Healthcare facilities in the United States account for 4.8% of the total area in the commercial sector and are responsible for 10.3% of total energy consumption in this sector. The number of healthcare facilities increased by 22% since 2003, leading to a 21% rise in energy consumption and an 8% reduction in energy intensity per unit of area (544.8 kWh/m²). This study provides an analytical overview of the end-use energy consumption data in healthcare systems for hospitals in the United States. The energy intensity of the U.S. hospitals ranges from 640.7 kWh/m² in Zone 5 (very hot) to 781.1 kWh/m² in Zone 1 (very cold), with an average of 738.5 kWh/m². This is approximately 2.6 times higher than that of other commercial buildings. High energy intensity in the healthcare facilities, particularly in hospitals, along with energy costs and associated environmental concerns make energy analysis crucial for this type of facility. The proposed analysis shows that U.S. healthcare facilities have higher energy intensity than those of most other countries, especially the European ones. This necessitates the adoption of more energy-efficient approaches to the infrastructure and the management of healthcare facilities in the United States.

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.

This paper begins with the recognition that climate zones influence nonprocess energy use in industrial buildings. Nonprocess energies are heating, cooling, lighting, and ventilation. Nonprocess energy data have been collected from the literature (about 68 buildings) across a wide range of climate zones. The hypothesis tested in this research is: if an industrial building has a characteristic nonprocess energy related to physical dimensions and desired comfort level, then using cooling degrees day (CDD) and heating degrees day (HDD) factors can normalize the measured nonprocess temperature control data for the climate zone differences. That is, do measured nonprocess energy intensities (W/m²), if corrected for climate zone differences, within each building category become more similar and hence reflecting the basic building temperature control energy use? The five U.S. climate zones and the location for each facility in this study were identified. To investigate how the location influences the amount of heating and cooling at each facility, a baseline analysis of five representative cities in each zone was done to obtain the 5-year average CDD and HDD. The reported values of heating and cooling for each facility were then adjusted using this baseline and the climate zones of that facility, so that each facility was then referenced to zone 3; that is, as if all manufacturing facilities were in the same zone 3. The mean, median, standard deviation, and total nonprocess energies for current and zone-adjusted nonprocess energy for each facility in this study were calculated. The mean values of current and adjusted heating and cooling remained close to each other and the standard deviation was not reduced by these adjustments. Thus, the hypothesis of using CDD/HDD to quantitatively account for and hence to adjust for different climate zones appears to not be valid. The absence of improvement (reducing the standard deviation) by normalizing heating and cooling energy using adjustment for climate factors using the concept of CDD/HDD implies that some other correction principles are needed for evaluating fundamental needs for industrial building heating and cooling. The inability to reduce the geographic (that is, climate zone) effects of industrial plant nonprocess energy intensities supports the de-emphasis of this tool in the ASHRAE Handbook.

In this study, published information on non-process energy use, which includes lighting, heating, cooling, ventilation, humidity control, and particulate control, for industrial buildings has been analyzed and compiled and then represented in power intensity (W/ft²). More than 30 different sources of data related to industrial building energy use (covering about 82 buildings) were identified and analyzed. Energy receives a lot of consideration in analyzing prospective savings in industrial facilities since the industrial sector consumes more than 30% of the total energy in the United States. The main objective of this study is to establish representative ranges for non-process energy use that can then be used with full process the life cycle for industrial products. The industrial manufacturing buildings were classified into six categories according to non-process energy use. The power intensity for each building category was estimated from the available data. Previous analyses of industrial energy use often expressed non-process energy as a percentage of total energy but without clear values of actual non-process energy. This information is a low value because the actual non-process energy is then dominated by the dominator which is unspecified.

In this study, four important techniques for estimating the non-process energy (heating, cooling, lighting and ventilation) in industrial and manufacturing buildings were examined. The building energy data for two industrial facilities were collected and analysed. The building non-process energy includes lighting, heating, cooling and ventilation. The power intensity (kW/m²) for each energy type use was estimated using two or more methods (direct measurement of non-process energy, direct measurement of process energy, regression and simulation) and then analysed. The results of these estimates were close to each other which indicate that any method can be used based on available information. This non-process energy needs to be clearly defined to allow more quantitative improvements. The utilization of percentage of non-process energy in industrial and manufacturing plants is an important potential source of energy and cost improvements. Previous analysis of industrial energy use often expressed non-process energy as a percentage of total energy, but without clear values of actual non-process energy. This information is a low value since the actual non-process energy is then dominated by the dominator which is unspecified.

In this study, published information on nonprocess energy use, which includes lighting, heating, cooling, ventilation, humidity control, and particulate control, for industrial buildings has been analyzed and compiled and then represented in power intensity (W/ft²). More than thirty different sources of data related to industrial building energy use (covering about 82 buildings) were identified and analyzed. Energy receives a lot of consideration in analyzing prospective savings in industrial facilities since the industrial sector consumes more than 30% of the total energy in the US. The main objective of this study is to establish representative ranges for nonprocess energy use that can then be used with full process the life cycle for industrial products. The industrial manufacturing buildings were classified into six categories according to nonprocess energy use. The power intensity for each building category was estimated from the available data. Previous analyses of industrial energy use often expressed nonprocess energy as a percentage of total energy but without clear values of actual nonprocess energy. This information is a low value since the actual nonprocess energy is then dominated by the dominator which is unspecified.