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The COVID-19 pandemic has significantly affected people's behavioral patterns and schedules because of stay-at-home orders and a reduction of social interactions. Therefore, the shape of electrical loads associated with residential buildings has also changed. In this paper, we quantify the changes and perform a detailed analysis on how the load shapes have changed, and we make potential recommendations for utilities to handle peak load and demand response. Our analysis incorporates data from before and after the onset of the COVID-19 pandemic, from an Alabama Power Smart Neighborhood with energy-efficient/smart devices, using around 40 advanced metering infrastructure data points. This paper highlights the energy usage pattern changes between weekdays and weekends pre– and post–COVID-19 pandemic times. The weekend usage patterns look similar pre– and post–COVID-19 pandemic, but weekday patterns show significant changes. We also compare energy use of the Smart Neighborhood with a traditional neighborhood to better understand how energy-efficient/smart devices can provide energy savings, especially because of increased work-from-home situations. HVAC and water heating remain the largest consumers of electricity in residential homes, and our findings indicate an even further increase in energy use by these systems. [ABSTRACT FROM AUTHOR]

[Display omitted] • The communal areas can have different levels of energy performance in the 15 types of public residential buildings under study. • Spatial design features can evidently affect the energy consumption in the communal areas. • The ability to harvest daylight serves as a key determinant of energy efficiency. • Both econometric and machine learning techniques are proved to be effective to generate reliable findings. In this paper, we construct a unique database for 1228 residential buildings in Hong Kong to investigate how the spatial features of these residential buildings affect the electricity consumption in the communal area. We choose Hong Kong for this analysis as the city owns a large number of standard-type residential buildings managed by the public institution, which could be affected strongly by environmental policies. Both the machine learning method, based on the Least Absolute Shrinkage and Selection Operator (LASSO), and econometric regressions are adopted to analyse the data. We first utilize the machine learning LASSO technique to identify the most relevant factors for the subsequent econometric analysis. Our results show that the electricity demand for relatively low consumption building types, such as Twin Tower, is 6% lower than that of the high consumption building types. Newly constructed buildings usually belong to the medium consumption types, with the estimated monthly electricity consumption per apartment in communal areas to be around 50.2 kWh on average in 2020. These findings shed light on the nexus between spatial features and energy use for complex buildings, potentially contributing to the better crafting of energy-saving policy and the improvement of residential building programmes. [ABSTRACT FROM AUTHOR]

• A new retrofitting strategy by using prefabricated elements window machine. • Minimization of the renovation costs and speeding up the retrofit action. • A potential in energy saving and improving the Indoor Environment Quality. • Further significance in retrofit and healthy home office working environments. The renovation of existing residential buildings across Europe has become a key factor for energy saving and the improvement of Indoor Environment Quality (IEQ). This paper presents an investigation of IEQ for a typical 1960s single-family house in Germany before and after a new retrofitting strategy (prefabricated element with integrated ventilation and new thermal envelope). In-situ measurements were applied to investigate the energy efficiency and IEQ improvement for the new retrofitting strategy in temperate winter regions of Europe. The simulation model replicates the measured data to be closer to reality and simulates a home office (COVID-19) scenario. The in-situ measurements showed that our retrofitting strategy increases the indoor air temperature during the heating period by up to 2.5 °C, decreases relative humidity by up to 3.8% and increases CO 2 by up to 24 ppm than before. As the simulation results shows, the retrofitted home office scenario allows a reduction in heating energy of up to 77%, an increase in CO 2 concentration of up to 41.8 ppm, 0.5% less relative humidity and 1.6 °C higher indoor air temperature than that without retrofitting. The new approach to retrofit residential buildings thus showed a potential in energy saving and improving the IEQ. [ABSTRACT FROM AUTHOR]

• The types and benefits of occupancy-based temperature controls for residential buildings are reviewed. • A systematic analysis approach is performed for a wide range of temperature controls specific to Saudi households. • Smart occupancy-controls can save on average 36% of annual energy consumption for a typical KSA household. The paper describes the potential energy efficiency and demand response benefits of occupancy-based temperature controls applicable to residential buildings for the Kingdom of Saudi Arabia (KSA). Specifically, various spatial and temporal temperature controls are evaluated to account for spatial and temporal occupancy patterns associated to typical living styles of Saudi households. The analysis results show that the benefits of occupancy-based temperature settings can be significant when at least two thermal zones controlled by different thermostats exist within the same housing unit. Specifically, occupancy-based thermostat settings can save up to 38.7% of total annual electricity consumption for a dwelling located in Riyadh. Moreover, when the indoor temperature settings are adjusted spatially, a reduction of up to 34.7% in peak demand can result for most KSA regions. Using a bottom-up analysis approach, it is found that when scaling up to the entire KSA housing stock, the adoption of smart thermostats has the potential to reduce annual electricity use by 48 TWh/year or over 30% of the national residential sector energy consumption. [ABSTRACT FROM AUTHOR]