TOCSJ

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The managers need to have a reasonable guide on the operation and management in district heating system (DHS), so it's very necessary to predicting the heat load for DHS. In this paper, the relationships between the heat load and weather conditions have been researched in order to determine the inputs variables and output variable of the future heat load prediction model. Using the given data from the obtained database, the multiple regression modelling and analysis method was carried out so as to establish the corresponding heat load prediction models for the DHS. The results shown that the square correlation coefficient between the heat load's measured value and estimated value are all greater than 0.9000, and the mean absolute percentage error (MAPE) between the heat load's measured value and estimated value are all less than 4.00%. Moreover, the corresponding maximum absoult relative errors between the heat load's measured value and estimated value are all less than 8%. The results also indicated that the heat load prediction model's accuracy is relatively high. Furthermore, these 5 heat load prediction models can be applied in the real DHS and this multiple regression method can be promoted into the other engineering field.This is an open access article licensed under the terms of the (https://creativecommons.org/licenses/by/4.0/legalcode), which permits unrestricted, noncommercial use, distribution and reproduction in any medium, provided the work is properly cited.

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“…In general, the heat load is calculated as a steady-state value, which is not consistent with the actual value. Westphal and Lamberts presented a transfer function method to analyze the dynamic heat load of nonresidential buildings based on simplified meteorological data [3]. A model and corresponding computer code, which are based on the accurate high-order numerical solution of the transient energy equation and the hydraulic prediction of pressure and fluid flow rates within the complex pipe network, are developed to simulate the thermal transients in local heating systems [4].…”

Section: System Performance and Influencing Factors Of Dynamicmentioning

confidence: 99%

Dynamic Heat Supply Prediction Using Support Vector Regression Optimized by Particle Swarm Optimization Algorithm

Wang

2016

Mathematical Problems in Engineering

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We developed an effective intelligent model to predict the dynamic heat supply of heat source. A hybrid forecasting method was proposed based on support vector regression (SVR) model-optimized particle swarm optimization (PSO) algorithms. Due to the interaction of meteorological conditions and the heating parameters of heating system, it is extremely difficult to forecast dynamic heat supply. Firstly, the correlations among heat supply and related influencing factors in the heating system were analyzed through the correlation analysis of statistical theory. Then, the SVR model was employed to forecast dynamic heat supply. In the model, the input variables were selected based on the correlation analysis and three crucial parameters, including the penalties factor, gamma of the kernel RBF, and insensitive loss function, were optimized by PSO algorithms. The optimized SVR model was compared with the basic SVR, optimized genetic algorithm-SVR (GA-SVR), and artificial neural network (ANN) through six groups of experiment data from two heat sources. The results of the correlation coefficient analysis revealed the relationship between the influencing factors and the forecasted heat supply and determined the input variables. The performance of the PSO-SVR model is superior to those of the other three models. The PSO-SVR method is statistically robust and can be applied to practical heating system.

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The use of simplified weather data to estimate thermal loads of non-residential buildings

Cited by 37 publications

References 2 publications

On the Effect of Global Warming and the UAE Built Environment

On the Effect of Global Warming and the UAE Built Environment

Multiple Regression Model Based on Weather Factors for Predicting The Heat Load of A District Heating System in Dalian, China—A Case Study

Dynamic Heat Supply Prediction Using Support Vector Regression Optimized by Particle Swarm Optimization Algorithm

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