Simulation-based coefficients for adjusting climate impact on energy consumption of commercial buildings

Wang, Na; Makhmalbaf, Atefe; Srivastava, Viraj; Hathaway, John E.

doi:10.1007/s12273-016-0332-1

Cited by 16 publications

(7 citation statements)

References 13 publications

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“…For this normalization the base temperature is set to 18°C, and energy meter reading is the summation of electric and gas energy consumption. As no additional information about occupant behavior is available, "e" coefficient for P parameter in (7) is set to zero.…”

Section: Simulation and Resultsmentioning

confidence: 99%

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Structure dependent weather normalization

Beheshti

Sahebalam

Nidoy

2019

Energy Science & Engineering

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In this paper, we introduce a new analytical method to normalize and forecast the energy usage/loss of residential and commercial buildings. Weather conditions have large effects on energy and economic activity. Weather Normalization is an important step in building energy rating and retrofit measurements. It has also become increasingly important because of changes in the worlds weather patterns due to global warming. Accounting for the impacts of weather on energy use in buildings is an extremely exhaustive challenge because of the complexity and diversity in the operation of the mechanical and electrical systems. In traditional weather normalization methods some building parameters, such as building size, window size, construction joints, and the effect of flues, are missing. We present a Structure Dependent Energy Usage/Loss (SDE U/L) linear and nonlinear models by using Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) to capture and forecast the behavior of energy consumption/loss. This model considers different building and weather parameters. Using the (SDE U/L) model, we present an innovative approach for linear and nonlinear weather normalization. Our simulation results demonstrate the flexibility and advantages of our structure dependent weather normalization method. Unlike most existing methods, the (SDE U/L) method does not impose any constraints on a property on its property type, use details, and energy data to be able to perform weather normalization for any building over time.

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Section: Simulation and Resultsmentioning

confidence: 99%

“…Including our parametric modeling (7) in energy consumption equation (6), the energy reading value is obtained as follows:…”

Section: Structure Dependent Energy Usage/loss Formulationmentioning

confidence: 99%

Structure dependent weather normalization

Beheshti

Sahebalam

Nidoy

2019

Energy Science & Engineering

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“…Two indices, namely Heating Degree-Day (HDD) and Cooling Degree-Day (CDD), are commonly used to measure the sum of the daily variation of the temperature below or above a certain threshold and to adjust the heating and cooling energy demand (Isaac and van Vuuren, 2009). Although the degree-days based weather normalization has been criticized for its inherent limitations (Wang et al, 2016), this approach has been adapted in this study for its simplicity of use and the minimal amount of data required. Where HDD18 and CDD26 are the annually heating and cooling degree-days and Tout is the outdoor daily average temperature.…”

Section: Stock Average Space Heating and Cooling Euismentioning

confidence: 99%

Low carbon heating and cooling of residential buildings in cities in the hot summer and cold winter zone - A bottom-up engineering stock modeling approach

Yao

et al. 2019

Journal of Cleaner Production

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Building stock modeling can predict stock energy consumption and carbon emissions for both current and future conditions to inform building design and retrofitting policies. A 'bottom-up' engineering approach for building stock energy modeling is attractive to built environment energy researchers because of its capacity for detailed energy analysis. However, such studies in China have been very limited to date. The aim of this research is to develop a modeling approach to residential building stock energy consumption for space heating and cooling. A holistic four-step approach of archetype configurations; building performance simulation; stock floor area estimation and local weather adjustment is presented. The Chongqing municipality was chosen to demonstrate the approach. The results show that adopting the northern China standard pattern of central space heating for Chongqing's urban residential stock is not feasible because it dramatically increases primary energy consumption and therefore carbon dioxide emissions from space heating usage. By applying energy conservation retrofit measures to the Chongqing urban residential stock, the total energy consumption for space heating and cooling and resulting carbon dioxide emissions can be significantly reduced, with estimated reductions of 57.6% to 60.7% in 2020 and 55.3% to 57.2% in 2050. The method described can provide useful information and guidance for policymakers contemplating energy retrofit schemes.

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“…The current combination results in an overall set of 2448 building models that covers 80% of the US commercial floorspace [8]. These models have been used to analyze the energy savings and cost impacts of energy-efficiency code updates [9,10]; develop prescriptive new construction and retrofit design guides [11,12]; create technical potential scales for building asset scores [13,14]; develop typical energy-conservation measure savings estimates for up-front incentives through utility programs [15]; create performance, cost, lifetime and time-to-market targets for new technologies to inform DOE's technology investment portfolio [16]; and many other applications [17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Potential Energy, Demand, Emissions, and Cost Savings Distributions for Buildings in a Utility’s Service Area

Bass

Copeland

2020

Energies

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Several companies, universities, and national laboratories are developing urban-scale energy modeling that allows the creation of a digital twin of buildings for the simulation and optimization of real-world, city-sized areas. Prior to simulation-based assessment, a baseline of savings for a set of utility-defined use cases was established to clarify the initial business case for specific energy efficient building technologies. In partnership with a municipal utility, 178,337 OpenStudio and EnergyPlus models of buildings in the utility’s 1400 km2 service area were created, simulated, and assessed with measures for quantifying energy, demand, cost, and emissions reductions of each building. The method of construction and assumptions behind these models is discussed, definitions of example measures are provided, and distribution of savings across the building stock is provided under a maximum technical adoption scenario.

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Simulation-based coefficients for adjusting climate impact on energy consumption of commercial buildings

Cited by 16 publications

References 13 publications

Structure dependent weather normalization

Structure dependent weather normalization

Low carbon heating and cooling of residential buildings in cities in the hot summer and cold winter zone - A bottom-up engineering stock modeling approach

Potential Energy, Demand, Emissions, and Cost Savings Distributions for Buildings in a Utility’s Service Area

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