Annual Cooling Degree Days (annual sum of the number of degrees that the daily mean temperature is above 22°C each day), projections for a range of future warming levels from UKCP18. Provided on a 12km BNG grid.This metric is related to power consumption for cooling systems and air conditioning required on hot days, so this index is useful for predicting future changes in energy demand for cooling. In practice, this varies greatly throughout the UK, depending on personal thermal comfort levels and building designs, so these results should be considered as rough estimates of overall demand changes on a large scale. This data contains a field for each warming level. They are named 'CDD' (Cooling Degree Days), the warming level, and 'upper' 'median' or 'lower' as per the description below. E.g. 'CDD 2.5 median' is the median value for the 2.5°C projection. Decimal points are included in field aliases but not field names e.g. 'CDD 2.5 median' is 'CDD_25_median'. Data defaults to displaying 'CDD 2.0°C median' values, use 'change style' to display other values.The warming levels used are 1.5°C, 2.0°C, 2.5°C, 3.0°C, 4.0°C, and two baselines are also provided for 1981-2000 (corresponding to 0.51°C warming) and 2000-2017 (corresponding to 0.835°C warming).What is the data?The data is from the UKCP18 regional projections using the RCP8.5 scenario. Rather than giving projections for a specific date under different scenarios, one scenario is used and projections are given at the different warming levels. So this data shows the expected Cooling Degree Days should these warming levels be reached, at the time that the warming level is reached.For full details, see 'Future Changes to high impact weather in the UK'. HM Hanlon, D Bernie, G Carigi and JA Lowe. Climatic Change, 166, 50 (2021) https://doi.org/10.1007/s10584-021-03100-5What do the 'median', 'upper', and 'lower' values mean?This scenario is run as 12 separate ensemble members. To select which ensemble members to use, a single value was taken from each ensemble member - the mean of a 21yr period centred on the year the warming level was reached. They were then ranked in order from lowest to highest.The 'lower' fields are the second lowest ranked ensemble member.The 'higher' fields are the second highest ranked ensemble member.The 'median' fields are the median average of all ensemble members.This gives a median average value, and a spread of the ensemble members indicating the level of uncertainty in the projections.This dataset forms part of the Met Office’s Climate Data Portal service. This service is currently in Beta. We would like your help to further develop our service, please send us feedback via the site - https://climate-themetoffice.hub.arcgis.com/

What does the data show? A Cooling Degree Day (CDD) is a day in which the average temperature is above 22°C. It is the number of degrees above this threshold that counts as a Coolin Degree Day. For example if the average temperature for a specific day is 22.5°C, this would contribute 0.5 Cooling Degree Days to the annual sum, alternatively an average temperature of 27°C would contribute 5 Cooling Degree Days. Given the data shows the annual sum of Cooling Degree Days, this value can be above 365 in some parts of the UK.Annual Cooling Degree Days is calculated for two baseline (historical) periods 1981-2000 (corresponding to 0.51°C warming) and 2001-2020 (corresponding to 0.87°C warming) and for global warming levels of 1.5°C, 2.0°C, 2.5°C, 3.0°C, 4.0°C above the pre-industrial (1850-1900) period. This enables users to compare the future number of CDD to previous values.What are the possible societal impacts?Cooling Degree Days indicate the energy demand for cooling due to hot days. A higher number of CDD means an increase in power consumption for cooling and air conditioning, therefore this index is useful for predicting future changes in energy demand for cooling.In practice, this varies greatly throughout the UK, depending on personal thermal comfort levels and building designs, so these results should be considered as rough estimates of overall demand changes on a large scale.What is a global warming level?Annual Cooling Degree Days are calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming.   The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the Annual Cooling Degree Days, an average is taken across the 21 year period. Therefore, the Annual Cooling Degree Days show the number of cooling degree days that could occur each year, for each given level of warming. We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.What are the naming conventions and how do I explore the data?This data contains a field for each global warming level and two baselines. They are named ‘CDD’ (Cooling Degree Days), the warming level or baseline, and 'upper' 'median' or 'lower' as per the description below. E.g. 'CDD 2.5 median' is the median value for the 2.5°C projection. Decimal points are included in field aliases but not field names e.g. 'CDD 2.5 median' is 'CDD_25_median'. To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘CDD 2.0°C median’ values.What do the ‘median’, ‘upper’, and ‘lower’ values mean?Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future. For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, Annual Cooling Degree Days were calculated for each ensemble member and they were then ranked in order from lowest to highest for each location. The ‘lower’ fields are the second lowest ranked ensemble member. The ‘upper’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and upper fields, the greater the uncertainty.‘Lower’, ‘median’ and ‘upper’ are also given for the baseline periods as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past.  Useful linksThis dataset was calculated following the methodology in the ‘Future Changes to high impact weather in the UK’ report and uses the same temperature thresholds as the 'State of the UK Climate' report.Further information on the UK Climate Projections (UKCP).Further information on understanding climate data within the Met Office Climate Data Portal.

This documentation and dataset can be used to test the performance of automated fault detection and diagnostics algorithms for buildings. The dataset was created by LBNL, PNNL, NREL, ORNL and ASHRAE RP-1312 (Drexel University). It includes data for air-handling units and rooftop units simulated with PNNL's large office building model.

Demand.ninja Tool is a customisable model for hourly heating and cooling demand applicable globally at all spatial scales.

These datasets can be used to evaluate and benchmark the performance accuracy of Fault Detection and Diagnostics (FDD) algorithms or tools. It contains operational data from simulation, laboratory experiments, and field measurements from real buildings for seven HVAC systems/equipment (rooftop unit, single-duct air handler unit, dual-duct air handler unit, variable air volume box, fan coil unit, chiller plant, and boiler plant). Each dataset includes a .pdf file to document key information necessary to understand the content and scope, multiple csv files containing all the time-series data for faults at different severity levels and one fault-free case, and a ttl file to visualize the data according to BRICK schema. The dataset was created by LBNL, PNNL, NREL, ORNL and Drexel University.

This report is the third in a series of reports sponsored by the U.S. Department of Energy Geothermal Technologies Program in which a range of water-related issues surrounding geothermal power production are evaluated. The first report made an initial attempt at quantifying the life cycle fresh water requirements of geothermal power-generating systems and explored operational and environmental concerns related to the geochemical composition of geothermal fluids. The initial analysis of life cycle fresh water consumption of geothermal power-generating systems identified that operational water requirements consumed the vast majority of water across the life cycle. However, it relied upon limited operational water consumption data and did not account for belowground operational losses for enhanced geothermal systems (EGSs). A second report presented an initial assessment of fresh water demand for future growth in utility-scale geothermal power generation. The current analysis builds upon this work to improve life cycle fresh water consumption estimates and incorporates regional water availability into the resource assessment to improve the identification of areas where future growth in geothermal electricity generation may encounter water challenges.

Updated Risk Registers for major subsystems of the StingRAY WEC completed according to the methodology described in compliance with the DOE Risk Management Framework developed by NREL.

Mesh, properties, initial conditions, injection/withdrawal rates for modelling thermal, hydrological, and mechanical effects of fluid injection to and withdrawal from ground for Stockton University reservoir cooling system (aquifer storage cooling system), Galloway, New Jersey, for unscheduled two hour injection at 133 % designed capacity, on fine scale grid, with some results. Second simulation of J.T. Smith, E. Sonnenthal, P. Dobson, P. Nico, and M. Worthington, 2021. Thermal-hydrological-mechanical modeling of Stockton University reservoir cooling system, Proceedings of the 46th Workshop on Geothermal Reservoir Engineering, Stanford University, SGP-TR-218, from which Figures 6-9, pertain.

Mesh, properties, initial conditions, injection/withdrawal rates for modeling thermal, hydrological, and mechanical effects of fluid injection to and withdrawal from ground for Stockton University reservoir cooling system (aquifer storage cooling system), Galloway, New Jersey, on large scale grid, with some results. First simulation of J.T. Smith, E. Sonnenthal, P. Dobson, P. Nico, and M. Worthington, 2021. Thermal-hydrological-mechanical modeling of Stockton University reservoir cooling system, Proceedings of the 46th Workshop on Geothermal Reservoir Engineering, Stanford University, SGP-TR-218, from which Figures 1-5 pertain.