The COVID19 pandemic has impacted the global economy, social activities, and Electricity Consumption (EC), affecting the performance of historical data-based Electricity Load Forecasting (ELF) algorithms. This study thoroughly analyses the pandemic's impact on these models and develop a hybrid model with better prediction accuracy using COVID19 data. Existing datasets are reviewed, and their limited generalization potential for the COVID19 period is highlighted. A dataset of 96 residential customers, comprising 36 and six months before and after the pandemic, is collected, posing significant challenges for current models. The proposed model employs convolutional layers for feature extraction, gated recurrent nets for temporal feature learning, and a self-attention module for feature selection, leading to better generalization for predicting EC patterns. Our proposed model outperforms existing models, as demonstrated by a detailed ablation study using our dataset. For instance, it achieves an average reduction of 0.56% & 3.46% in MSE, 1.5% & 5.07% in RMSE, and 11.81% & 13.19% in MAPE over the pre- and post-pandemic data, respectively. However, further research is required to address the varied nature of the data. These findings have significant implications for improving ELF algorithms during pandemics and other significant events that disrupt historical data patterns.

Many studies conducted previously have reported that due to lockdowns or stay-at-home orders associated with the COVID-19 pandemic in April 2020 residential power consumption has increased in countries, particularly in cities worldwide. This study compared the power consumption of 1,339 detached houses in Japan over the past three years as well as a year after the pandemic and analyzed living behavioral changes in the 12 months after the pandemic using a questionnaire survey of occupants. As of March 2021, which is after 12 months of the beginning of the pandemic, it was confirmed that the way of life had returned to almost normal, and as a factor in increasing consumption, working from home would remain the only behavioral change that may take root in Japanese society.

In the era of post-Coronavirus Disease 2019, the dedicated outdoor air system (DOAS), which provides 100% outdoor air for the building, is widely acknowledged as it can ensure acceptable indoor air quality by delivering fresh outdoor air to occupied space. The DOAS with a proper design and operation can provide sufficient ventilation and dehumidification while achieving energy efficiency. Nonetheless, there is limited guidance in determining the optimal control sequence of the DOAS for the designers and operators to implement in practice. Accordingly, in practice, a number of issues have been acknowledged in the design and control phases of DOAS, including insufficient ventilation and dehumidification, and increasing supply air dry-bulb temperature in fear of over-cooling, which might cause significant discomfort and energy waste. There have been efforts to develop high-performing DOAS controls for better energy efficiency. However, such controls are often complex, or difficult to interpret, for building designers and operators to consider in practice. In this regard, this paper explores a simulation-based framework for generating a supply air temperature control sequence of the DOAS not only to ensure improved energy-saving potential but also to guarantee the implement-ability of the control logic. The U.S Department of Energy prototype primary school with dynamic occupancy profiles was modeled with a whole building simulation program, EnergyPlus. The model consists of a DOAS with an exhaust air energy recovery system for ventilation and fan-coil units for space cooling and heating. Then, a Genetic Algorithm was adopted to find the true optimal supply air temperature control sequence in terms of minimizing the energy cost of the heating, ventilation, and air conditioning system operation. Lastly, Decision Tree was adopted to extract rules out of the optimums to derive an implementable sequence of operation for the DOAS supply air temperature. A total of 12 week-simulation including four weeks of heating, cooling, and shoulder seasons, separately, under the weather condition of New York City was conducted for the case study. This case study identified that the optimization-informed rule extraction-based control, when compared to conventional outdoor air temperature-based reset control, could save about 13% of energy cost and 25% of energy consumption throughout the heating, cooling, and shoulder seasons. It is notable that the energy-saving was mainly achieved by reducing the heating energy consumption. Importantly, it nearly corresponds to the true optimal control result, which reduces approximately 14% of energy cost and 27% of energy consumption. From the results, it can be highlighted that the optimization-informed rule extraction can be as energy effective as the optimal control, while significantly reducing the complexity of the control.

The ready-made garment industry is critical to the Bangladesh economy. There is an urgent need to improve current working conditions and build capacity for heat mitigation as conditions worsen due to climate change. We modelled a typical, mid-sized, non-air-conditioned factory in Bangladesh and simulated how the indoor thermal environment is altered by four rooftop retrofits (1. extensive green roof, 2. rooftop shading, 3. white cool roof, 4. insulated white cool roof) on present-day and future decades under different climate scenarios. Simulations showed that all strategies reduce indoor air temperatures by around 2 °C on average and reduce the number of present-day annual work-hours during which wetbulb globe temperature exceeds the standardised limits for moderate work rates by up to 603 h - the equivalent of 75 (8 h) working days per year. By 2050 under a high-emissions scenario, indoor conditions with a rooftop intervention are comparable to present-day conditions. To reduce the growing need for carbon-intensive air-conditioning, sustainable heat mitigation strategies need to be incorporated into a wider range of solutions at the individual, building, and urban level. The results presented here have implications for factory planning and retrofit design, and may inform policies targeting worker health, well-being, and productivity.

Investigation of housing indoor temperature is important for understanding the comfort, health and living conditions of the local residents. The traditional method to measure indoor temperature is to place sensors at the target places, which is not only expensive but also inconvenient for indoor temperature investigation, especially for the investigation at community and city scale. In this study, a novel method was proposed to obtain the indoor temperatures remotely from outside the building through window opening area using and infrared camera. Compared with the traditional contact measurement method, the proposed remote sensing method could detect the indoor temperature without entering the room. Moreover, the infrared image could reflect the spatial distribution information of indoor temperature. To verify the feasibility and accuracy of this method, an experiment was conducted in a test room under heating, transitional, and cooling conditions with various window opening grades. It was found that the infrared images at the window opening area could reflect the spatial distribution of indoor temperature with an accuracy within 0.5 °C under stable heating and transitional conditions. In the fan coil cooling condition, however, although the infrared image can reflect the cold air flow pattern, the deviations between the infrared temperature and the measured room temperature exceeded 1.0 °C. The effect of window opening grade on the recognition accuracy kept within 0.5 °C.

Since the COVID-19 outbreak, the restrictive policies enacted by countries in response to the epidemic have led to changes in the movement of people in public places, which has had a direct impact on the use and energy consumption of various public buildings. This study was based on electricity consumption data for 25 on-campus public buildings at 1-hour intervals between January 2020 and June 2022 at Tewnte University in the Netherlands, and after the data were climate-corrected by multiple regression analysis, the changes in EU and EUI for various types of buildings were compared for different restriction periods using ANOVA, LSD and t-tests. And additionally, further analyzed the changes and reasons for the electricity consumption of various public buildings on campus and customers' electricity consumption behavior in a period of time after the lifting of the epidemic restriction policy. The results of ANOVA analysis show that the restriction policy has a significant effect on teaching, sports, and cultural buildings, and the electricity intensity of the three types of buildings is reduced by 0.28, 0.09, and 0.07 kwh/m/day respectively under the strict restriction policy; The -test results show that during the restriction period, all building types, except for living and academic buildings, show a significant decreasing trend, with the teaching buildings having the greatest energy saving potential, with an average daily EU reduction of 1088kwh/day and an EUI reduction of 0.075kwh/ m/day. The above findings provide a case study of a complete cycle of energy consumption changes in university buildings under similar epidemic restriction policies before and after the epidemic restriction, and inform the electricity allocation policies of university and government energy management authorities.

With increasing energy use and outbreaks of respiratory infectious diseases (such as COVID-19) in buildings, there is a growing interest in creating healthy and energy-efficient indoor environments. A novel heating system named low-temperature radiant floor coupled with intermittent stratum ventilation (LTR-ISV) is proposed in this study. Thermal performance, indoor air quality, energy and exergy performance were investigated and compared with conventional radiant floor heating (CRFH) and conventional radiant floor heating with mixing ventilation (CRFH + MV). The results indicated that LTR-ISV had a more uniform operative temperature distribution and overall thermal sensation, and air mixing was enhanced without generating additional draft sensation. Compared with CRFH and CRFH + MV, the indoor CO concentration in LTR-ISV can be reduced by 1355 ppm and 400 ppm, respectively. Airborne transmission risk can also be reduced by 5.35 times. The coefficient of performance for CRFH, CRFH + MV, and LTR-ISV during working hours was 4.2, 2.5, and 3.4, respectively. The lower value of LTR-ISV was due to the high energy usage of the primary air handing unit. In the non-working hours, LTR-ISV was 0.6 and 1.3 higher compared to CRFH and CRFH + MV, respectively. The exergy efficiency of LTR-ISV, CRFH, and CRFH + MV was 81.77 %, 76.43 %, and 64.71 %, respectively. Therefore, the LTR-ISV system can meet the requirements of high indoor air quality and thermal comfort and provides a reference for the energy-saving use of low-grade energy in space heating.

With the outbreak of infectious diseases such as Corona Virus Disease 2019, medical staff work intensively in isolated plots, medical disposable protective clothing (MDPC) has poor air condition and humidity permeability, which seriously reduces the thermal comfort of medical staff. In this paper, the effect of indoor thermal environment and activity levels on thermal comfort inside MDPC was studied by experiment. Five parts of the body were measured inside MDPC and the appropriate movements were chosen to simulate different levels of labor intensity. Meanwhile, physiological parameters and subjective thermal sensation were statistically analyzed. The results showed the influence range of different indoor temperatures on the temperature and humidity inside MDPC was about 1 °C and 10 %, respectively; it indicated that the environment inside MDPC could be improved by reducing indoor temperature, that is, a cross intelligent adjustment mode was proposed. The effect of labor intensity on the temperature inside MDPC was significantly less than that of humidity. Within 20 min, the humidity changes under moderate and heavy labor intensity were even more than 10 %, and the subjective discomfort threshold of the subjects increased by nearly 50 %. Furthermore, the maximum benefit could be obtained by concentrating cooling on back, forehead, chest and upper arm. Theoretical models of working time, labor intensity, and temperature and humidity inside MDPC under different indoor temperatures and different parts were given. In addition, acceptable regions inside MDPC which were approximately parallelogram in the enthalpy-humidity chart. These conclusions could be a reference for future thermal comfort inside MDPC research.

Stringent lockdowns have been one of the defining features of the COVID-19 pandemic. Lockdowns have brought about drastic changes in living styles, including increased residential occupancy and telework practices predicted to last long. The variation in occupancy pattern and energy use needs to be assessed at the household level. Consequently, the new occupancy times will impact the performance of energy efficiency measures. To address these gaps, this work uses a real case study, a two-story residential building in the Okanagan Valley (British Columbia, Canada). Further, steady-state building energy simulations are performed on the HOT2000 tool to evaluate the resiliency of energy efficiency measures under a full lockdown. Three-year monitored energy data is analyzed to study the implications of COVID-19 lockdowns on HVAC and non-HVAC loads at a monthly temporal scale. The results show a marked change in energy use patterns and a higher increase in May 2020 compared to the previous two years. Calibrated energy models built on HOT2000 are then used to study the impacts of pre-COVID-19 (old normal occupancy) and post-COVID-19 (new normal occupancy) on energy upgrades performance. The simulations show that under higher occupancy times, the annual electricity use increased by 16.4%, while natural gas use decreased by 7.6%. The results indicate that overall residential buildings following pre-COVID-19 occupancy schedules had higher energy-saving potential than those with new normal occupancy. In addition, the variation in occupancy and stakeholder preferences directly impact the ranking of energy efficiency measures. Furthermore, this study identifies energy efficiency measures that provide flexibility for the decision-makers by identifying low-cost options feasible under a range of occupancy schedules.

During the COVID-19 pandemic, strict stay-at-home orders have been implemented in many Chinese universities in virus-hit regions. While changes in electricity consumption in the residential sector caused by COVID-19 have been thoroughly analysed, there is a lack of insight into the impact of the stay-at-home order on electricity consumption in university dormitory buildings. Based on questionnaire survey results, this study adopted the statistical Kaplan-Meier survival analysis to analyse the energy-use behaviours of university students in dormitories during the COVID-19 pandemic. The electricity load profiles of the dormitory buildings before and during the implementation of the stay-at-home order were generated and compared to quantitatively analyse the influence of COVID-19 pandemic on the energy-use behaviours of university students, and the proposed load forecasting method was validated by comparing the forecasting results with monitoring data on electricity consumption. The results showed that: 1) during the implementation of the stay-at-home order, electricity consumption in the university dormitory buildings increased by 41.05%; 2) due to the increased use of illuminating lamps, laptops, and public direct drinking machines, the daily electricity consumption increased most significantly from 13:00 to 18:00, with an increase rate of 97.15%; and 3) the morning peak shifted backward and the evening peak shifted forward, demonstrating the effect of implementing the stay-at-home order on reshaping load profiles.

In this paper, we present the concept and formulation of a short-term Markov corrector to an underlying day-ahead building load forecasting model. The models and the correctors are then integrated to the building supervision, control and data acquisition system to automate the self-updating and retraining processes. The proposed Markov corrector is experimentally proven to significantly improve the reactivity of the forecasting models with respect to untaught variations. Developed in a discrete manner over a continuous forecasting model, the corrector also helps to capture better the consumption peaks during the activity days. A proof-of-concept is demonstrated via the case study of the GreenER building, where the impact of the Markov correctors to the performance of the existing day-ahead load forecasting system (based on Prophet model) was analyzed during the 2021/2022 winter, under the influences of the Omicron wave of the COVID-19 pandemic.

After the outbreak of COVID-19, the indoor environment has become particularly important in closed spaces, being a common concern in environmental science and public health, and of great significance for the building environment. To improve the indoor air quality and control the spread of viruses, the analysis of inhalable particles in indoor environments is critical. In this research, we study standards focused on inhalable particles and indoor environmental quality, as well as analyzing the movement and diffusion of indoor particles. Based on our analysis, we conduct an experimental study to determine the distribution of indoor inhalable particles of different sizes before and after diffusion under the conditions of underfloor air distribution. Furthermore, the mathematical modeling method is adopted to simulate the indoor flow field, particle trajectories, and pollutant dispersion process. The k-ε two-equation model is applied as the turbulence model in the numerical simulation, while the Lagrangian discrete phase model is adopted to trace the motion of particles and analyze the distribution characteristics of indoor particles. The results demonstrate that fine particles (i.e., those with size less than 0.5 μm) have a significant impact on the indoor particle concentration, while coarse particles (i.e., with size above 2.5 μm) have a greater influence on the total mass concentration of indoor particles. Small-sized particles can easily follow the airflow and diffuse to upper parts of the room. Overall, the effects of indoor particles on indoor air quality, including the potential threat of aerosol transmission of respiratory infectious diseases, are non-negligible. Application of the presented research can contribute to improving the health-related aspects of the building environment.

Anthropogenic greenhouse gas emissions are driving global increases in temperature. This rise will likely lead to an increase in demand for cooling in the coming years. However, increasing temperatures are not the main explanatory factor for why the world is moving towards more cooling. This paper compares population and area-weighted cooling and heating degree-days derived using ERA5-Land reanalysis temperature, to show that population growth in warmer parts of the world drives cooling demand globally. The analysis shows that mean global area-weighted heating degree-days have fallen 8.46 °C days/year, whereas population-weighted heating degree-days have fallen by 12.5 °C days/year. At the same time, mean global area-weighted cooling degree-days have risen by 3.0 °C days/year, while population-weighted cooling degree-days have risen at 6.0 °C days/year. By using sub-country analysis, this paper shows that population-weighted degree-days can substantially differ from area-weighted degree-days. Finally, the findings highlight that the choice of heating and cooling degree-day base temperature is the most important parameter in the variability of degree-days and will need to be understood better in order to accurately account for future heating and cooling energy demand.

A growing number of studies have showed energy demand changes during COVID-19; this study aims to further disclose the impact of the restriction and easing policies on the energy consumption of public buildings where occupants' usage and activities are regulated in response to the pandemic. This study analyzes half-hourly electricity consumption data of 35 public buildings covering 6 building types in the Perth and Kinross Council area in Scotland, U.K., over the span of 2020 and 2021. The results show that the restriction has a greater impact on the electricity reduction in the first year of the pandemic than that in the second year. In response to the restriction, the electricity use intensity of all public buildings reduces significantly (p < 0.001) except office buildings with no significant reduction (p > 0.05); secondary schools have the highest electricity consumption reduction (275.04 kwh/day), while museums have the lowest reduction (58.62 kwh/day). In addition, the electricity consumption and electricity use intensity of museum, library and school buildings are inversely proportional to the restriction intensity, while this is opposite for office buildings. Combing restriction intensity and mobility data, this research reveals the different impacts of the restriction policies on the electricity consumption of public buildings during the pandemic, which reflects people's changing attitudes and behaviors towards COVID-19. The results provide a reference basis for energy management to develop more realistic energy demand policies based on public building types and to optimize the electricity supply load and energy profile during the COVID-19 pandemic.

Natural ventilation is considered the first suggestion for COVID-19 prevention in buildings by the World Health Organization (WHO). Solar chimney's viability in aged care centers or similar facilities was analyzed numerically and theoretically. A new solar chimney design was proposed to reduce the cross-infection risk of COVID-19 based on an airflow path through window, ceiling vent, attic, and then chimney cavity. Solar chimney performance, quantified by the natural ventilation rate, presented power function with window area, ceiling vent area, cavity height, and solar radiation. The ceiling vent is suggested to be closer to the corridor to enhance the performance and ventilation coverage of the room. A cavity gap of 1.0 m is recommended to balance the ventilation performance and construction cost. A theoretical model was also developed for aged care centers with multiple rooms and a joint attic. Its predictions obey reasonably well with the numerical results. Solar chimney's viability in aged care center is confirmed as a 7.22 air change per hour (ACH) ventilation can be achieved even under a low solar radiation intensity of 200 W/m, where its performance fulfills the minimal ventilation requirement (, 6 ACH) suggested by the WHO for airborne infection isolation rooms. This study offers a new design and a guideline for the future implementation of solar chimney in aged care centers or similar facilities.

Partial occupancy of commercial offices has become the norm in the wake of the COVID-19 pandemic. Given this, occupant-centric control (OCC), which adapt building systems based on occupants' presence or preferences, offer an alternative to traditional control that assumes full occupancy. However, poor sequences of operation can degrade the benefits of OCC. This paper explores this interaction by examining energy data from two buildings - one with two control logic faults corrected and an occupancy-based ventilation OCC implemented in 2020, and one with traditional ventilation - from 2019 to 2020. Sequences that impacted implementation in the first building are discussed. Then, a calibrated energy model of the second building is developed to evaluate how occupancy-based ventilation alongside changes to the sequences of operation - namely supply air temperature (SAT) reset and economizer high limits - impacted energy use. The inclusion of OCC and improved sequences in the second building saved 30.6% and 9.6% of annual heating and cooling energy, respectively. Without an SAT reset, OCC saved 4.4% and 3.9% of heating and cooling, respectively, compared to 15.7% and 5.7% when an SAT was present. These results begin to characterize the relationship sequences of operation and OCC implementations have with one another in commercial offices.

Restricting social distancing is an effective means of controlling the COVID-19 pandemic, resulting in a sharp drop in the utilization of commercial buildings. However, the specific changes in the operating parameters are not clear. This study aims to quantify the impact of COVID-19 lockdowns on commercial building energy consumption and the indoor environment, including correlation analysis. A large green commercial building in Dalian, China's only country to experience five lockdowns, has been chosen. We compared the performance during the lockdown to the same period last year. The study found that the first lockdown caused a maximum 63.5% drop in monthly energy consumption, and the second lockdown was 55.2%. The energy consumption per unit area in 2020 dropped by 55.4% compared with 2019. In addition, during the lockdown, the compliance rate of indoor thermal environment increased by 34.7%, and indoor air quality was 9.5%. These findings could partly explain the short-term and far-reaching effects of the lockdown on the operating parameters of large commercial buildings. Humans are likely to coexist with COVID-19 for a long time, and commercial buildings have to adapt to new energy and health demands. Effective management strategies need to be developed.

The COVID-19 pandemic has led to considerable morbidity and mortality, and consumed enormous resources (e.g. energy) to control and prevent the disease. It is crucial to balance infection risk and energy consumption when reducing the spread of infection. In this study, a quantitative human, behavior-based, infection risk-energy consumption model for different indoor environments was developed. An optimal balance point for each indoor environment can be obtained using the anti-problem method. For this study we selected Wangjing Block, one of the most densely populated places in Beijing, as an example. Under the current ventilation standard (30 m/h/person), prevention and control of the COVID-19 pandemic would be insufficient because the basic reproduction number ( ) for students, workers and elders are greater than 1. The optimal required fresh air ventilation rates in most indoor environments are near or below 60 m/h/person, after considering the combined effects of multiple mitigation measures. In residences, sports buildings and restaurants, the demand for fresh air ventilation rate is relatively high. After our global optimization of infection risk control (  ≤ 1), energy consumption can be reduced by 13.7% and 45.1% on weekdays and weekends, respectively, in contrast to a strategy of strict control (  = 1 for each indoor environment).

The implementation of the movement control order (MCO) to curb the spread of the 2019 novel corona virus disease (COVID-19) have influenced household energy consumption patterns around the world. This study aims to investigate household energy consumption of urban residential buildings in major cities of Indonesia during COVID-19 pandemic. Three representative major cities of Indonesia were selected to investigate detailed information about household appliances and gas consumption through face-to-face interviews in 2021 (n = 311). The factors affecting household energy consumption were investigated by multiple regression analysis. The results showed that, overall, the average annual energy consumption of all samples during pandemic was approximately 23.5 GJ, 3.0 GJ larger than before pandemic. The difference was primarily attributed to the use of air conditioning and cooking. The statistical analysis clearly indicated that the increase in household income (low-to high-cost houses), which would increase household size and number of appliances including air conditioning, thus increased total household energy consumption. We recommended the following potential energy-saving strategies for urban houses in Indonesia: (a) control the number of family members, (b) use more energy efficiency standards for electrical appliances and (c) encourage energy-saving lifestyles, particularly to younger adults by adopting passive cooling techniques (window opening) whereever possible.

COVID-19 has forced people to spend more time working and studying at home; in particular, people who share an apartment stay in their respective bedrooms almost all day. This study investigated the impact of ventilation modes on the indoor air quality (IAQ) of 24-hour occupied bedrooms and provided ventilation suggestions for people who stay in their bedrooms for a long time during the pandemic compared with the study of traditional apartment ventilation. In addition, the fresh air energy consumption of different ventilation modes was compared to help residents save energy. In summer, a window-opening ratio of 25% (0.3 m) could effectively improve IAQ. However, it is not recommended to use natural ventilation in winter because the outdoor PM concentration is too high. Moreover, the fresh air energy consumption for the automatic control window-opening ratio was 1/5 of that for a window-opening ratio of 25%. In the whole summer, it can save 196.1 kW·h compared to a fixed window-opening ratio of 25%. Fresh air systems could greatly improve IAQ and lower energy consumption regardless of the season. However, the automatic-control window-opening ratio mode has lower energy consumption, which is approximately 0.37 times that of fresh air systems in summer.

"Stay-at-home" orders and other health precautions enacted during the COVID-19 pandemic have led to substantial changes in residential electricity usage. We conduct a case study to analyze data from 390 apartments in New York City (NYC) to examine the impacts of two key drivers of residential electricity usage: COVID-19 case-loads and the outdoor temperature. We develop a series of regression models to predict two characteristics of residential electricity usage on weekdays: The average occupied apartment's consumption (kWh) over a 9am-5pm window and the hourly peak demand (Watt) over a 12pm-5pm window. Via a Monte Carlo simulation, we forecast the two usage characteristics under a possible scenario in which stay-at-home orders in NYC, or a similar metropolitan region, coincide with warm summer weather. Under the scenario, the 9am-5pm residential electricity usage on weekdays is predicted to be 15% - 24% higher than under prior, pre-pandemic conditions. This could lead to substantially higher utility costs for residents. Additionally, we predict that the residential hourly peak demand between 12pm and 5pm on weekdays could be 35% - 53% higher than that under pre-pandemic conditions. We conclude that the projected increase in peak demand - which might arise if stay-at-home guidelines coincided with hot weather conditions - could pose grid management challenges, especially for residential feeders. We also note that, if there is a longer lasting shift towards work and study-from-home, utilities will have to rethink load profile considerations. The applications of our predictive models to managing future smart-grid technology are also highlighted.