Modified Models and Database for Calculating Cooling Degree-Days for the Egyptian Climates

Electrical energy usage of commercial buildings (over 60% in the USA) is of growing concern due to its impact on the environment. To reduce it, it is necessary to identify the factors contributing to commercial building energy usage. Towards this, we studied the importance of various building features and their contributions on cooling energy, using two datasets of Commercial Building Energy Consumption Survey (CBECS) 2018 and 2012. For this, Shapash, an explainable artificial intelligence technique, was used, with Random Forest, to predict the cooling energy usage intensity (EUI). The two most important features for 2018 are cooling degree days and principal building activity, while cooling degree days and cooling percentage were for 2012 with 54.79% and 33.46% contributions of the total consumption respectively. The comparison of feature importance indicated that cooling degree days are the topmost important feature impacting cooling EUI and have a more significant contribution of 34.29% for 2018 and 19.68% for 2012. Overall, the most important features impacting cooling EUI were cooling degree days, principle building activity, cooling percentage and total hours open per week. The results from this study provide insights into the most significant factors influencing energy consumption and help in developing strategies for reducing it.

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

Low temperature-induced sterility in rice: Evidence for the effects of temperature before panicle initiation

In cases where a quick insight into the operation of an HVAC system is more important than accuracy, cooling degree days can be used for monitoring electric energy consumption dependent on meteorological conditions. Cooling degree days are calculated from differences between outdoor temperatures above the base temperature and the base temperature itself, therefore containing both climate and building information. The difficulties in applying this method are the determination of base temperature and choosing a procedure for calculating degree days, which vary depending on the resolution of the weather data used. In addition, the cooling degree method has a major flaw, i.e. it considers only a linear dependence between cooling energy consumption and sensible cooling load, thereby ignoring latent loads, which become more significant at higher outdoor temperatures. In this article an analysis of real electric energy consumption data using the cooling degree method and an improved method derived from it that includes latent loads, as well as a comparison between them, are shown. Both methods are applied several times to metered data, each time with a different combination of a method for determining base temperature and a degree day calculation technique.

To study past climate conditions, main climatic factor related to the energy consumption namely temperature and precipitation were considered. Heating degree days (HDD), cooling degree days (CDD) and sunshine hours were analyzed. According to all reports in different scales temperature of Astara region, in last half century has been increased. In the longer future time period, the study area will face with higher values of increasing temperature. HDD with base temperature of 18 °C reduced in last 18 years. HDD in January which is cold month also reduced. Meanwhile CDD increased. CDD in July which is warm month also increased. This is in harmony with increasing the number of sunshine hours in the same time slice. By changing HDD and CDD in cool and warm seasons, it means the pattern of energy consumption in the region has changed. Astara location is included in both of Central Asia and West Asia sub-regions in IPCC AR4. Projection for temperature change in Central Asia sub-region is not in agreement with national and local downscaling results.

<div> <p><span>Future climate projections for the Mediterranean area point out to an important increase in temperature during this century, independently on the considered emission scenario. This projected increase will have a significant impact on temperature-related climate indices, such as heating and cooling degree-days, and also, the economic costs to maintain climate comfort within buildings, especially for summer. These indices are key outside temperature-based indices to evaluate the energy consumption of buildings.</span><span> </span></p> </div><div> <p><span>In this work, a very high spatial resolution database for heating (HDD) and cooling degree-days (CDD) has been performed from different temperature data: The network of automatic weather station from the Meteorological Service of Catalonia from 2006 to 2015 (138 stations in 32,000 km2), and non-automatic weather station data from 1971 to 2015. It has been taking as a baseline 15 and 18 °C for HDD, and 21 and 25 °C as thresholds for CDD.</span><span> </span></p> </div><div> <p><span>Data from automatic weather stations allow us to compute these indices with a high temporal accuracy (hourly or sub-hourly time scales), but they are only available with a highly-dense network for the last few 15 years. Otherwise, data from non-automatic weather stations allows us to analyse a wider temporal coverage, but only with daily-mean temperature data. Thus, in areas with a complex topography, as it is the case of Catalonia, important differences at annual scale can appear when computing HDD and CDD from daily or hourly-mean values, especially for zones prone to thermal inversion situations. Hourly data leads to increase HDD and CDD for each considered threshold. The differences among them reach annual-mean values higher than 200 ºC-days for heating degree-days and 150 ºC-days for cooling degree-days.</span><span> </span></p> </div><div> <p><span>Taking into account statistically downscaled climate projections at 1-km spatial resolution from 3 IPCC-AR5 global climate simulations forced by RCP4.5 and RCP8.5 emission scenarios, it is expected a significant decrease in HDD for the Pyrenees, up to 2000-2400 ºC-days in 2100, for 15 and 18 ºC thresholds, respectively. Meanwhile, it is projected an important increase in CDD for the Ebro Valley, up to 400-600 ºC-days in 2100, for 25 and 21 ºC thresholds, respectively.</span></p> </div><p> </p>

Volumetric Risk Hedging Strategies and Basis Risk Premium for Solar Power

The present paper is continuation of our recent study and analyzes the potential changes of residential heating and cooling degree-days as well as three stakeholder-relevant indices of agro-meteorological change (growing season length, sum of the active and sum of the effective temperatures) for Central and Southeast Europe over near past (1975–2004), near (2021–2050) and far (2070–2099) future periods. All indicators were calculated from the output data of our simulations with the regional climate model RegCM driven by the ERA-Interim reanalysis for the near past and by the global circulation model HadGEM2-ES under RCP8.5 CMIP5 radiative forcing scenario for the future periods. The validation of the model-based indices against their counterparts, computed from the observational dataset E-OBS, shows that the model reproduces their spatial variability and magnitude generally well. A linear bias correction of the considered indices is also demonstrated. Consistent with the general trend of the mean and extreme temperatures over the region, the study reveals a decrease of the heating degree days and considerable increase of the cooling degree days and the agro-meteorological indices practically over the whole domain in the future. The detected changes are fairly not symmetrical - the relative increase of the cooling degree days is significantly bigger than the decrease of the heating degree-days.KeywordsHeating and cooling degree-daysAgro-meteorological indicesCMIP5 RCP8.5Bias correctionRegional climate simulation

Meteorological data collected at SRS since the mid-1960’s have been analyzed for trends in minimum and maximum temperature, heating and cooling degree days, precipitation and relative humidity. The trends in meteorological data collected have been relatively small compared to the interannual variability that is observed. The observed increases, while small, appear to be real (statistically significant). Overnight low temperatures (3.1°F) have increased over twice as fast as the increases in daytime highs (1.4°F). Similarly, there are statistically significant increases in the number of cooling degree days as well. There has been a similar decrease in the number of HDD and freezing days, consistent with the overall increase in overnight low temperatures.

Social housing in tropical regions faces critical thermal comfort challenges that will intensify under future climate change, yet current design practices lack systematic frameworks for evaluating long-term performance across multiple climate scenarios. This study assesses the thermal performance of social housing in southeastern Mexico using energy simulation, supervised machine learning, and global sensitivity analysis. Two housing typologies (single-story and two-story) were modeled across four cities (Mérida, Campeche, Cancún, and Tuxtla Gutiérrez) under climate change scenarios (RCP 2.6, 4.5, and 8.5) for 2050 and 2100. Various machine learning models were trained to predict comfort temperature and cooling degree days. Regression Trees demonstrated superior performance, with R2 values exceeding 0.98 for both thermal comfort indicators, achieving RMSE values of 0.0095 °C for comfort temperature and 0.2613 °C for cooling degree days. Global sensitivity analysis using the PAWN method revealed that ambient temperature was the most influential variable, accounting for 45–49% of the total sensitivity, followed by solar radiation (17–22%) and relative humidity (10–12%), while building-specific parameters had modest impacts (0.6–3.8%). Geographic variations were significant, with Mérida and Campeche showing higher cooling demands than Cancún and Tuxtla Gutiérrez. Future climate projections indicate substantial increases in cooling requirements by 2100, with CDD values expected to increase by approximately 40–50% under the RCP 8.5 scenario compared to current conditions. This research presents a computational framework for assessing thermal comfort in social housing, providing evidence-based insights for climate-adaptive building strategies in tropical regions.

Heatwaves are driving rapidly increasing cooling demand, placing power systems under growing stress with implications for energy resilience in a changing climate. While recent advances in climate prediction have improved understanding of near-term climate variability, the seasonal-to-multiyear predictability of extreme cooling demand remains insufficiently explored. Here, we assess the prediction skill of heatwaves, cooling degree days (CDDs), and derived categories of cooling demand across Northern Hemisphere hotspots using initialized and uninitialized simulations from the Community Earth System Model version 2 Multiyear Prediction System (CESM2-MP) over the period 1981–2020. We evaluate predictability associated with externally forced signals and internal climate variability, with a focus on summer thermal extremes relevant to cooling demand. We find that externally forced signals provide robust seasonal-to-multiyear predictability of terrestrial heatwaves, enabling skillful forecasts of dry and humid CDDs and associated categories of elevated cooling demand at multi-year lead times. Predictive skill is strongest in regions including the US Southwest, Arabian Peninsula, Central America, and Southeast Asia, where forecasts reliably distinguish between below-normal, elevated, and extreme demand years. Internal climate variability, including ENSO-related signals, contributes additional but more limited predictability at approximately one-year lead time. Our results indicate emerging multi-year predictability of heatwave-driven cooling demand, highlighting the potential for climate-informed approaches to anticipate future demand extremes and support energy-system resilience and adaptation planning.Key words: terrestrial heatwaves, cooling degree days, cooling demand, predictability, external forcing, CESM2-MP, climate extremes, climate risk, energy resilience, hotspots, urban applications, socio-economic and health impacts.

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.

Global climate change affects residential heating and cooling demand that further contributes to carbon dioxide (CO2) emissions. The spatio-temporal changes in magnitude and distribution of the demands in China are poorly understood. In addition, few studies have focused on the future impact of climate change on long term residential CO2 emissions in China. Here we investigate regional changes in CO2 emissions calculated from degree-days. Our results show that heating degree-days (HDD), cooling degree-days (CDD) and their associated CO2 emissions all have large spatio-temporal variability. We find that average durations of HDD and CDD are predicted to be 34 days shorter and 63 days longer by the end of century (2071–2100) than history (1976–2005). CO2 emissions from residential cooling and heating are predicted to increase 218% and decrease 30% in China by end-century, respectively. We further examine the CO2 emissions from residential heating and cooling in five cities representative of five contrasting architectural climate zones in China. The CO2 emissions from heating of these cities are projected to decrease by end-century: 26% in Harbin, 32% in Beijing, 43% in Shanghai, 42% in Kunming, and 61% in Shenzhen. The CO2 emissions from cooling of these cities all increase by end-century: 436% in Harbin, 215% in Beijing, 223% in Shanghai, 765% in Kunming, and 149% in Shenzhen.

Climate warming impact on degree-days and building energy demand in Switzerland

This study utilizes advanced machine learning (ML) algorithms to assess the influence of external weather-related factors on the energy efficiency (EE) of residential buildings. Two deep neural network (DNN) models were developed: a standard DNN and an enhanced one incorporating residual layers and attention mechanisms. The models were trained using a dataset comprising historical meteorological data and energy performance data from residential homes in York, UK. The investigation indicated that energy consumption is substantially influenced by meteorological factors including temperature (denoted as heating degree days (HDD) and cooling degree days (CDD)), humidity, wind velocity, and sun radiation. The random forest model surpassed previous models, attaining root mean square error (RMSE) of 0.0819 and a R 2 score of 1.0000, underscoring its exceptional capacity to represent the intricate interactions between meteorological factors and energy usage.The advanced deep neural network demonstrated favourable outcomes with an RMSE of 4.12 and a R 2 of 0.878, underscoring the significance of employing complicated architectures to represent intricate relationships. The results underscore the need of integrating ML for EE forecasting and identify critical domains for enhancing household energy consumption. The study compared multiple ML models, highlighting their benefits and weaknesses for prediction accuracy and resilience. This is followed by the application of SHAP (SHapley Additive exPlanations) to interpret model outputs and obtain an understanding of the significance of these weather features.