Load consumption prediction utilizing historical weather data and climate change projections

2017 ◽  
Author(s):  
Po-Chen Chen ◽  
Mladen Kezunovic
Keyword(s):  
Climate Change ◽  
Weather Data ◽  
Climate Change Projections ◽  
Consumption Prediction

Generating test reference years from the UKCP09 projections and their application in building energy simulations

2011 ◽  
Vol 33 (4) ◽  
pp. 387-406 ◽  
Author(s):  
H Du ◽  
CP Underwood ◽  
JS Edge
Keyword(s):  
Climate Change ◽  
Carbon Emission ◽  
Building Energy ◽  
Weather Data ◽  
Research Groups ◽  
Future Time ◽  
Climate Change Projections ◽  
Time Horizons ◽  
Energy Simulations ◽  
Operative Temperatures

In this study, test reference year (TRY) data for three UK cities are generated from the new UKCP09 climate change projections 1 for a variety of future time horizons and carbon emission scenario assumptions. The data are applied to the energy simulation of three commercial buildings and one house for the three city locations (London, Manchester and Edinburgh), three future time horizons in this century and three carbon emission scenarios. Results are compared with those generated using alternative TRYs from two other research groups who used UKCP09 1 as well as with the existing TRY data sets which form the CIBSE Future Weather Years 2 in order to produce robust results. Results of future simulations of peak summer operative temperatures, peak cooling demand, annual cooling energy, peak heating demand and annual heating energy are presented for the four building case studies benchmarked against control weather data for the period 1960–1989. The results show increasing internal operative temperatures (non-air-conditioned) and increasing air-conditioning demands (air-conditioned) throughout this century and though peak heating demands remain similar to control data, annual heating energy consumptions can be expected to fall sharply. Practical applications: Currently, practitioners can use Test Reference Years for use in building energy simulations. In 2009, the CIBSE released Future Weather Years, which go further by allowing practitioners to explore the thermal and comfort behaviour of buildings at future time horizons thus helping to ‘future proof’ a design. In 2009, the United Kingdom Climate Impacts Programme released a new generation of climate change scenario data (the UKCP09 climate change projections) using probabilistic methods. These are the most comprehensive data yet and provides a greater degree of detail than was available to generate the CIBSE Future Weather Years. It is therefore likely that the new data will gradually become the normal basis for investigating future building thermal and comfort response. In this study, a sample of TRY is generated from the UKCP09 data and applied to the simulation of a sample of ‘real’ buildings. The results are compared with both the existing CIBSE Future Weather Years as well as with Test Reference Years generated using UKCP09 by two other research groups. The results provide a robust way forward for simulating building thermal and comfort response using future weather data.

scholarly journals Seamless climate change projections and seasonal predictions for bushfires in Australia

2020 ◽  
Vol 70 (1) ◽  
pp. 120
Author(s):  
Andrew J. Dowdy
Keyword(s):  
Climate Change ◽  
Long Range ◽  
Time Scales ◽  
Weather Conditions ◽  
Weather Data ◽  
Future Climate Change ◽  
Data Sets ◽  
Fire Weather ◽  
Climate Change Projections ◽  
Different Time Scales

Spatio-temporal variations in fire weather conditions are presented based on various data sets, with consistent approaches applied to help enable seamless services over different time scales. Recent research on this is shown here, covering climate change projections for future years throughout this century, predictions at multi-week to seasonal lead times and historical climate records based on observations. Climate projections are presented based on extreme metrics with results shown for individual seasons. A seasonal prediction system for fire weather conditions is demonstrated here as a new capability development for Australia. To produce a more seamless set of predictions, the data sets are calibrated based on quantile-quantile matching for consistency with observations-based data sets, including to help provide details around extreme values for the model predictions (demonstrating the quantile matching for extremes method). Factors influencing the predictability of conditions are discussed, including pre-existing fuel moisture, large-scale modes of variability, sudden stratospheric warmings and climate trends. The extreme 2019–2020 summer fire season is discussed, with examples provided on how this suite of calibrated fire weather data sets was used, including long-range predictions several months ahead provided to fire agencies. These fire weather data sets are now available in a consistent form covering historical records back to 1950, long-range predictions out to several months ahead and future climate change projections throughout this century. A seamless service across different time scales is intended to enhance long-range planning capabilities and climate adaptation efforts, leading to enhanced resilience and disaster risk reduction in relation to natural hazards.

Impact of weather data and climate change projections in the refurbishment design of residential buildings in cooling dominated climate

2021 ◽  
Vol 303 ◽  
pp. 117584
Author(s):  
Rosa Francesca De Masi ◽  
Antonio Gigante ◽  
Silvia Ruggiero ◽  
Giuseppe Peter Vanoli
Keyword(s):  
Climate Change ◽  
Residential Buildings ◽  
Weather Data ◽  
Climate Change Projections

Winter wheat yield response to climate variability in Denmark

2010 ◽  
Vol 149 (1) ◽  
pp. 33-47 ◽  
Author(s):  
K. KRISTENSEN ◽  
K. SCHELDE ◽  
J. E. OLESEN
Keyword(s):  
Climate Change ◽  
Winter Wheat ◽  
Grain Yield ◽  
Inflection Point ◽  
Soil Types ◽  
Weather Data ◽  
Climate Projections ◽  
Climate Change Projections ◽  
Final Model ◽  
Winter Temperatures

SUMMARYData on grain yield from field trials on winter wheat under conventional farming, harvested between 1992 and 2008, were combined with daily weather data available for 44 grids covering Denmark. Nine agroclimatic indices were calculated and used for describing the relation between weather data and grain yield. These indices were calculated as average temperature, radiation and precipitation during winter (1 October–31 March), spring (1 April–15 June) and summer (16 June–31 July), and they were included as linear and quadratic covariates in a mixed regression model. The model also included an effect of year to describe the change in yield caused by unrecorded variables such as management changes. The final model included all effects that were significant for at least one of the two soil types (sandy and loamy soils). Seven of the nine agroclimatic indices were included in the final model that was used to predict the wheat grain yield under five climate scenarios (a baseline for 1985 and two climate change projections for 2020 and 2040) for two soil types and two locations in Denmark.The agroclimatic index for summer temperature showed the strongest effect causing lower yields with increasing temperature, whereas yield increased with increasing radiation during summer and spring. Winter precipitation and spring temperature did not affect grain yield significantly. Grain yield responded non-linearly to mean winter temperature with the highest yield at 4·4°C and lower yields both below and above this inflection point.The application of the model predicted that the average yield would decrease under projected climate change. The average decrease varied between 0·1 and 0·8 t/ha (comparable to a relative reduction of 1·6–12.3%) depending on the climate projection, location and soil type. On average, the grain yield decreased by about 0·25 t/ha (c. 3.6%) from 1985 to 2020 and by about 0·55 t/ha (c. 8·0%) from 1985 to 2040. The predicted yield decrease depended on climate projection and was larger for wheat grown in West Zealand than in Central Jutland and in most cases also larger for loamy soils than for sandy soils.The inter-annual variation in grain yield varied greatly between climate projections. The coefficient of variation (CV) varied between 0·16 and 0·46 and was smallest for wheat grown on loamy soils in Central Jutland in the baseline climate and largest for winter wheat grown under one of the 2040 climate projections. The increase in CV is not so much an effect of increased climatic variability under the climate change projections, but more an effect of increased winter temperature, where more extreme winter temperatures (lower or higher than the inflection point at 4·4°C) increased the effect of winter temperatures.

scholarly journals A probabilistic assessment of climate change impacts on yield and nitrogen leaching from winter wheat in Denmark

2011 ◽  
Vol 11 (9) ◽  
pp. 2541-2553 ◽  
Author(s):  
C. D. Børgesen ◽  
J. E. Olesen
Keyword(s):  
Climate Change ◽  
Sandy Soils ◽  
Climate Change Impacts ◽  
Soil Types ◽  
Weather Data ◽  
N Leaching ◽  
Climate Projections ◽  
Minor Effect ◽  
Climate Change Projections ◽  
Temperature And Precipitation

Abstract. Climate change will impact agricultural production both directly and indirectly, but uncertainties related to likely impacts constrain current political decision making on adaptation. This analysis focuses on a methodology for applying probabilistic climate change projections to assess modelled wheat yields and nitrate leaching from arable land in Denmark. The probabilistic projections describe a range of possible changes in temperature and precipitation. Two methodologies to apply climate projections in impact models were tested. Method A was a straightforward correction of temperature and precipitation, where the same correction was applied to the baseline weather data for all days in the year, and method B used seasonal changes in precipitation and temperature to correct the baseline weather data. Based on climate change projections for the time span 2000 to 2100 and two soil types, the mean impact and the uncertainty of the climate change projections were analysed. Combining probability density functions of climate change projections with crop model simulations, the uncertainty and trends in nitrogen (N) leaching and grain yields with climate change were quantified. The uncertainty of climate change projections was the dominating source of uncertainty in the projections of yield and N leaching, whereas the methodology to seasonally apply climate change projections had a minor effect. For most conditions, the probability of large yield reductions and large N leaching losses tracked trends in mean yields and mean N leaching. The impacts of the uncertainty in climate change were higher for loamy sandy soil than for sandy soils due to generally higher yield levels for loamy sandy soils. There were large differences between soil types in response to climate change, illustrating the importance of including soil information for regional studies of climate change impacts on cropping systems.

Uncertainty in CMIP6 climate change projections for the Australian monsoon is tied to hemispheric-scale changes

2020 ◽  
Author(s):  
Sugata Narsey ◽  
Josephine R. Brown ◽  
Robert A. Colman ◽  
Francois Delage ◽  
Scott Brendan Power ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Change Projections ◽  
Australian Monsoon

scholarly journals Heating and Cooling Degree-Days Climate Change Projections for Portugal

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 715
Author(s):  
Cristina Andrade ◽  
Sandra Mourato ◽  
João Ramos
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Thermal Comfort ◽  
Energy Demand ◽  
Residential Buildings ◽  
Degree Days ◽  
Climate Change Projections ◽  
Heating And Cooling ◽  
Cooling Degree Days ◽  
Heating Energy Demand

Climate change is expected to influence cooling and heating energy demand of residential buildings and affect overall thermal comfort. Towards this end, the heating (HDD) and cooling (CDD) degree-days along with HDD + CDD were computed from an ensemble of seven high-resolution bias-corrected simulations attained from EURO-CORDEX under two Representative Concentration Pathways (RCP4.5 and RCP8.5). These three indicators were analyzed for 1971–2000 (from E-OBS) and 2011–2040, and 2041–2070, under both RCPs. Results predict a decrease in HDDs most significant under RCP8.5. Conversely, it is projected an increase of CDD values for both scenarios. The decrease in HDDs is projected to be higher than the increase in CDDs hinting to an increase in the energy demand to cool internal environments in Portugal. Statistically significant linear CDD trends were only found for 2041–2070 under RCP4.5. Towards 2070, higher(lower) CDD (HDD and HDD + CDD) anomaly amplitudes are depicted, mainly under RCP8.5. Within the five NUTS II

Short Communication: Climate change and biofuel wheat: A case study of southern Saskatchewan

2012 ◽  
Vol 92 (3) ◽  
pp. 421-425 ◽  
Author(s):  
Hong Wang ◽  
Yong He ◽  
Budong Qian ◽  
Brian McConkey ◽  
Herb Cutforth ◽  
...  
Keyword(s):  
Climate Change ◽  
Grain Yield ◽  
Short Communication ◽  
Weather Data ◽  
Yield Reduction ◽  
Climate Change Scenarios ◽  
Communication Climate ◽  
Stochastic Weather Generator ◽  
Ipcc Sres

Wang, H., He, Y., Qian, B., McConkey, B., Cutforth, H., McCaig, T., McLeod, G., Zentner, R., DePauw, R., Lemke, R., Brandt, K., Liu, T., Qin, X., White, J., Hunt, T. and Hoogenboom, G. 2012. Short Communication: Climate change and biofuel wheat: A case study of southern Saskatchewan. Can. J. Plant Sci. 92: 421–425. This study assessed potential impacts of climate change on wheat production as a biofuel crop in southern Saskatchewan, Canada. The Decision Support System for Agrotechnology Transfer-Cropping System Model (DSSAT-CSM) was used to simulate biomass and grain yield under three climate change scenarios (CGCM3 with the forcing scenarios of IPCC SRES A1B, A2 and B1) in the 2050s. Synthetic 300-yr weather data were generated by the AAFC stochastic weather generator for the baseline period (1961–1990) and each scenario. Compared with the baseline, precipitation is projected to increase in every month under all three scenarios except in July and August and in June for A2, when it is projected to decrease. Annual mean air temperature is projected to increase by 3.2, 3.6 and 2.7°C for A1B, A2 and B1, respectively. The model predicted increases in biomass by 28, 12 and 16% without the direct effect of CO2 and 74, 55 and 41% with combined effects (climate and CO2) for A1B, A2 and B1, respectively. Similar increases were found for grain yield. However, the occurrence of heat shock (>32°C) will increase during grain filling under the projected climate conditions and could cause severe yield reduction, which was not simulated by DSSAT-CSM. This implies that the future yield under climate scenarios might have been overestimated by DSSAT-CSM; therefore, model modification is required. Several measures, such as early seeding, must be taken to avoid heat damages and take the advantage of projected increases in temperature and precipitation in the early season.

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