Search Results (31)

Weather derivative markets, particularly Chicago Mercantile Exchange (CME) Heating Degree Day (HDD) and Cooling Degree Day (CDD) futures, face challenges from complex temperature dynamics and spatially heterogeneous co-extremes that standard Gaussian models overlook. Using daily data from 13 major U.S. cities (2014–2024), we first construct a two-stage baseline model to extract standardized residuals isolating stochastic temperature deviations. We then estimate the Extreme Value Index (EVI) of HDD/CDD residuals, finding that the nonlinear degree-day transformation amplifies univariate tail risk, notably for warm-winter HDD events in northern cities. To assess multivariate extremes, we compute Tail Dependence Coefficient (TDC), revealing pronounced, geographically clustered tail dependence among HDD residuals and weaker dependence for CDD. Finally, we compare Gaussian, Student’s t, and Regular Vine Copula (R-Vine) copulas via joint VaR–ES backtesting. The R-Vine copula reproduces HDD portfolio tail risk, whereas elliptical copulas misestimate portfolio losses. These findings highlight the necessity of flexible dependence models, particularly R-Vine, to set margins, allocate capital, and hedge effectively in weather derivative markets. Full article

Being an agricultural country, Pakistan requires lots of water for irrigation. A major portion of its water resources is located in the upper indus basin (UIB). The snowmelt runoff generated from high-altitude areas of the UIB provides inflow into the Indus river system that boosts the water supply. Snow accumulation during the winter period in the highlands in the watershed(s) becomes a source of water inflow during the snow-melting period, which is described according to characteristics like snow depth, snow density, and snow water equivalent. Snowmelt water release (SWE) and snowmelt water depth (SD) maps are generated by tracing snow occurrence from MODIS-based images of the snow-cover area, evaluating the heating degree days (HDDs) from MODIS-derived images of the land surface temperature, computing the solar radiation, and then assimilating all the previous data in the form of the snowmelt model and ground measurements of the snowmelt water release (SWE). The results show that the average snow-cover area in the Astore river basin, in the upper indus basin, ranges from 94% in winter to 20% in summer. The maps reveal that the annual average values of the SWE range from 150 mm to 535 mm, and the SD values range from 600 mm to 2135 mm, for the snowmelt period (April–September) over the years 2010–2020. The areas linked with vegetation experience low SWE accumulation because of the low slopes in the elevated regions. The meteorological parameters and basin characteristics affect the SWE and can determine the SD values. Full article

Accurate energy demand forecasting is vital for optimizing resource allocation and energy efficiency. Despite advancements in various prediction models, existing approaches often struggle to capture the complex, nonlinear relationships between temperature variations and electricity consumption. To address this issue, this paper introduces a novel hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks with an attention mechanism designed to forecast day-ahead electricity demand in Australia. This research aims to enhance the accuracy of electricity demand predictions by effectively modeling the impact of heating degree days (HDDs) and cooling degree days (CDDs) on energy usage. The HDDs and CDDs capture extreme weather conditions. They are critical for understanding spikes in energy consumption for heating and cooling needs. The proposed model leverages the strengths of CNNs in extracting spatial features in HDDs and CDDs, LSTMs in capturing temporal dependencies, and the attention mechanism in focusing on the most relevant aspects of the data. This study compares the CNN-LSTM-Attention model with traditional methods, including Deep Neural Networks, and demonstrates superior performance. The results show a significant reduction in both Mean Absolute Error and Mean Absolute Percentage Error, confirming the model’s effectiveness. The primary contribution of this paper lies in the novel integration of CDD and HDD data within the CNN-LSTM framework, which has not been extensively explored in prior studies. This approach offers a robust solution for energy management, particularly in climates with significant temperature fluctuations. Full article

This paper investigates the efficacy of weather derivatives as a risk management tool in the agricultural sector of Naousa, Greece, focusing on tree crops sensitive to temperature variations. The specific purpose is to assess how effectively weather derivative options can mitigate financial risks for farmers by providing strategic solutions. The study assesses the strategic application of Heating Degree Days (HDD) index options and their potential to alleviate economic vulnerabilities faced by farmers due to temperatures fluctuations. Employing different strike prices in Long Call and Straddle options strategies on the HDD index, the research offers tailored risk management solutions that cater to varying risk aversions among farmers. Moreover, the study applies the Value at Risk (VaR) methodology to quantify the financial security that weather derivatives can furnish, revealing a significantly reduced probability of severe financial losses in hedged scenarios compared to no-hedge conditions. Results show that all implemented strategies effectively enhance financial outcomes compared to scenarios without hedging, highlighting the exceptional utility of weather derivatives as risk management tools in the agricultural sector. Strategy 4, which exhibits the lowest VaR, emerges as the most effective, providing substantial protection against adverse weather conditions. This research supports the notion that weather derivatives can substantially contribute to the economic sustainability of rural economies, influencing policy decisions toward enhancing financial instruments for risk management in agriculture. Full article

The problem of energy poverty (EP), when energy becomes unaffordable for some population groups, is not only a problem for developing countries, but this phenomenon is appearing more and more often in European countries. In Europe, it is estimated that 50 to 125 million people are living in energy poverty. We hear more and more about energy poverty in connection with the current energy crisis and rising energy prices, but also because of insufficient renewable use. Due to increasing energy prices, we are increasingly hearing about the deepening energy poverty in Slovakia. This study aims to evaluate the development of energy poverty in Slovakia compared to other EU countries. The situation is studied from the view of the number of heating and cooling days, the percentage of the population that cannot maintain adequate heat at home, the percentage of the population that lacks heat, and the percentage of residents without enough heat. During the research, we used distribution analysis, trend analysis, analysis of variance, and one-way analysis. The main results show that the heating degree days (HDD) index recorded a decrease, the cooling degree days (CDD) index recorded an increase, and energy poverty is most obvious in a low-income group of inhabitants, having shortage of heat, when renewable energy sources (RES) use contributes to the mitigation of energy poverty. Solving the unfavorable situation of energy poverty is possible by increasing the share of renewables used in the gross final energy consumption for heating and cooling, primarily in residential buildings. The results provide information for policymakers regarding the triple bottom line approach (people, planet, and profit). Full article

The present study investigates the energy efficiency of different container house configurations across thirty European locations. By employing Heating Degree Days (HDDs) and Cooling Degree Days (CDDs), the research delves into climatic zone exploration, providing a simplified climatic classification for residential purposes and comparing it with the Köppen–Geiger model. The authors use specific hourly climatic data for each location, obtained through dynamic simulations with TRNSYS v.18 software. Initially, the CDDs are calculated by using different base temperatures (comfort temperatures that minimize energy demand) tailored to the specific conditions of each case. Then, the thermal loads of container houses are evaluated in different climatic scenarios, establishing a direct correlation between climatic conditions and the energy needs of these innovative and modular housing solutions. By comparing stacked and adjacent modular configurations in container housing, particularly in post-disaster scenarios, the study underscores the importance of adaptive design to optimize energy efficiency. The analysis conducted by the authors has allowed them to propose a climate characterization model based on HDDs, CDDs, and solar irradiance, obtaining an effective novel correlation with the Köppen–Geiger classification, especially in extreme climates. The present model emerges as a powerful tool for climate characterization in residential applications, offering a new perspective for urban planning and housing design. Furthermore, the results reveal a significant correlation between climate classification and the specific energy needs of container houses, emphasizing the direct influence of regional climatic characteristics on energy efficiency, particularly in small-sized dwellings such as container houses. Full article

In 2023, residential and commercial sectors together consumed approximately 27.6% of total United States (U.S.) energy, equivalent to about 20.6 quadrillion Btu. Factoring in the electrical system energy losses, the residential sector represented approximately 19.7% of total U.S. energy consumption during that time. There were approximately 144 million housing units in the United States in 2023, which is increasing yearly. In this study, information on energy usage in the United States residential sector has been analyzed and then represented as energy intensities to establish benchmark data and to compare energy consumption of varying sizes and locations. First, public sources were identified and data from these previously published sources were aggregated to determine the energy use of the residential sector within the US. Next, as part of this study, the energy data for seven houses/apartments from five different United States climate zones were collected firsthand. That data were analyzed, and the energy intensity of each home was calculated and then compared with the energy intensities of the other homes in the same states using Residential Energy Consumption Survey (RECS) data. The energy intensity for each facility was calculated based on the actual energy bills. Finally, the study evaluated the carbon footprint associated with residential energy consumption in all 50 states to reinforce the importance of sustainable development initiatives. Full article

The change in heating and cooling needs of Greece in the near future due to the climate change is assessed in the present study. Global and regional climate models and two different representative concentration pathways (RCPs) are used to simulate the expected change in temperature. A widely used methodology of computation of heating degree days (HDDs) and cooling degree days (CDDs) is employed with a base temperature of 18 °C. In agreement with the expected temperature rise in the near future, an HDD decrease and CDD increase under both RCPs is also expected. The changes under RCP8.5 are stronger compared to those under RCP4.5. Differences related to topography are noted. The HDD decrease is stronger than CDD increase but the relative increase in CDDs is higher than the relative increase in HDDs. The highest absolute decreases in HDDs are expected for February and March while the highest absolute increases in CDDs are expected during the three summer months. Full article

Rice production is threatened by climate change, particularly heat stress (HS). Nonstructural carbohydrates (NSCs) remobilization is a key physiological mechanism that allows rice plants to cope with HS. To investigate the impact of short-term HS on the remobilization of nonstructural carbohydrates (NSCs) in rice, two cultivars (Huaidao-5 and Wuyunjing-24) were subjected to varying temperature regimes: 32/22/27 °C as the control treatment, alongside 40/30/35 °C and 44/34/39 °C, for durations of 2 and 4 days during the booting, flowering, and combined stages (booting + flowering) within phytotrons across the years 2016 and 2017. The findings revealed that the stem’s NSC concentration increased, while the panicle’s NSCs concentration, the efficiency of NSCs translocation from the stem, and the stem NSC contribution to grain yield exhibited a consistent decline. Additionally, sugar and starch concentrations increased in leaves and stems during late grain filling and maturity stages, while in panicles, the starch concentration decreased and sugar concentration increased. The heat-tolerant cultivar, Wuyunjing-24, exhibited higher panicle NSC accumulation under HS than the heat-sensitive cultivar, Huaidao-5, which had more stem NSC accumulation. The flowering stage was the most vulnerable to HS, followed by the combined and booting stages. Heat degree days (HDDs) were utilized to quantify the effects of HS on NSC accumulation and translocation, revealing that the flowering stage was the most affected. These findings suggest that severe HS makes the stem the primary carbohydrate storage sink, and alleviation under combined HS aids in evaluating NSC accumulation, benefiting breeders in developing heat-tolerant rice varieties. Full article

In the context of global population growth and energy scarcity, building energy consumption has become a critical issue with implications for the sustainable development of human society. Winter heating consumption constitutes a large portion of total energy used in buildings, especially in regions with cold climates. This paper employs the degree-day method to analyze the energy consumption of central heating in northern Chinese cities. The study sample consists of 60 target cities, including 30 located in severe cold regions and the remaining 30 in cold regions. By utilizing heating energy consumption and climate data from 2019, the relationships between heating intensity (kWh/m²) and heating degree days (HDDs) are established for the selected cities. Additionally, statistical analysis and model comparisons are conducted. The results show strong positive correlations between heating intensity and HDDs in both severe cold regions and cold regions, with the actual heating base temperatures for the two regions being 21 °C and 22.3 °C, respectively. Moreover, the deviation index of heating intensity is introduced to analyze the energy consumption characteristics of central heating in northern cities from three perspectives: city size, level of heating development, and geographical regions. The analysis suggests that cities with large population, strong economies, and high levels of development exhibit better energy-saving performance. Lastly, several improvement suggestions are proposed to address the potential problems related to energy conservation of central heating systems in cities of northern China. Full article

The current climate change context raises the demand for reducing energy and environmental impacts while keeping an economic balance and building users’ comfort. Thermal insulation solutions are potential allies in ensuring the adequacy of existing buildings for challenging sustainability requirements. In this scenario, silica-aerogel-fibre-based thermal renders are innovative solutions for which integrated approaches still lack information, and they should be compared with benchmark multilayer solutions, such as those based on expanded polystyrene (EPS), extruded polystyrene (XPS), mineral wool (MW), and insulated corkboard (ICB), to evidence their prospective economic, environmental, and energy benefits. This paper quantifies the optimum insulation thicknesses, life cycle savings, payback periods, and environmental impacts of innovative thermal renders compared to conventional thermal insulation materials when applied as a retrofit in existing facade walls. The results show that cost-optimised thermal renders with sisal fibres led to the best overall performance. Higher heating needs led to higher optimum render thicknesses and life cycle savings. With a 0.02 m thickness, aerogel-fibre-based thermal renders outperformed other materials in terms of heating-degree days (HDD) from 1000 °C·day onwards; they can save approximately EUR 60∙m⁻², 1000 MJ∙m⁻², and 100 kg CO₂ eq∙m⁻² while presenting a U-value 13% lower throughout their 30-year lifetime when compared with the second-best multilayer solution with XPS. Full article

This research is based on an energy audit of two multi-unit residential buildings (MURBs) located in Toronto, Canada. Energy consumption (gas and electricity) data were extracted from the energy bills of the two buildings for a consecutive period of three years. The data were then normalized to account for variations in weather conditions. Conclusions were drawn from correlation analyses between kWh, cooling degree days (CDDs), and heating degree days (HDDs), which were then compared to the energy consumption benchmarks of MURBs within the GTA. An energy simulation using e-Quest v.3.64 was performed, utilizing the advantages of the e-Quest building modeling tool to create a virtual 3D model of the audited buildings. A baseline model was constructed to reflect the actual buildings and was used to simulate the outcomes and calculate the projected energy savings from window replacements with a higher energy efficiency than the existing ones. The simulation results revealed that triple low-E glazing outperformed single- and double-glass windows, achieving reductions of 38% and 34% in gas consumption, respectively. The building envelope simulations showed that enhancing insulation reduced gas consumption by 4%, while an insulation upgrade demonstrated no discernible savings. Reducing the window area by 20% (north/south sides) led to a 6% decrease in gas consumption, while a 30% reduction resulted in approximately 9% of energy savings. Full article

Heating and Cooling Degree Days (HDD, CDD) are indicative of the energy needs of buildings and also are associated with agriculturalism, tourism and other outdoor activities. Under the changing climate, future modifications of HDD and CDD are of primary importance. In the present work, monthly and annual HDD and CDD are computed and analyzed for the present and near future climate conditions. Elevation and sea proximity were found to be crucial in the formulation of energy requirements. Summer energy needs for cooling are expected to increase due to global warming while winter needs for heating are expected to decrease. Full article

Building energy requalification in Italy and Europe has been much discussed in recent years due to the high percentage of existing buildings with poor energy performance. In this context, it is useful to obtain a user-friendly and fast tool to predict the thermal energy demand (TED) for space conditioning and the related primary energy consumption (PEC) as a function of climatic stress. In this study, the SLABE methodology (simulation-based large-scale uncertainty/sensitivity analysis of building energy performance) is used to simulate representative Italian buildings, varying parameters such as geometry, envelope and HVAC (heating, ventilating and space conditioning) systems. MATLAB^® in combination with EnergyPlus is used to analyze 200 buildings belonging to two structural types (multi-family buildings and apartment blocks) built in 1961–1975. Nine scenarios (as-built scenarios and eight retrofit ones) are investigated in 63 climatic locations. A regression analysis shows that the classical HDDs (heating degree days) approach cannot give an accurate prediction of TED because solar radiation is not accounted for. Thus, new climatic indices are developed alongside solar radiation, including the heating stress index (HSI), the cooling stress index (CSI) and the yearly climatic stress index (YCSI). The purpose of our work is to obtain climatic stress curves for the prediction of TED and PEC. Testing of this novel approach is performed by comparison with another building energy simulation tool, showing a low discrepancy, i.e., the coefficient of variation of the root mean square error is between 12% and 28%, which confirms certain reliability of the approach here proposed. Full article

A more accurate determination of energy demands for buildings is of utmost importance for estimating future energy demands. This article presents two novel ideas that have the potential to contribute to a more precise determination of expected energy demands. The first idea involves accounting for a building’s function more thoroughly, which enables the determination of different energy demands for two or more identical buildings, depending on their respective usage functions. According to a case study, the heating energy demand can be up to twice as high in a commercial facility compared with a residential building. Similarly, the cooling energy requirement can also differ. The second idea concerns determining the heating degree day (HDD) and cooling degree day (CDD) values from the daily minimum and maximum temperatures. This idea may be relevant when few instantaneous values are available for the daily mean temperature. According to the case study, the calculated values from the daily minimum and maximum temperatures follow the HDD and CDD values specified from the daily mean temperature. However, the difference is less than 2% for the heating season and higher for the cooling season. Therefore, further research is required to refine the constants in the cooling equation. Full article