Heating Degree Days Research Articles

제조업과 서비스 부문 기후 리스크 측정

지구 온난화와 그에 따라 발생빈도가 늘어난 이상기온 현상으로 인해 제조업과 서비스 업종에서 감당해야 할 기후위험 역시 증가하는 추세에 있다. 본 연구는 Pardo et al. (2002)이 이용한 간단한 실증모델 추정을 통해 품목별 기후리스크를 측정한다. 실증분석의 결과, 제조업 품목의 26.7%, 서비스 업종의 27.9%가 날씨여건에 따라 판매량이나 경영성과에 유의미한 변화를 경험하고 있는 것으로 나타났다. Given the presence of global warming, the economic impact of climate changes on output sales has been discussed in the literature, but rarely with empirical evidences. In this present study, a simple log-model was employed to identify the economic impacts of weather changes in manufacturing and service sectors in Korea. For this empirical exercise, weather variables including the CDD (cooling degree days) and HDD (heating degree days) were computed using the Korea's meteorological records covering the period 1970-2012. According to estimation results, 26.7% (144 over 539) and 27.9% (64 over 229) of the manufacturing and service sectors, respectively, are found to be weather-sensitive.

Estimating the characteristics of the Urban Heat Island Effect in Nicosia, Cyprus, using multiyear urban and rural climatic data and analysis

In order to detect the Urban Heat Island Effect and its consequences on heating and cooling degree days, an analysis of the hourly meteorological measurements during the period 1983–2011 for the city of Nicosia in Cyprus has been conducted. The stations used were located in the City Center (station no.640) and the area of Athalassa (station no. 666) in Nicosia, which are low altitude Mediterranean climatic stations. The temperature change that has been identified on both stations has an impact on heating and cooling degree days. Cooling degree days follow a statistically positive trend for both stations. Heating degree days follow a negative trend for both stations, with the Nicosia City Center station being the only one with a statistically significant trend. Additional cooling degree days have been calculated for the station of Nicosia City Center during the summer period June–August, and less heating degree days during the winter period December–February, in comparison with the Athalassa Area Station, highlighting the existence of the Urban Heat Island Effect. The Urban Heat Island Effect is determined to be stronger during the winter period, mainly in February. Finally, the mean differences, as calculated, in cooling and heating degree days follow a non-statistically significant trend.

An econometric model for annual peak demand for small utilities

This paper presents a model that can be used to estimate the annual peak demand in electricity consumption for small sized electric utilities. The developed model was used to investigate the influence of econometric variables on the power demand of N. Cyprus. Utilizing historical annual databases, analysis of variance (ANOVA), and the statistical methods, it was found that number of customers, price of electricity, number of tourists, population, as well as heating degree days correlate with the annual peak demand with R2 = 0.995. The performance of the model was measured using mean absolute scaled error (MASE) and mean absolute percentage error (MAPE) for in-sample (1992–2010) and out-of-sample (2011–2013) data. Also, to ensure the validity of the outcomes, models were regenerated by decreasing the in-sample data (i.e. increasing the out of sample data) back to five consecutive years and MAPE and MASE calculations were repeated. The results indicated that the model, using the above-mentioned parameters as regressors, has very strong predictive ability and can be used to estimate the annual peak demand. The combination of peak demand, base demand and energy consumption models can be utilized as a useful technique for the purpose of resource planning for small sized utilities.

Impacts of global warming on residential heating and cooling degree-days in the United States.

Climate change is expected to decrease heating demand and increase cooling demand for buildings and affect outdoor thermal comfort. Here, we project changes in residential heating degree-days (HDD) and cooling degree-days (CDD) for the historical (1981–2010) and future (2080–2099) periods in the United States using median results from the Climate Model Intercomparison Project phase 5 (CMIP5) simulations under the Representation Concentration Pathway 8.5 (RCP8.5) scenario. We project future HDD and CDD values by adding CMIP5 projected changes to values based on historical observations of US climate. The sum HDD + CDD is an indicator of locations that are thermally comfortable, with low heating and cooling demand. By the end of the century, station median HDD + CDD will be reduced in the contiguous US, decreasing in the North and increasing in the South. Under the unmitigated RCP8.5 scenario, by the end of this century, in terms of HDD and CDD values considered separately, future New York, NY, is anticipated to become more like present Oklahoma City, OK; Denver, CO, becomes more like Raleigh, NC, and Seattle, WA, becomes more like San Jose, CA. These results serve as an indicator of projected climate change and can help inform decision-making.

Optimum Sizing of Residential Cogeneration for Prefeasibility Estimations. An Analytical Approach

Abstract An analytical method is developed to estimate the optimum size of cogeneration units in residential applications used for electrical supply, space heating and domestic hot water production. The method is validated by comparing its results with those of detailed simulation fromtwenty-five main cities of Greece, and found to achieve a coefficient of variation of root-mean squared error less than 4%. The optimum sized CHPcorresponds to 32-47% of maximum load, and proved to be mainly related to the heating degree-days of the area (correlation coefficient R= 0.985) rather than the minimum temperature.

High-resolution temperature fields to evaluate the response of Italian electricity demand to meteorological variables: an example of climate service for the energy sector

The dependence of Italian daily electricity demand on cooling degree-days, heating degree-days and solar radiation is investigated by means of a regression model applied to 12 consecutive 2-year intervals in the 1990–2013 period. The cooling and heating degree-days records used in the model are obtained by (i) estimating, by means of a network of 92 synoptic stations and high-resolution gridded temperature climatologies, a daily effective temperature record for all urbanised grid points of a high-resolution grid covering Italy; (ii) using these records to calculate corresponding grid point degree-days records; and (iii) averaging them to get national degree-days records representative of urban areas. The solar radiation record is obtained with the same averaging approach, with grid point solar radiation estimated from the corresponding daily temperature range. The model is based on deterministic components related to the weekly cyclical pattern of demand and to long-term demand changes and on weather-sensitive components related to cooling degree-days, heating degree-days and solar radiation. It establishes a strong contribution of cooling degree-days to the Italian electricity demand, with values peaking in summer months of the latest years up to 211 GWh day−1 (i.e. about 23 % of the corresponding average Italian electricity demand). This contribution shows a strong positive trend in the period considered here: the coefficient of the cooling degree-days term in the regression models increases from the first 2-year period (1990–1991) to the last one (2012–2013) by a factor 3.5, which is much greater than the increase of the Italian total electricity demand.

Estimating the Impact of Temperature Change on Electricity Consumption in Seoul

Climate change, in particular temperature change, has an impact on the demand for heating and cooling. This paper explores the effect of gradually warming climate on the demand for heating and cooling in Seoul during 1995-2014 using an autoregressive distributed-lag model, a family of time- series econometric multivariate regression model. The estimated results reveal that there are two peaks in Seoul's electricity consumption because cooling degree days (CDD) and heating degree days (HDD) are statistically highly significant. CDD's regression coefficient for a short and long-run model is approximately twice bigger than HDD's and the summer peak is more important in terms of electricity consumption in Seoul. Furthermore, there exists a long-run relationship between electricity consumption and the explanatory variables such as economic growth, CDD, HDD, seasonal dummies, and black out dummy.

Secular trends in monthly heating and cooling demands in Croatia

This paper analyzes long-term heating and cooling trends for five locations in Croatia from 1901 to 2008 to assist in the revision of Croatia’s heating and cooling energy policy. Trends in monthly heating degree-days (HDD) and cooling degree-days (CDD) were determined for three related temperature threshold values each and analyzed to provide insight into the influence of desired thermal comfort on the extent of changes in energy consumption. Monthly trends in the corresponding number of heating days (HD) and cooling days (CD) were also analyzed. A basic investigation of HDD, HD, CDD, and CD trends proved to be essential to the development of a complete description of important climate-related conditions that impact energy demands associated with heating and cooling. In a few cases, the dependence of the trends on the implemented temperature thresholds was rather pronounced and was reflected in great spatial and temporal variations in monthly trends. The statistical significance of the detected monthly trends illustrated a diverse range of possible impacts of climate changes on heating and cooling energy consumption both across and within three main climate regions in Croatia (continental, mountainous, and maritime). It is confirmed that the applied monthly scale for analyses is suitable for assessing heating and cooling practices.

The sensitivity of present-time electricity demand on past climate change: a case study for Italy

Abstract A methodology for estimating secular daily minimum, mean and maximum (Tn, Tm and Tx) temperature records for any urbanised point of a 30-arc-second-resolution grid covering Italy is presented. It is based on the superimposition of 1961–1990 climatologies and departures from them (anomalies). The anomalies are obtained by applying inverse distance weighting to 143 Italian high-quality records, whereas the climatologies are based on a larger dataset and on the application of local weighted linear regression of temperature versus elevation. The grid-point Tn, Tm and Tx records are then used to set up secular records (period 1801–2013) of temperature-derived variables that influence Italy present-time national electricity demand. They are national averages over Italian urbanised areas of cooling degree-days (CDD), heating degree-days (HDD) and solar radiation deficit with respect to a defined threshold (S), with solar radiation estimated using daily temperature range as a proxy. The monthly and yearly sums of the daily CDD, HDD and S records are then used, alongside with a model allowing to link these variables to present-time Italy electricity demand, in order to understand the impact of climate variability and change on present-time Italian electricity demand. We find that temperature changes as the ones observed in the last two centuries are capable of altering significantly the present-time monthly profile of the electricity demand, raising (lowering) summer (winter) months contributions. The impact is higher in summer months where it exceeds 5 % of present-time Italy average monthly electricity demand, whereas the decrease of the winter demand is rather low because of a very limited use of electricity for heating. The summer and winter opposite-sign changes result globally in an increase of the yearly demand of about 5 TWh, corresponding to about 1.5-2.0 % of present-time Italy yearly electricity demand.

Microcogeneration in buildings with low energy demand in load sharing application

The paper investigates the introduction of a MCHP (Micro Combined Heat and Power) system in buildings with low energy demand with respect to the current building stock. A load sharing approach between a multifamily residential building and an office one is taken into account. Dynamic simulations are carried out in order to evaluate the thermo-economic performance of the analyzed system. Particular attention is given to the estimation of the electric load of the different users, as the economic profitability of a MCHP system is strongly influenced by the amount of self-consumed electricity. In order to analyze the influence of climatic conditions, two different geographical locations in Italy (Naples and Turin, having 1034 and 2617 heating degree days, respectively) are considered. The results of this study indicate that the installation of MCHP systems in buildings with low energy demand allows to increase the percentage of self-consumed electricity reducing the bidirectional electricity flow between the users and the external grid, as well as the impact on the grid itself due to the large diffusion of distributed generation systems.Moreover this study shows that the load sharing approach between users with different load profile leads to better energy, environmental and economic results with respect to a conventional system. The climatic conditions play an important role on the MCHP operational hours and hence on the thermo-economic performance of the system. The primary energy saving of the system located in Turin is equal to 8.8% with respect to 6.2% of the system located in Naples. Also the environmental performance, evaluated in terms of equivalent CO2 avoided emissions, are better in Turin (8.3%) than in Naples (6.7%). The economic analysis shows acceptable values of the pay-back period in presence of economic support mechanisms. The findings of this study show that the introduction of a MCHP system in load sharing approach leads to thermo-economics advantages even considering the lower heating needs of well-insulated buildings.

Energy and material flows of megacities.

Understanding the drivers of energy and material flows of cities is important for addressing global environmental challenges. Accessing, sharing, and managing energy and material resources is particularly critical for megacities, which face enormous social stresses because of their sheer size and complexity. Here we quantify the energy and material flows through the world's 27 megacities with populations greater than 10 million people as of 2010. Collectively the resource flows through megacities are largely consistent with scaling laws established in the emerging science of cities. Correlations are established for electricity consumption, heating and industrial fuel use, ground transportation energy use, water consumption, waste generation, and steel production in terms of heating-degree-days, urban form, economic activity, and population growth. The results help identify megacities exhibiting high and low levels of consumption and those making efficient use of resources. The correlation between per capita electricity use and urbanized area per capita is shown to be a consequence of gross building floor area per capita, which is found to increase for lower-density cities. Many of the megacities are growing rapidly in population but are growing even faster in terms of gross domestic product (GDP) and energy use. In the decade from 2001-2011, electricity use and ground transportation fuel use in megacities grew at approximately half the rate of GDP growth.

Pricing and hedging quanto options in energy markets

In energy markets, the use of quanto options has increased significantly in recent years. The payoff from such options are typically written on an underlying energy index and a measure of temperature. They are suited to managing the joint price and volume risk in energy markets. Using a Heath-Jarrow-Morton approach, we derive a closed-form option pricing formula for energy quanto options under the assumption that the underlying assets are lognormally distributed. Our approach encompasses several interesting cases, such as geometric Brownian motions and multifactor spot models. We also derive Delta and Gamma expressions for hedging. Further, we illustrate the use of our model by an empirical pricing exercise using NewYork Mercantile Exchange-traded natural gas futures and Chicago Mercantile Exchange-traded heating degree days futures for New York.

Climate change and its role in forecasting energy demand in buildings: A case study of Douala City, Cameroon

The foremost role of a building is to assure the comfort of its occupants. The thermal comfort of a building depends on the outdoor climate and requires a demand in energy for heating and cooling. In this paper, demand of energy (heating/cooling) in the buildings is discussed in Douala, Cameroon. Daily data of the last 40 years coming from five weather stations of Cameroon have been studied. Some forecasts have been carried out with 14 GCM models, associated to three future climate scenarios B1, A2, and A1B. However, only INCM3 of General Circulation Model (GCM) and A2 scenario was used. Energy demand in buildings is valued by HDD (heating degree day) and CDD (cooling degree day) indices. Obtained results show that the temperature evolves more quickly in dry season than in rainy season in Douala. Climate rise indicates an increasing demand of energy in the buildings for cooling. Global Douala heating shows a definite effect on outdoor comfort. From 2045 to 2075, the demand of energy for cooling will be superior to 50%. The total demand in energy for heating in the buildings is estimated to be 67.882 kcal from 1970 to 2000 and will be around 67.774 kcal from 2013 to 2043.

DEVELOPMENT OF A GREENHOUSE BY USING SOLAR ENERGY FOR PRODUCING PEPPER CROP

This study aimed to utilization of solar energy as a renewable energy source to enhance the environment of greenhouse for pepper crop production through comparing between two greenhouses. Two semicircle greenhouses (30 m × 6 m × 3 m) were used to achieve the objective of this study. The solar collector with vacuum tubes was used for heating greenhouse (developed greenhouse). The second, greenhouse without any heating sources (traditional greenhouse). This study indicated that the heating system for greenhouse has a big effect of temperature distribution uniformity inside the greenhouse. The soil temperatures for developed greenhouse were 17.2, 16.49, 16.68 and 17.11 °C at soil depth 5, 10, 15 and 20 cm respectively. The highest values of ultra violet (UV) dose were 13 and 10.2 mJ.cm-2 for developed greenhouse and traditional greenhouse respectively. The highest values of heating degree day (HDD) were 0.3 °C and 0.1 °C for traditional greenhouse and developed greenhouse respectively. Generally, the developed greenhouse was lower needy to cooling degree day (CDD) and HDD than traditional greenhouse. The heating system for developed greenhouse has a significant agronomic effect especially on the precocity of the production. Developed greenhouse was better than traditional greenhouse for all vegetative growth of pepper plant parameters. Pepper crop specification of developed greenhouse yield was better than traditional greenhouse especially second packing for all early yield and yield components of pepper plant parameters.

Modeling and Forecasting Average Temperature for Weather Derivative Pricing

The main purpose of this paper is to present a feasible model for the daily average temperature on the area of Zhengzhou and apply it to weather derivatives pricing. We start by exploring the background of weather derivatives market and then use the 62 years of daily historical data to apply the mean-reverting Ornstein-Uhlenbeck process to describe the evolution of the temperature. Finally, Monte Carlo simulations are used to price heating degree day (HDD) call option for this city, and the slow convergence of the price of the HDD call can be found through taking 100,000 simulations. The methods of the research will provide a frame work for modeling temperature and pricing weather derivatives in other similar places in China.

Effects of urban green areas on air temperature in a medium-sized Argentinian city

Urban climate is the result of both atmospheric and geographic factors affecting a region, as well as the morphology, structures and human activities in a city. Urban vegetation in particular affects this climate at a local scale and provides many other social, economic and ecological benefits. Thus, it is important to explore the effects of different green areas used for urban and periurban agriculture and forestry activities (UPAF) on daily atmospheric temperature and the required degrees of cooling or refrigerating temperature. Comfort temperatures were defined using a range 18-24°C and analyzed using actual measured as well as forecasted temperatures using a future scenario. Actual temperatures were recorded from September 2013 to August 2014 using digital sensors across eight sites in Rosario, Argentina: three in the central core with no vegetation, one in the central core with street trees, one in an urban agriculture site, one in a public park and two in periurban agricultural areas. Results show that air temperature in the central core with no vegetation were higher than those in other sites with vegetation during day and night, with the exception of the temperatures measured at the central core site with street trees. Findings also show that temperature effects in urban agricultural gardens of approximately 0.2 ha were similar to those of gardens and public parks 2-3 ha in size. Three UPAF types were classified according to cooling degree days, which decreased in order from (1) central core with no trees; (2) central core with street trees and public parks; and (3) urban and periurban agriculture areas. Conversely, the opposite trends for heating degree days were found. Results from this study can be used for integrating UPAF measures into climate change mitigation and urban planning policies in medium-sized cities in the developing world.

A research on environmental rating systems considering building energy performances in different climatic regions of Turkey

Turkey's diverse regions lead to complex issues in sustainability. We studied to cities in different areas of the country to examine the effects of local climate on energy use in energy-efficient buildings. Erzurum is the coldest city; Antalya lies in the Mediterranean region and has the highest solar global horizontal radiation in Turkey. We used Heating Degree Days (HDD) and Cooling Degree Days (CDD) values for both cities to estimate the energy demand for heating and cooling the buildings. In Erzurum, CO2 emissions are high, because fuel consumption to heat the same building is more than 4 times of that of Antalya in winter. CO2 emission is significant as the key greenhouse gas. In Antalya, the electricity costs for cooling the same building are more than 42 times that of Erzurum; CO2 emissions are also higher during summer. A building certified by the Leadership in Energy and Environmental Design (LEED) uses about 35% less energy for heating and cooling in both cities. The economic and environmental contributions of a LEED-certified building in Erzurum is higher during cold weather, while in Antalya, a LEED-certified building conserves comparatively more energy and retains more CO2 during hot weather. The results show that the LEED Certification System can be a more international system if geographical and climatic differences are also taken into consideration.

Energy consumption based on heating/cooling degree days within the urban environment of Athens, Greece

The degree-day method is considered to be a fundamental and a rather simple method to estimate heating and cooling energy demand. This study aims in a detailed and accurate assessment of cooling and heating degree days in different locations within the Greater Athens area (GAA), Greece. To achieve this goal, hourly values of air temperature from eight different locations within the GAA, covering the period 2001–2005, were used. Thus, the monthly and the annual number of cooling and heating degree days for each one of the examined locations could be estimated separately. Furthermore, an effort is made to evaluate the energy consumption for a specific building, based on the degree-day method, to indicate the impact of the canopy layer urban heat island on neighboring regions within the GAA. Results reveal that there is great spatial variability of energy demand and energy consumption along with significant differences in expenses for heating and cooling among neighboring regions within the GAA. Finally, regarding the energy demands of buildings, it is important to take into account intra-urban variability of canopy layer climates against an ensemble mean throughout the city, because the latter can result in inaccurate estimations and conclusions.

Mid‐twenty‐first‐century projected trends in North American heating and cooling degree days

Heating degree days and cooling degree days are overall measures of the demand for heating and cooling services. These measures serve as indices for financial products known as temperature derivatives (and more generally, weather derivatives), which allow utility companies and other businesses to hedge against weather risks. Weather derivatives are traded on the Chicago Mercantile Exchange, and as of 2011, the size of the weather risk market was estimated to be $11.8bn. Questions linking climate change to weather derivatives naturally arise. In this paper, we estimate trends in the annual projected number of heating degree days and cooling degree days over North America using regional climate model output for years 2038–2068. The regional climate model data are obtained from the North American Regional Climate Change Assessment Program. Two methods are demonstrated, one empirical and data driven, and the other taking advantage of spatial statistical models. Both methods incorporate regional climate model ensemble variability and produce trend estimates across North America. Copyright © 2014 John Wiley & Sons, Ltd.

Association between climate factors, pollen counts, and childhood hay fever prevalence in the United States

Climate factors and pollen counts may play a role in hay fever. We sought to determine the impact of specific climate factors and pollen counts on the US prevalence of hay fever and statewide variation in prevalence. We used a merged analysis of the 2007 National Survey of Children's Health from a representative sample of 91,642 children aged 0 to 17 years and the 2006-2007 National Climate Data Center and Weather Service measurements of relative humidity (%), indoor heating degree days, precipitation, Palmer Hydrological Drought Index, clear sky and issued ultraviolet indices, stratospheric ozone levels, and outdoor air temperature and National Allergy Bureau total pollen counts. Multivariate survey logistic regression models controlled for sex, race/ethnicity, age, household income, and birthplace. The US prevalence of hay fever in childhood was 18.0% (95% CI, 17.7% to 18.2%), with the highest prevalence in southeastern and southern states. Hay fever prevalence was significantly lower with second and third quartile mean annual relative humidity (logistic regression, P ≤ .01 for both), fourth quartile mean annual Palmer Hydrological Drought Index (P = .02), third and fourth quartile mean annual heating degree days (P < .0001 for both), and third and fourth quartile mean annual stratospheric ozone levels but increased with second, third, and fourth quartile mean annual temperature (P ≤ .02 for both), fourth quartile mean annual precipitation (P = .0007), mean total pollen counts (P = .01), and second, third, and fourth quartile issued ultraviolet index (P ≤ .0001 for all). Principal-component analysis was also used to determine the combined effects of correlated climate variables and pollen counts. This study provides evidence of the influence of climate on the US prevalence of childhood hay fever.