Cooling Demand Research Articles

Matching supply and demand of cooling service provided by urban green and blue space

Urban blue-green space (UBGS) has been extensively investigated for its climate regulation capability in the context of climate change. However, current efforts are limited toward the cooling effects mismatch in supply and demand of UBGS. This study adapts the framework of ecosystem supply and demand to quantify the supply-demand matching of the cooling effects of UBGS. Two indices were proposed to evaluate cooling provision and demand of UBGS: Cooling Supply Index (CSI), accounting for cooling contribution and spatial effect, and Cooling Demand Index (CDI), considering physical and social dimensions of cooling demand. Four-quadrant analysis and bivariate local indicators of spatial autocorrelation (BiLISA) analysis were employed to distinguish the supply-demand matching status: high supply with high demand, low supply with high demand, low supply with low demand, and high supply with low demand. The results show that 1) CSI and CDI yield great performance in quantifying cooling supply and demand, respectively; 2) the four-quadrant analysis and bivariate LISA analysis are complementary to each other, and can target those areas in different matching statuses for climate-sensitive planning and design; and 3) particular attention should be given to areas characterized by low supply with high demand. This study marks a crucial step toward integrating social aspects into the analysis of cooling effects provided by UBGS. It enriches the analysis framework for urban ecosystem services and provides valuable guidance for landscape planning to mitigate heat stress.

Review of Existing Tools for the Assessment of European Building Stock Energy Demand for Space Heating and Cooling

There is currently a growing interest in lowering energy demand and, consequently, greenhouse gas emissions in all sectors. Several attempts by national governments to reduce energy demand are centered on the residential sector, since it accounts for a significant amount of the final energy demand. In order to estimate its energy demand and to evaluate the techno-economic effects of adopting energy efficiency and renewable energy technologies, there are comprehensive models suited for residential applications, since energy demand characteristics of the residential sector are complicated and interrelated. Based on these models, several tools are nowadays available to support designers and policymakers. These tools are designed to be user-friendly and to include the possibility to develop simulated scenarios for energy demand, production of CO2 emissions, and economic costs. The present study aims to offer an up-to-date extended overview of the most functional and widespread tools for the assessment of the current energy demand of the European building stock for space heating and cooling demand, both regarding open source and commercial licenses. Results highlighted the tools most commonly used by examining real applications, identifying their strengths and weaknesses and pinpointing the primary deficiencies for the benefit of future developers.

Mapping of Energy Consumption for Cooling – Assessment of the Cooling Demand Potential for the City of Riga

Abstract Considering the global increase in mean air temperature and society’s demand for a comfortable indoor microclimate, cooling solutions in buildings will play an essential role in achieving global climate goals. Researchers estimate that global energy consumption will be significantly affected by the energy required for cooling. Assessment of cooling demand potential is an essential step to further analyse cooling options for buildings in certain areas. Within the framework of this study, mapping of energy consumption for cooling in the city of Riga (Latvia) is carried out. Mapping allows to assess the demand in a spatial perspective, identifying areas with a denser energy demand indicator, and, accordingly, a higher potential for implementation of district cooling and this method can be used in other countries and cities as well. Energy consumption for cooling at the building level was calculated and visualized using cadastral data and building energy certificate data. The results show that the potential energy consumption for cooling in the city of Riga can reach up to 397 GWh per year and the largest proportion of energy consumption for cooling is predicted in the category of multi – apartment buildings (up to 155 GWh per year), highlighting the need to pay particular attention how cooling demand is addressed in residential building sector. Results can be further used to evaluate the technical and economic feasibility of the district cooling implementation in parts of the city.

Enhancing Thermal Efficiency through the Use of Graphite-Infused Phase Change Materials in Roof Structures to Reduce Building Cooling Demand

This research focuses on the thermal properties of three distinctive paraffin waxes—PCMA, PCMB, and PCMC—each characterized by a specific melting point. The crucial phase transition temperature intervals and latent heat values were examined using differential scanning calorimetry (DSC) in the temperature range of 0 °C to 80 °C. These parameters are pivotal for the effective application of these phase change materials (PCMs) in building envelopes, influencing the overall heat storage performance. The study delved into the development and encapsulation of blends containing both the phase change material (PCM) and graphite. This involves combining the chosen PCM with graphite powder and examining the weight ratios of 10% and 20%. The thermal characteristics of these blends revealed that a 10% ratio of graphite powder proved effective in improving the PCM with graphite. This resulted in a reduced range of melting and solidification temperatures while maintaining the essential chemical structure of the PCM without additives. Furthermore, the practical application of PCM–graphite composites within a building’s envelope was explored, revealing a substantial reduction in heat transfer from the exterior to the interior of the building. This underscores the potential for energy-efficient building designs.

Catalyzing Cooling Tower Efficiency: A Novel Energy Performance Indicator and Functional Unit Including Climate and Cooling Demand Normalization

Energy and climate targets necessitate efficiency indicators to reflect resource-saving potentials. Prevailing indicators for cooling towers, however, often omit the effect of outside conditions. Hence, this study introduces an innovative indicator grounded in the energy efficiency ratio. Our proposed metric is the cost–benefit ratio between electricity demand and the thermodynamic minimum airflow. Thus, we call the novel indicator the airflow performance indicator. To validate its feasibility, we apply the indicator first to an extensive dataset encompassing 6575 cooling tower models and second to a year-long case study involving a data center’s wet cooling system. As a result, the energy performance indicator demonstrates that dry cooling requires eight times more minimum airflow at the median than evaporative cooling would, directly correlating to the fan power. Furthermore, efficiency benchmarks derived from the dataset of 6575 cooling tower models provide a comparative assessment of the case study. Defining the quantified benefit as minimum airflow additionally underscores the limitations of free cooling as the wet cooling system only partly covers the cooling demand, requiring chillers additionally. In conclusion, the indicator empowers the identification of energy-saving potentials in the selection, design, and operation of cooling towers. Moreover, the functional unit definition provides a foundation for future life cycle assessments of cooling towers, enhancing cooling tower efficiency and sustainability.

Spatial Distribution of Future Demand for Space Cooling Applications and Potential of Solar Thermal Cooling Systems

Demand for space cooling systems is growing worldwide. The main reasons are socioeconomic developments such as the growing world population and the rise of economic wealth, especially in developing countries. These developments run simultaneously with global warming effects, increasing the need for cooling. This study introduces the development of the Cooling Demand Market Index (CDMI), which indicates the demand for cooling appliances worldwide at a spatial resolution of 1 km. It is based on population density, Gross Domestic Product (GDP)/capita and Cooling Degree Days (CDD) per climate zone. The CDMI is calculated for 2020 and 2050 in four different future scenarios in accordance with the Spatial Socioeconomic Pathways (SSP) and Representative Concentration Pathways (RCP). Further, the Solar Thermal Cooling Index (STCI) was developed to spatially estimate the worldwide potential to use solar thermal cooling systems based on solar availability and limitations due to maximum heat rejection temperature. Results of the CDMI show that the economic demand for cooling solutions is increasing, especially in developing countries, and that India will be by far the largest market by 2050. Countries such as Burundi and the Democratic Republic of the Congo show the strongest national increases in CDMI. The STCI indicates that ammonia absorption chillers and zeolite adsorption chillers can serve the vast majority of the market thanks to their capability to run at high condenser temperatures.

Time Series Forecast of Cooling Demand for Sustainable Chiller System in an Office Building in a Subtropical Climate

Commercial buildings can take up one-third of the energy related carbon emissions. There is limited research on forecasting cooling demands to evaluate sustainable air conditioning systems under climate change. This paper develops a simplified cooling demand model based on the time series of climatic and architectural variables to analyze carbon reduction by a sustainable chiller system. EnergyPlus is used to simulate hourly cooling demands of a hypothesized high-rise office building in Hong Kong under a change of architectural parameters and future climate conditions. An hourly cooling demand model with R2 above 0.9 is developed with inputs of the window-to-wall ratio, outdoor air enthalpy, global solar radiation, wind speed and their two steps ahead. The validated model is then used to analyze carbon reduction potentials by free cooling and a full variable speed chiller system. The low carbon technologies reduce carbon emissions by over 20% with but the reduction shrinks to 2.51–4.93% under future climate conditions. The novelty of this study is the simplified cooling demand model based on the time series of climatic and architectural variables. The significances of this study are to quantify carbon reduction by a sustainable chiller system under climate change and to appeal for more carbon reduction technologies for carbon neutrality.

Impact of Phase Change Materials on Cooling Demand of an Educational Facility in Cairo, Egypt

Heat gains and losses via building envelopes are impacted by varied characteristics such as geometry, orientation, properties of the building materials, and the type of construction and its interface with the exterior environment. Current studies are investigating the use of phase change materials (PCMs) characterized by high latent heat and low thermal conductivity that may cause temperature time lag and reduce amounts of heat transferred through building envelopes. The prime objectives of this research are evaluating zones’ energy consumption by type for an educational facility in a dry arid climate, examining the effects of a PCM (RT28HC) and polyurethane insulating material, comparing these effects to the existing situation with respect to cooling energy savings and CO2 emissions, and studying the effect of varying PCM thicknesses. The working methodology depended on gathering the real status and actual material of the building, constructing models of the building using Design Builder (DB) simulation software, and comparing the insulation effect of incorporating polyurethane and phase change insulating materials. A parametric study evaluated various PCM thicknesses (6, 12, 18, 24, 30, and 36 mm). Validation was performed primarily for a selected year’s energy usage; simulation results complied with field measurements. The results revealed that an 18 mm PCM had a high efficiency regarding thermal comfort attributes, which reduced cooling energy by 17.5% and CO2 emissions by 12.4%. Consequently, this study has shown the significant potential of PCM regarding improved energy utilization in buildings.

Dynamic heat transfer model of vertical green façades and its co-simulation with a building energy modelling program in hot-summer/warm-winter zones

Cooling demand dominates the energy consumption of buildings in hot-summer/warm-winter zones. Vertical green façades (VGFs) are an effective design strategy to reduce the cooling load in these regions. Many studies have investigated the cooling benefits of VGFs; however, the lack of accurate and low-cost co-simulation methods limits their application as it is difficult for architects and engineers to use them to evaluate the energy consumption of buildings. To address this issue, we developed a dynamic heat transfer (DHT) model for VGFs based on the Beer–Lambert Law and Penman–Monteith Equation. We then proposed a co-simulation approach for VGFs with EnergyPlus by using an additional heat source term and energy management system. We subsequently conducted field measurements to validate the accuracy of the DHT model and the reliability of the co-simulation approach. Finally, we created simulation scenarios in four typical cities in the hot-summer/warm-winter zone of China and analysed the effects of a VGF on the cooling load of an office building. The results indicated that the VGF reduced the room cooling load by 11.7–18.4%. Furthermore, improving the wall insulation only led to a 0.3–8.4% reduction in the room cooling load. Moreover, the cooling load saving rates of rooms with a VGF and well-insulated walls were 6.6–15.8%. Our research is likely to prove useful to future researchers investigating VGFs using DHTs and building designers attempting to effectively incorporate VGFs into architectural plans.

Global Radiative Sky Cooling Potential Adjusted for Population Density and Cooling Demand

Thanks to recent advances in nanophotonics and scalable manufacturing of metamaterials, radiative sky cooling has emerged as a “self-reliant” cooling technology with various potential applications. However, not every region across the globe is well suited for the adoption of radiative cooling technologies, depending on the local climate, population density, cooling demand, air conditioning saturation, economic prosperity, etc. Because the atmospheric downward longwave radiation, especially the portion from the atmospheric window (8–13 µm), is substantially affected by weather conditions, the performance of a well-designed radiative cooler can be vastly different across regions and seasons. Here, we first map the global radiative sky cooling potential in the form of net cooling power density. We then further evaluate it based on the global population density and cooling demand. In terms of the adjusted potential, we show that geographically and demographically “transitional” regions, located between wet and dry climates as well as sparsely and densely populated regions, are better suited for the adoption of radiative cooling technologies because of their temperate climate and moderate population density. Even in densely populated and humid regions, the cumulative impact and other accompanying benefits must not be ignored.

Identifying household cooling savings potential in the hot summer and cold winter climate zone in China: A stochastic demand frontier approach

Cooling demand increases more than ten times over the past two decades in China. Especially, cooling in the Hot Summer and Cold Winter (HSCW) climate zone increases rapidly and becomes the fastest growing part in urban residential energy consumption. It will bring huge challenges to carbon emission cap control if effective and reasonable energy-saving and emission reduction measures are not taken. This study uses a data-driven method to identify cooling savings potential at household level using survey data from 1068 households in 2018 in Chongqing, China. A stochastic frontier analysis model is applied to decompose the actual cooling electricity consumption into minimum consumption based on fixed household characteristics and estimate the over-consumption (i.e. the amount of cooling consumption that could be saved). The results show that households have an average cooling efficiency of 65 %, which indicates that each household in the HSCW area has the potential to reduce its cooling electricity consumption by 35% while still maintaining the same level of cooling services currently produced. The study shows that the cooling savings potential is strongly influenced by level of household income, the level of building energy efficiency standards, cooling behavior characteristics and households’ energy-saving consciousness. Policy implications are derived.

Climate Change and Building Energy Consumption: A Review of the Impact of Weather Parameters Influenced by Climate Change on Household Heating and Cooling Demands of Buildings

The growth of urban population as the result of economic and industrial development has changed our place of living from a prosperous place to where the resources are carelessly consumed. On the other hand, long-term climate change, i.e. global warming, has had adverse impact on our resources. Certain resources are on the verge of depletion as the consequence of climate change and inconsiderate consumption of resources, unless serious measures are implemented immediately. The building sector, whose share in the municipal energy consumption is considerably high, is a key player that may successfully solve the problem. This paper aims to study the effects of climate change on the energy consumption of buildings and analyze its magnitude to increase the awareness of how construction can reduce the overall global energy consumption. A descriptive-analytical method has been applied to analyze valid models of energy consumption according to different scenarios and to interpret the conditions underlying current and future energy consumption of buildings. The results clearly show that the energy consumption in the building sector increasingly depends on the cooling demand. With that being said, we can expect the reduction of overall energy consumption of buildings in regions with high heating demands, whereas rising the energy consumption in buildings is expected in regions with high cooling demand. To conclude, the long-term climate change (e.g. global warming) underlies the increased energy consumption for the cooling demand whose share in total energy consumption of buildings much outweighs the heating demand. Therefore, to conserve our resources, urban energy planning and management should focus on working up a proper framework of guidelines on how to mitigate the cooling loads in the energy consumption patterns of buildings.

Parsimonious Modelling for Estimating Hospital Cooling Demand to Improve Energy Efficiency

Abstract Of all the different types of public buildings, hospitals are the biggest energy consumers. Cooling systems for air conditioning and healthcare uses are particularly energy intensive. Forecasting hospital thermal-cooling demand is a remarkable and innovative method capable of improving the overall energy efficiency of an entire cooling system. Predictive models allow users to forecast the activity of water-cooled generators and adapt power generation to the real demand expected for the day ahead, while avoiding inefficient subcooling. In addition, the maintenance costs related to unnecessary starts and stops and power-generator breakdowns occurring over the long term can be reduced. This study is based on the operations of a real hospital facility and details the steps taken to develop an optimal and efficient model based on a genetic methodology that searches for low-complexity models through feature selection, parameter tuning and parsimonious model selection. The methodology, called GAparsimony, has been tested with neural networks, support vector machines and gradient boosting techniques. Finally, a weighted combination of the three best models was created. The new operational method employed herein can be replicated in similar buildings with similar water-cooled generators.

The Effect of Coupling Solar Thermal System and Geothermal Heat Pump Systems in Areas with Unbalanced Heating and Cooling Demand

Geothermal source heat pump (GSHP) systems as renewable energy systems are being more frequently installed as part of the zero-energy building drive. However, in South Korea, where a large amount of heating load can be required, maintaining high system performance by using only a GSHP is difficult owing to the gradual degradation of its thermal performance. The performance of a solar-assisted GSHP system was therefore experimentally analyzed and compared with a GSHP-only system. The results showed that the heating coefficient of performance of the GSHP-only operation was 5.4, while that of the solar-assisted GSHP operation was 7.0. In the case of the GSHP-only system, the maximum temperature of the heat pump water supply on the heat source side was initially 13.1 °C, but this rapidly decreased to 11.4 °C during operation. For the solar-assisted GSHP system, the temperature of the water supply to the heat source side of the heat pump was controlled at 15–20.9 °C, and the power consumption for system operation was reduced by about 20% compared with that for the GSHP-only system. Much higher temperatures could be supplied when solar heat is used instead of ground heat, as solar heat contributes to the performance improvement of the heat pump system.

Advancement in Urban Climate Modelling at Local Scale: Urban Heat Island Mitigation and Building Cooling Demand

As cities and their population are subjected to climate change and urban heat islands, it is paramount to have the means to understand the local urban climate and propose mitigation measures, especially at neighbourhood, local and building scales. A framework is presented, where the urban climate is studied by coupling a meteorological model to a building-resolved local urban climate model, and where an urban climate model is coupled to a building energy simulation model. The urban climate model allows for studies at local scale, combining modelling of wind and buoyancy with computational fluid dynamics, radiative exchange and heat and mass transport in porous materials including evaporative cooling at street canyon and neighbourhood scale. This coupled model takes into account the hygrothermal behaviour of porous materials and vegetation subjected to variations of wetting, sun, wind, humidity and temperature. The model is driven by climate predictions from a mesoscale meteorological model including urban parametrisation. Building energy demand, such as cooling demand during heat waves, can be evaluated. This integrated approach not only allows for the design of adapted buildings, but also urban environments that can mitigate the negative effects of future climate change and increased urban heat islands. Mitigation solutions for urban heat island effect and heat waves, including vegetation, evaporative cooling pavements and neighbourhood morphology, are assessed in terms of pedestrian comfort and building (cooling) energy consumption.

A Critical Review of Façade Retrofit Measures for Minimizing Heating and Cooling Demand in Existing Buildings

The building industry is currently experiencing a myriad of challenges, including low energy performance in existing buildings. Since the façade considerably contributes to heating and cooling in buildings, façade retrofit is presumed as an effective solution to minimize cooling and heating demand in existing buildings.In-depth scrutiny in façade retrofit actions reveals different Façade Retrofit Measures (FRMs) that aim to minimize cooling and heating demand in existing buildings. They include Energy Conservation Measures (ECMs), which prevent excessive heat transmittance, and Energy Modulation Measures (EMMs), which modulate energy consumption through passive heating and cooling strategies. A Combination of Measures (CoMs) is also the state-of-the-art in the face of retrofit actions.In this paper, a further analysis elaborates on the effectiveness of each measure in Cooling-Dominated (CD) and Heating-Dominated (HD) climates. This analysis showed that façade retrofit could reduce energy consumption for heating and cooling by up to 50% through architectural interventions.The study findings also revealed that ECMs effectively minimize energy consumption in a heating-dominated climate by lowering conductive and convective heat transfer. However, EMMs are more crucial in reducing energy consumption in a cooling-dominated climate by controlling radiative heat transfer.

Self-Learning Algorithm to Predict Indoor Temperature and Cooling Demand from Smart WiFi Thermostat in a Residential Building

Smart WiFi thermostats have moved well beyond the function they were originally designed for; namely, controlling heating and cooling comfort in buildings. They are now also learning from occupant behaviors and permit occupants to control their comfort remotely. This research seeks to go beyond this state of the art by utilizing smart WiFi thermostat data in residences to develop dynamic predictive models for room temperature and cooling/heating demand. These models can then be used to estimate the energy savings from new thermostat temperature schedules and estimate peak load reduction achievable from maintaining a residence in a minimum thermal comfort condition. Back Propagation Neural Network (BPNN), Long-Short Term Memory (LSTM), and Encoder-Decoder LSTM dynamic models are explored. Results demonstrate that LSTM outperforms BPNN and Encoder-Decoder LSTM approach, yielding and a MAE error of 0.5 °C, equal to the resolution error of the measured temperature. Additionally, the models developed are shown to be highly accurate in predicting savings from aggressive thermostat set point schedules, yielding deep reduction of up to 14.3% for heating and cooling, as well as significant energy reduction from curtailed thermal comfort in response to a high demand event.

The critical role of humidity in modeling summer electricity demand across the United States

Cooling demand is projected to increase under climate change. However, most of the existing projections are based on rising air temperatures alone, ignoring that rising temperatures are associated with increased humidity; a lethal combination that could significantly increase morbidity and mortality rates during extreme heat events. We bridge this gap by identifying the key measures of heat stress, considering both air temperature and near-surface humidity, in characterizing the climate sensitivity of electricity demand at a national scale. Here we show that in many of the high energy consuming states, such as California and Texas, projections based on air temperature alone underestimates cooling demand by as much as 10–15% under both present and future climate scenarios. Our results establish that air temperature is a necessary but not sufficient variable for adequately characterizing the climate sensitivity of cooling load, and that near-surface humidity plays an equally important role.

Effects of Rooftop Photovoltaics on Building Cooling Demand and Sensible Heat Flux Into the Environment for an Installation on a White Roof

Abstract Photovoltaic (PV) panels are commonly used for on-site generation of electricity in urban environments, specifically on rooftops. However, their implementation on rooftops poses potential (positive and negative) impacts on the heating and cooling energy demand of buildings, and on the surrounding urban climate. The adverse consequences can be compounded if PV is installed on top of an otherwise highly reflective (“white”) rooftop. This study investigates these impacts on a test building in Tempe, AZ, by directly measuring the temperature of all involved surfaces. These measurements are supplemented by whole-building energy simulations to model the energy implications for archetypical residential and retail buildings. This includes calculations of the ratio of the energy demand penalty to electricity generation as well as the net sensible heat flux to the ambient environment. Results indicate that the summertime cooling energy penalty due to blockage of outgoing longwave radiation can be 4.9—11.2% of the PV electricity generation. The addition of PV to the white roof resulted in a small decrease in the computed sensible heat flux at night, but a daytime increase in sensible flux by more than a factor of 10 (from less than 25 W/m2 for the white roof alone, to more than 250 W/m2 when PV is added to the roof). This study highlights the potential unintended consequences of rooftop PV under certain conditions and provides a broader perspective for building designers and urban planners.

Risk and Uncertainty Analysis of Cooling Demand in Multi-Chiller System Using Downside Risk Constraints Method

The optimal performance of a multi-chiller system (MCS) is the crucial factor in managing the expected power consumption (EPC). Also, the uncertainty of cooling demand in the industrial or residential sector plays a crucial role, which should be modeled and managed. So, the stochastic risk-constrained performance of the optimal chiller loading is studied in an uncertain environment in this paper. Scenario-based stochastic programming is applied to the provided case study to model the cooling demand uncertainty (CDU), and the downside risk constraints (DRC) are implemented to model the associated risks. The risk-averse performance of the MCS is compared with the risk-neutral one to show the positive effects of the DRC. The proposed model is implemented under the DICOPT solver in GAMS software. The comparison results show that the expected power consumption of MCS is increased slowly, while the expected risk-in-power consumption (ERIPC) is decreased promptly.