The Influence of Heating Degree Days on Fuelwood Consumption in Households in Selected Countries of Central and Southeastern Europe

Serbia is one of the few European countries that does not keep official statistics and does not have data on heating degree days. A heating degree day (HDD) represents a measure to quantify the energy needs for heating a building. In order to create a database, six meteorological stations in Serbia had been selected, for which the heating degree days were calculated for every year in the period 2010-2018. The months with the highest values of heating degree days were also determined for each year of the analyzed period. In addition to the annual level, heating degree days in the heating seasons over the analyzed period were calculated for the six selected stations, as well as the length and the average air temperature of each heating season. In Serbia, heating season officially lasts from October 15 to April 15. To determine the influence of the calculated annual heating degree days on fuelwood consumption in households in Serbia, over the period 2010-2018, multiple econometric models were formulated. The influence of the annual values of heating degree days on fuelwood consumption for household space heating in Slovenia and Croatia was analyzed, as well. The analysis of energy consumption in the households of the selected countries showed that wood fuels are mostly used for heating, primarily fuelwood. This is the reason why this type of fuel was selected for the research.

Analysis of Cooling and Heating Degree Days over Mexico in Present and Future Climate

One simple way to estimate the relationship between air temperature and the energy needed for heating and cooling is to use the concept of degree day. Cooling degree days (CDD) and heating degree days (HDD) are indicators of the energy required to reach comfort levels and are related directly to energy demands. Therefore, using a novel approach, we examine the current conditions and future projections in degree days over Mexico using observations (Livneh and CPC), ERA5 reanalysis, and simulations from the Regional Climate Model (RegCM4). The RegCM4 experiments were driven by different General Circulation Models for two Representative Concentration Pathways scenarios. We consider three 20-year periods as “present conditions” (1995–2014), “near-future conditions” (2041–2060), and “far-future conditions” (2080–2099). The results suggest that in the future, under the lowest radiative forcing scenario there will be a smaller increase (decrease) in CDD (HDD) for the far-future, as compared to the near-future. This could represent the model’s response to the peak of radiative forcing at mid-century and its subsequent decline. For the highest radiative forcing scenario, we found a greater increase (decrease) in CDD (HDD) for the far-future, which could be explained by the response of the RegCM4 to the warming increase projected for 2100.

IMPACTS OF BUILDING FUNCTION ON NORMALIZED-STEAM CONSUMPTION: ANALYSIS OF FLOOR AREA NORMALIZATION VERSUS LINEAR REGRESSION ON HEATING DEGREE-DAYS IN A HEATING-DOMINATED CLIMATE

ABSTRACT Linear regression analysis is one the most common methods for weather-normalizing energy data, where energy versus degree-days is plotted, quantifying the impacts of outside temperature on buildings’ energy use. However, this approach solely considers dry-bulb temperature, while other climate variables are ignored. In addition, depending on buildings’ internal loads, weather impact can be less influential, making the linear regression method not applicable for energy data normalization in internally driven buildings (such as research laboratory buildings, healthcare facilities, etc.). In this study, several existing buildings from different categories, all located on the University of Massachusetts Amherst campus and exposed to the same weather conditions in a heating-dominated climate, were analyzed. For all cases, regression of monthly steam use on heating degree-days and floor-area normalized steam data were used, investigating applicability of the former when the latter changes. It was found that internal loads can skew steam consumption, depending on the building functionality, making the effect of degree-days negligible. For laboratory-type buildings, besides heating and domestic hot water production, steam is also used for scientific experiments. Here, daily occupancy percentage, even during weekends and holidays, was higher than that of other buildings, indicating the intensity of scientific experiments performed. This significantly impacted steam consumption, resulting in higher floor-area-normalized steam usage. In these cases, steam use did not provide an outstanding correlation to heating degree-days. Whereas, for cases with other functionality-types and lower floor-area normalized steam, coefficients of determination in regressions were high. This study concludes that even for buildings located in the same climate, depending on how building functionality and occupancy schedule influence floor-area normalized steam use, multivariate linear regression can provide more accurate analysis, rather than simple linear regression of steam on heating degree-days.

Spatial-Temporal Changes and Associated Determinants of Global Heating Degree Days

The heating degree days (HDDs) could indicate the climate impact on energy consumption and thermal environment conditions effectively during the winter season. Nevertheless, studies on the spatial-temporal changes in global HDDs and their determinants are scarce. This study used multi-source data and several methods to explore the rules of the spatial distribution of global HDDs and their interannual changes over the past 49 years and some critical determinants. The results show that global HDDs generally became larger in regions with higher latitudes and altitudes. Most global change rates of HDDs were negative (p < 0.10) and decreased to a greater extent in areas with higher latitudes. Most global HDDs showed sustainability trends in the future. Both the HDDs and their change rates were significantly partially correlated with latitude, altitude, mean albedo, and EVI during winter, annual mean PM2.5 concentration, and nighttime light intensity (p = 0.000). The HDDs and their change rates could be simulated well by the machine learning method. Their RMSEs were 564.08 °C * days and 3.59 °C * days * year−1, respectively. Our findings could support the scientific response to climate warming, the construction of living environments, sustainable development, etc.

A Case Study in Actual Building Performance and Energy Modeling with Real Weather Data

As part of this study, an issue has been identified with regards to there being a performance gap with energy efficient buildings. This has been validated through literature review in the areas of occupancy behavior, modeling accuracy and reviewing energy consumption of energy efficient buildings. In order to analyze the error generated between predicted and actual energy performance, a case study approach has been adopted. The Ron Joyce Centre is a LEED Gold Certified building that is part of the McMaster University campus in Burlington. Actual energy performance data has been collected along with detailed drawings to analyze its predicted energy performance using real weather data over a two-year period in eQUEST. The results indicate that eQuest is able to predict electrical consumption within 0.72% of actual on an annual basis. However, natural gas consumption is more erratic and inconsistent based on heating degree days and has fluctuating values with differences ranging between 21% to 4.5% on monthly basis. The overall predicted energy consumption for 2012 is 1096133 kWh and 33227 m

A Case Study in Actual Building Performance and Energy Modeling with Real Weather Data

As part of this study, an issue has been identified with regards to there being a performance gap with energy efficient buildings. This has been validated through literature review in the areas of occupancy behavior, modeling accuracy and reviewing energy consumption of energy efficient buildings. In order to analyze the error generated between predicted and actual energy performance, a case study approach has been adopted. The Ron Joyce Centre is a LEED Gold Certified building that is part of the McMaster University campus in Burlington. Actual energy performance data has been collected along with detailed drawings to analyze its predicted energy performance using real weather data over a two-year period in eQUEST. The results indicate that eQuest is able to predict electrical consumption within 0.72% of actual on an annual basis. However, natural gas consumption is more erratic and inconsistent based on heating degree days and has fluctuating values with differences ranging between 21% to 4.5% on monthly basis. The overall predicted energy consumption for 2012 is 1096133 kWh and 33227 m

The valuation of a natural gas-fired power plant with multiple turbines using clean spark spread and weather options

Assessing the value of a power plant is an important issue for plant owners and prospective buyers. In a deregulated market, an owner has the option to operate the plant when the revenue from selling the electricity is higher than the cost of operating the plant. This option is known as the spark spread option. Under emission restrictions, when the carbon cost is deducted from the spark spread, the option is named as the clean spark spread option. This thesis presents an analysis on the spark spread and clean spark spread option based valuation methods for a power plant with multiple gas turbines having different input–output characteristics, emission rates, and capacities. Electricity, natural gas and carbon allowance prices are assumed to follow mean–reverting processes. Results demonstrate that CO2 allowance cost reduces the expected plant value, while the flexibility of switching among turbines adds value to the power plant. Weather also affects the power plant operation. This thesis also presents a valuation model for a power plant integrating spark spread and weather options. A cooler winter drawing more electricity could generate a higher payoff for the plant owner. A warmer winter, however, could lead to a lower payoff. An owner holding a long position in a temperature–based put option could exercise the option when the winter is milder. The exercise is triggered by the drop of heating degree days below a strike degree day. The number of weather contracts to buy is determined by minimizing the variance of the total payoff. Pricing of the weather option is calculated based on the mean–reverting behavior of temperature. Results demonstrate that the integrating weather option along with spark spread option adds value to the downward spark spread option based valuation of the plant in a warmer winter. A comparison of temperature modeling approaches with an aim to pricing weather option is also investigated. Regime–switching models generated from a combination of different underlying processes are utilized to determine the expected heating and cooling degree days. Weather option prices are then calculated based on a range of strike heating degree days.

The valuation of a natural gas-fired power plant with multiple turbines using clean spark spread and weather options

Assessing the value of a power plant is an important issue for plant owners and prospective buyers. In a deregulated market, an owner has the option to operate the plant when the revenue from selling the electricity is higher than the cost of operating the plant. This option is known as the spark spread option. Under emission restrictions, when the carbon cost is deducted from the spark spread, the option is named as the clean spark spread option. This thesis presents an analysis on the spark spread and clean spark spread option based valuation methods for a power plant with multiple gas turbines having different input–output characteristics, emission rates, and capacities. Electricity, natural gas and carbon allowance prices are assumed to follow mean–reverting processes. Results demonstrate that CO2 allowance cost reduces the expected plant value, while the flexibility of switching among turbines adds value to the power plant. Weather also affects the power plant operation. This thesis also presents a valuation model for a power plant integrating spark spread and weather options. A cooler winter drawing more electricity could generate a higher payoff for the plant owner. A warmer winter, however, could lead to a lower payoff. An owner holding a long position in a temperature–based put option could exercise the option when the winter is milder. The exercise is triggered by the drop of heating degree days below a strike degree day. The number of weather contracts to buy is determined by minimizing the variance of the total payoff. Pricing of the weather option is calculated based on the mean–reverting behavior of temperature. Results demonstrate that the integrating weather option along with spark spread option adds value to the downward spark spread option based valuation of the plant in a warmer winter. A comparison of temperature modeling approaches with an aim to pricing weather option is also investigated. Regime–switching models generated from a combination of different underlying processes are utilized to determine the expected heating and cooling degree days. Weather option prices are then calculated based on a range of strike heating degree days.

Analysis of natural gas consumption and energy saving measures for powder coating and food processing companies in the Greater Toronto area (GTA)

Small and medium industries (SMEs) savings analysis and meaningful performance indicators can help Enbridge Gas Distribution Inc., and individual SMEs make effective decisions to improve facility performance. For this study, information on 11 SMEs’ energy consumption has been provided. This entails: preliminary benchmarking, separation of process and seasonal energy consumption, heating degree days, individual facilities owned reference temperature, normalized annual energy consumption, normalized process and seasonal energy consumption, oven energy consumption, energy balance of oven, energy intensity of oven, and non-productive energy consumption. The most appropriate performance indicator is energy intensity of oven-in bake ovens, cure ovens, and dry-off ovens. The results observed energy intensity in terms of natural gas consumption of bake ovens are from 24m3/ft3 to 30m3/ft3, where the intensity of ovens with finishing process companies are from 8m3/ft3 to 36m3/ft3. Potential natural gas savings from the facilities processing powder coating and baking are 19% to 53% of total oven energy consumption by reducing exhaust energy loss. In the same study observed in analyzing production scheduling, that 8% to 69% of energy consumption can be saved by proper shut-down operation and scheduling.

Analysis of natural gas consumption and energy saving measures for powder coating and food processing companies in the Greater Toronto area (GTA)

Small and medium industries (SMEs) savings analysis and meaningful performance indicators can help Enbridge Gas Distribution Inc., and individual SMEs make effective decisions to improve facility performance. For this study, information on 11 SMEs’ energy consumption has been provided. This entails: preliminary benchmarking, separation of process and seasonal energy consumption, heating degree days, individual facilities owned reference temperature, normalized annual energy consumption, normalized process and seasonal energy consumption, oven energy consumption, energy balance of oven, energy intensity of oven, and non-productive energy consumption. The most appropriate performance indicator is energy intensity of oven-in bake ovens, cure ovens, and dry-off ovens. The results observed energy intensity in terms of natural gas consumption of bake ovens are from 24m3/ft3 to 30m3/ft3, where the intensity of ovens with finishing process companies are from 8m3/ft3 to 36m3/ft3. Potential natural gas savings from the facilities processing powder coating and baking are 19% to 53% of total oven energy consumption by reducing exhaust energy loss. In the same study observed in analyzing production scheduling, that 8% to 69% of energy consumption can be saved by proper shut-down operation and scheduling.