Uncertainty in peak cooling load calculations

Muñoz, Fernando Domínguez; López, José Manuel Cejudo; Carrillo-Andrés, António

doi:10.1016/j.enbuild.2010.01.013

Cited by 161 publications

(91 citation statements)

References 7 publications

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“…Breesch and Janssens [7] conducted uncertainty and sensitivity analyses in climate and design-related parameters for thermal comfort evaluations of passive cooling in office buildings. Domínguez-Muñoz et al [8] studied the impacts of uncertainties in 20 design-related parameters on the simulated peakcooling loads using a Monte Carlo analysis. Hopfe and Hensen [9] described a case study of uncertainty analysis in building simulation introduced by design parameters.…”

Section: Introductionmentioning

confidence: 99%

Uncertainties in energy consumption introduced by building operations and weather for a medium-size office building

Wang

Mathew

Pang

2012

Energy and Buildings

150

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Deviations between predicted and actual building energy consumption can be attributed to uncertainties introduced by four components of such projections: (1) the accuracy of the underlying models in simulation tools, (2) the accuracy of input parameters describing the design conditions of building envelope and HVAC systems, (3) actual weather, (4) variations in building operation practices. This study investigates uncertainties in energy consumption due to actual weather and building operational practices, using a simulation-based analysis of a medium-size office building. The combined effect of poor practice in building operations across multiple parameters results in an increase in energy use of 49-79% across four selected cities, while good practice reduces energy use by 15-29% across the cities. The impact of year-to-year weather fluctuation on energy use ranges from -4% to 6%. To determine the uncertainty distribution profile for annual energy use, a Monte Carlo method is applied to sample the possible combinations. This study finds that the uncertainty distribution in annual energy consumption approximately follows a log-normal distribution, and shows that the uncertainty range due to operational factors even at an 80% confidence level can dwarf the impact of design features.

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Section: Introductionmentioning

confidence: 99%

Uncertainties in energy consumption introduced by building operations and weather for a medium-size office building

Wang

Mathew

Pang

2012

Energy and Buildings

150

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“…Other methods propagate specific samples are aimed at the sensitivity analysis, for example, the screening method of Morris [32]. The SRCs method has been adopted in this paper, of which the basis is to fit a linear multidimensional model [20] between model inputs and model outputs.ŷ…”

Section: The Srcs Methods For Sensitivity Analysismentioning

confidence: 99%

“…For each output, m possible results are obtained [20]: , is used to analyze the probability distribution of the errors of the weather forecast data and the real data. A statistical model related to the problem is determined, of which the solution is regarded as the probability distribution and mathematical expectation of the constructed model.…”

Section: The Principle Of the MCMmentioning

confidence: 99%

“…For example, Sarjiya [19] adopted a decision analysis method to handle the uncertainty of the load forecast in power systems for the aim of optimization of the operating strategy. Domínguez-Muñoz [20] proposed a new approach based on stochastic simulation methods to research the impact of the uncertainty of the internal disturbance on the peak cooling load in the buildings. Douglas et al [21] put forward a method to analyze the risk of short-term power system operational planning with the electrical load forecast uncertainty.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty Analysis of Weather Forecast Data for Cooling Load Forecasting Based on the Monte Carlo Method

2018

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Abstract:Recently, the cooling load forecasting for the short-term has received increasing attention in the field of heating, ventilation and air conditioning (HVAC), which is conducive to the HVAC system operation control. The load forecasting based on weather forecast data is an effective approach. The meteorological parameters are used as the key inputs of the prediction model, of which the accuracy has a great influence on the prediction loads. Obviously, there are errors between the weather forecast data and the actual weather data, but most of the existing studies ignored this issue. In order to deal with the uncertainty of weather forecast data scientifically, this paper proposes an effective approach based on the Monte Carlo Method (MCM) to process weather forecast data by using the 24-h-ahead Support Vector Machine (SVM) model for load prediction as an example. The data-preprocessing method based on MCM makes the forecasting results closer to the actual load than those without process, which reduces the Mean Absolute Percentage Error (MAPE) of load prediction from 11.54% to 10.92%. Furthermore, through sensitivity analysis, it was found that among the selected weather parameters, the factor that had the greatest impact on the prediction results was the 1-h-ahead temperature T(h-1) at the prediction moment.

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“…Schicktanz et al [12] analyzed the primary energy consumption and the economic applicability of a combined heating, cooling and power (CHCP) system in Germany and found that energy prices and the electric efficiency of the combined heating and power unit are dominant factors affecting its performance. Dominguez-Munoz et al [13] applied sensitivity analysis to find key factors for the calculation of peak cooling loads in buildings. It is clear that previous studies usually concentrated on office buildings.…”

Section: Building Parameters That Influence Energy Consumptionmentioning

confidence: 99%

Identification of the building parameters that influence heating and cooling energy loads for apartment buildings in hot-humid climates

Yıldız

Arsan

2011

Energy

129

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a b s t r a c tIdentifying the building parameters that significantly impact energy performance is an important step for enabling the reduction of the heating and cooling energy loads of apartment buildings in the design stage. Implementing passive design techniques for these buildings is not a simple task in most dense cities; their energy performance usually depends on uncertainties in the local climate and many building parameters, such as window size, zone height, and features of materials. For this paper, a sensitivity analysis was performed to determine the most significant parameters for buildings in hot-humid climates by considering the design of an existing apartment building in Izmir, Turkey. The Monte Carlo method is selected for sensitivity and uncertainty analyses with the Latin hypercube sampling (LHC) technique. The results show that the sensitivity of parameters in apartment buildings varies based on the purpose of the energy loads and locations in the building, such as the ground, intermediate, and top floors. In addition, the total window area, the heat transfer coefficient (U) and the solar heat gain coefficient (SHGC) of the glazing based on the orientation have the most considerable influence on the energy performance of apartment buildings in hot-humid climates.

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Uncertainty in peak cooling load calculations

Cited by 161 publications

References 7 publications

Uncertainties in energy consumption introduced by building operations and weather for a medium-size office building

Uncertainties in energy consumption introduced by building operations and weather for a medium-size office building

Uncertainty Analysis of Weather Forecast Data for Cooling Load Forecasting Based on the Monte Carlo Method

Identification of the building parameters that influence heating and cooling energy loads for apartment buildings in hot-humid climates

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