Data fusion analysis applied to different climate change models: An application to the energy consumptions of a building office (original) (raw)
Related papers
2020
During their long service life, buildings are faced with a changing climate. Hence, qualitative long-term data on weather conditions is required to assess the energy performance of buildings by means of building energy simulations. Convection-permitting climate models are promising, though requiring high computational time. This paper investigates the representativeness of one-year representative and extreme weather years derived from a 30-year convectionpermitting climate model in a current and future climatic context for an office building. While the average heating and cooling load is well presented by the average years considered, only considering temperature to select an extreme warm year leads to an underestimation of the maximal cooling load.
Simple future weather files for estimating heating and cooling demand
Estimations of the future energy consumption of buildings are becoming increasingly important as a basis for energy management, energy renovation, investment planning, and for determining the feasibility of technologies and designs. Future weather scenarios, where the outdoor climate is usually represented by future weather files, are needed for estimating the future energy consumption. In many cases, however, the practitioner’s ability to conveniently provide an estimate of the future energy consumption is hindered by the lack of easily available future weather files. This is, in part, due to the difficulties associated with generating high temporal resolution (hourly) estimates of future changes in air temperature. To address this issue, we investigate if, in the absence of high-resolution data, a weather file constructed from a coarse (annual) estimate of future air temperature change can provide useful estimates of future energy demand of a building. Experimental results based on both the degree-day method and dynamic simulations suggest that this is indeed the case. Specifically, heating demand estimates were found to be within a few per cent of one another, while estimates of cooling demand were slightly more varied. This variation was primarily due to the very few hours of cooling that were required in the region examined. Errors were found to be most likely when the air temperatures were close to the heating or cooling balance points, where the energy demand was modest and even relatively large errors might thus result in only modest absolute errors in energy demand.
Building Simulation, 2009
There is growing concern about the potential impact of climate change on the thermal performance of buildings. Building simulation is well-suited to predict the behaviour of buildings in the future, and to quantify the risks for prime building functions like occupant productivity, occupant health, or energy use. However, on the time scales that are involved with climate change, different factors introduce uncertainties into the predictions: apart from uncertainties in the climate conditions forecast, factors like change of use, trends in electronic equipment and lighting, as well as building refurbishment / renovation and HVAC (heating, ventilation, and air conditioning) system upgrades need to be taken into account. This article presents the application of two-dimensional Monte Carlo analysis to an EnergyPlus model of an office building to identify the key factors for uncertainty in the prediction of overheating and energy use for the time horizons of 2020, 2050 and 2080. The office has mixed-mode ventilation and indirect evaporative cooling, and is studied using the UKCIP02 climate change scenarios. The results show that regarding the uncertainty in predicted heating energy, the dominant input factors are infiltration, lighting gain and equipment gain. For cooling energy and overheating the dominant factors for 2020 and 2050 are lighting gain and equipment gain, but with climate prediction becoming the one dominant factor for 2080. These factors will be the subject of further research by means of expert panel sessions, which will be used to gain a higher resolution of critical building simulation input.
A Comparative Analysis of Different Future Weather Data for Building Energy Performance Simulation
Climate
The building energy performance pattern is predicted to be shifted in the future due to climate change. To analyze this phenomenon, there is an urgent need for reliable and robust future weather datasets. Several ways for estimating future climate projection and creating weather files exist. This paper attempts to comparatively analyze three tools for generating future weather datasets based on statistical downscaling (WeatherShift, Meteonorm, and CCWorldWeatherGen) with one based on dynamical downscaling (a future-typical meteorological year, created using a high-quality reginal climate model). Four weather datasets for the city of Rome are generated and applied to the energy simulation of a mono family house and an apartment block as representative building types of Italian residential building stock. The results show that morphed weather files have a relatively similar operation in predicting the future comfort and energy performance of the buildings. In addition, discrepancy bet...
Energy and Buildings, 2020
To better understand the impacts of the warming caused by global climate change on building performance, future hourly weather data that account for climate change are crucial to building simulation studies. Downscaling from general circulation models (GCMs) by the morphing method has been adopted by researchers worldwide. Using this method, we developed six sets of future hourly weather data for Hong Kong, taking the typical meteorological year (TMY) as the baseline climate. The ensemble mean from 24 general circulation models (GCMs) in the Coupled Model Intercomparison Project Phase 5 (CMIP5) has also been incorporated to take into account the uncertainties and biases between different models. These newly developed future weather data were then employed in the building energy simulation to evaluate the impacts of future climate change. Moreover, this study used the adaptive comfort standard (ACS) from ASHRAE Standard 55 in a mixed-mode residential building to consider the acclimatization effects of occupants in the changing climate. Results indicate that by the end of this century, the indoor discomfort percentage in the cooling seasons are expected to increase from 21.9% for TMY to 36.0% and 50.4% under RCP4.5 and RCP8.5 scenarios, respectively, while the annual cooling load is expected to increase up to 278.80%.
Energy and Buildings, 2012
The effects of future weather preparation using mean changes in different climatic variables at different time scales on residential building heating and cooling energy requirement predictions were investigated using dynamic building simulations. It was found that the predicted reductions in heating energy requirement obtained using any two future weather data sets are likely to be within or around 10% difference from each other if the future weather preparation incorporates at least the changes in the mean air temperatures. For cooling energy requirement predictions in temperate and hot climates, it is recommended to use future weather data prepared with the changes in at least two climatic variables, namely air temperature and humidity. However, for cooling energy requirement predictions in relatively cold climates, future weather preparation should consider the changes in mean monthly or seasonal climatic variables including at least air temperature, solar irradiance and humidity. Alternatively, changes in at least the mean monthly air temperature, daily maximum/minimum temperatures and humidity should be used. The cooling energy requirement predictions obtained using any two future weather data prepared with these recommended climatic variable sets are likely to be within or around 10% difference from each other.
The energy use in buildings is highly influenced by outdoor temperature changes. In the contest of nowadays climate change, its impact on the energy sector is important and needs to be assessed. This study investigates how the heat consumption (HC) of the existing regional building stock, located in a temperate climate in the Northern part of Europe (Belgium), will be influenced by future climate changes. First, the Seasonal Auto-Regressive Integrated Moving Average (SARIMA), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models are used to predict the temperature until 2050 from historical data. Second, the UK Met Office equations are applied for computing the heating degree-days (HDD) considering the base temperature of 15 • C. Finally, the HC of this building stock is projected until 2050 using the degree-days (DD) method. The decrease in HDD is about − 11.76% from 2012 to 2050. The HC reduction, calculated at the regional scale, is reaching − 8.82 %, − 10.00%, and − 11.26% for respectively residential, tertiary, and industrial buildings. The calculated HC is mapped on municipality, urban region, and province scales. The produced maps will help decision-makers set up efficient energy management strategies. The used methods can be replicated in other regions with the same kind of data.
Impact of climate change heating and cooling energy use in buildings in the United States
Energy and Buildings, 2014
Global warming has drawn great attention in recent years because of its large impact on many aspects of the environment and human activities in buildings. One area directly affected by climate change is the energy consumption for heating and cooling. To quantify the impact, this study used the HadCM3 Global Circulation Model (GCM) to generate weather data for future typical meteorological years, such as 2020, 2050, and 2080, for 15 cities in the U.S. based on three CO 2 emission scenarios. The method was validated by comparing the projected TMY3 data using HadCM3 with the actual TMY3 data. By morphing method, the weather data was downscaled to hourly data for use in building energy simulations by EnergyPlus. Two types of residential buildings and seven types of commercial buildings were simulated for each of the 15 cities. This paper is the first to systematically study the climate change impact on various types of residential and commercial buildings in all 7 climate zones in the U.S. through EnergyPlus simulations and provide weighted averaged results for national-wide building stock. We also identified geographical dependency of the impact of climate change on future energy uses. There would be a net increase in source energy consumption by the 2080s for climate zones 1-4 and net decrease in climate zones 6-7 based on the HadCM3 weather projection. Furthermore, this paper investigated natural ventilation performance in San Francisco, San Diego, and Seattle with improved natural ventilation model. We found that by the 2080s passive cooling would not be suitable for San Diego because of global warming, but it would still be acceptable for San Francisco and Seattle.
Using Typical and Extreme Weather Files for Impact Assessment of Climate Change on Buildings
Energy Procedia, 2017
District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, prolonging the investment return period. The main scope of this paper is to assess the feasibility of using the heat demand-outdoor temperature function for heat demand forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were compared with results from a dynamic heat demand model, previously developed and validated by the authors. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations.
Estimation of Heating Energy Use of Existing Houses in a Future Climate: 2050 vs 2007
2009
This paper presents a method for the estimation of potential impact of climate change on the heating energy use of existing houses. The proposed method uses the house energy signature, which is developed from the current heating energy use extracted from the utility bills (e.g., for year 2007) and corresponding climatic conditions. The energy signature, which is an energy-related characteristic of the house, is used along with the outdoor air temperature predicted for 2040-2069 to forecast the heating energy use. The potential impact of climate change is estimated as the percentage of variation of heating energy use in the future compared with the current situation.