The COVID-19 pandemic's impact on U.S. electricity demand and supply: an early view from the data

After the onset of the recent COVID-19 pandemic, a number of studies reported on possible changes in electricity consumption trends. The overall theme of these reports was that ``electricity use has decreased during the pandemic, but the power grid is still reliable''---mostly due to reduced economic activity. In this paper we analyze electricity data upto end of May 2020, examining both electricity demand and variables that can indicate stress on the power grid, such as peak demand and demand ramp-rate. We limit this study to three states in the USA: New York, California, and Florida. The results indicate that the effect of the pandemic on electricity demand is not a simple reduction from comparable time frames, and there are noticeable differences among regions. The variables that can indicate stress on the grid also conveyed mixed messages: some indicate an increase in stress, some indicate a decrease, and some do not indicate any clear difference. A positive message is that some of the changes that were observed around the time stay-at-home orders were issued appeared to revert back by May 2020. A key challenge in ascribing any observed change to the pandemic is correcting for weather. We provide a weather-correction method, apply it to a small city-wide area, and discuss the implications of the estimated changes in demand. The weather correction exercise underscored that weather-correction is as challenging as it is important

Role of rain as perception aid in assessing wind speeds and associated personal risks

Extreme event perception drives personal risks and, consequently, dictates household decision-making before, during, and after extreme events. Given this, increasing the extreme event perception accuracy of the public is important to improving decision-making in extreme event scenarios; however, limited research has been done on this subject. Results of a laboratory experiment, in which 76 human participants were exposed to hurricane-strength weather conditions, and asked to estimate their intensities and associated personal risks, is presented in this article. Participants were exposed to a range of identical wind speeds (20, 40, 60 mph) with (8 in/hr) and without rain. They then provided estimates of the perceived wind and rain (when present) speeds, and associated personal risks on a nominal scale of 0 to 10. Improvements in the accuracy of wind-speed perception at higher speeds were observed when rain was present in the wind field (41.5 and 69.1 mph) than when it was not (45.2 and 75.8 mph) for 40 and 60 mph wind speed exposures respectively. In contrast, risk perceptions were similar for both rain and non-rain conditions. This is particularly interesting because participants failed to estimate rain intensities (both horizontal and wind-driven rain) by a significant margin. We discuss the possible implications of rain as a perception aid to wind and the viability of using perception aids to better convey extreme weather risks. The article is concluded with revisiting discussions about the implications of past hurricane experience on wind intensity perception, personal risk assessment, and future directions in extreme weather risk perception research

Forecasting completed cost of highway construction projects using LASSO regularized regression

Finishing highway projects within budget is critical for state highway agencies (SHAs) because budget overruns can result in severe damage to their reputation and credibility. Cost overruns in highway projects have plagued public agencies globally. Hence, this research aims to develop a parametric cost estimation model for SHAs to forecast the completed project cost prior to project execution to take necessary measures to prevent cost escalation. Ordinary least square (OLS) regression has been a commonly used parametric estimation method in the literature. However, OLS regression has certain limitations. It, for instance, requires strict statistical assumptions. This paper proposes an alternative approach—least absolute shrinkage and selection operator (LASSO)—that has proved in other fields of research to be significantly better than the OLS method in many respects, including automatic feature selection, the ability to handle highly correlated data, ease of interpretability, and numerical stability of the model predictions. Another contribution to the body of knowledge is that this study simultaneously explores project-related variables with some economic factors that have not been used in previous research, but economic conditions are widely considered to be influential on highway construction costs. The data were separated into two groups: one for training the model and the other for validation purposes. Using the same dataset, both LASSO and OLS were used to build models, and then their performance was evaluated based on the mean absolute error, mean absolute percentage error, and root mean square error. The results showed that the LASSO regression model outperformed the OLS regression model based on the criteria

Energy use assessment of educational buildings: Toward a campus-wide sustainable energy policy

The purpose of this article is to assess the viability of blanket sustainability policies, such as Building Rating Systems in achieving energy efficiency in university campus buildings. We analyzed the energy consumption trends of 10 LEED-certified buildings and 14 non-LEED certified buildings at a major university in the US. Energy Use Intensity (EUI) of the LEED buildings was significantly higher (EUILEED= 331.20 kBtu/sf/yr) than non-LEED buildings (EUInon-LEED=222.70 kBtu/sf/yr); however, the median EUI values were comparable (EUILEED= 172.64 and EUInon-LEED= 178.16). Because the distributions of EUI values were non-symmetrical in this dataset, both measures can be used for energy comparisons—this was also evident when EUI computations exclude outliers, EUILEED=171.82 and EUInon-LEED=195.41. Additional analyses were conducted to further explore the impact of LEED certification on university campus buildings energy performance. No statistically significant differences were observed between certified and non-certified buildings through a range of robust comparison criteria. These findings were then leveraged to devise strategies to achieve sustainable energy policies for university campus buildings and to identify potential issues with portfolio level building energy performance comparisons

Building energy simulation and parallel computing: Opportunities and challenges

Increased focus on energy cost savings and carbon footprint reduction efforts improved the visibility of building energy simulation, which became a mandatory requirement of several building rating systems. Despite developments in building energy simulation algorithms and user interfaces, there are some major challenges associated with building energy simulation; an important one is the computational demands and processing time. In this paper, we analyze the opportunities and challenges associated with this topic while executing a set of 275 parametric energy models simultaneously in EnergyPlus using a High Performance Computing (HPC) cluster. Successful parallel computing implementation of building energy simulations will not only improve the time necessary to get the results and enable scenario development for different design considerations, but also might enable Dynamic-Building Information Modeling (BIM) integration and near real-time decision-making. This paper concludes with the discussions on future directions and opportunities associated with building energy modeling simulations

Improved procedures for business accommodation on transportation construction projects

Highway construction projects have direct impacts on adjacent businesses. The nature and the degree of impact depend on individual business characterization and project specific factors. The type of business is also a relevant factor in predicting the impact of transportation construction projects. This paper presents the results of research focused on developing an in-depth understanding of these relationships. The study includes project case studies of three transportation construction projects in Florida. Surveys were conducted with all adjacent businesses, which were combined with analyses of the business accommodation procedures employed by State Highway Agencies (SHAs) nationwide to provide measure the efficiency of present rules. The results include an analysis of differing priorities for different classification of businesses and development of design and construction management best practices to better accommodate businesses during highway construction. A pilot project that employed business accommodation principles devised in this research, and improvements to business accommodations observed were compared to cases where no measures were taken

A framework to analyze the impact of sustainable infrastructure on human productivity

The built environment has a profound impact on our natural environment, economy, health and productivity. As the majority of the people spent most of their time inside buildings, the environment in which they perform their daily activities will have an impact on their health and productivity. Studies have been conducted about the negative impacts of presence of non-favorable conditions to human health and well being. The term "Sick Building Syndrome" (SBS) is used to describe situations in which building occupants experience acute health and comfort problems that appear to be linked to their time spent in a building. Sustainable infrastructure rating systems have requirements intended to improve occupant productivity and health.While the impact of Sustainable Infrastructure in energy consumption and waste/water reduction can be measured using available tools, the impact on productivity remained as an assumption that is not clearly measured. The purpose of this research is to develop a framework to assess whether the impacts of the incorporation of features intended to improve occupants’ performance and health such as: increased ventilation, lightning and thermal comfort serve their intended purpose

The potential of XML technology as an answer to the data interchange problems of the construction industry

The complex supply chain relations of the construction industry, coupled with the substantial amount of information to be shared on a regular basis between the parties involved, make the traditional paper‐based data interchange methods inefficient, error prone and expensive. The successful information technology (IT) applications that enable seamless data interchange, such as the Electronic Data Interchange (EDI) systems, have generally failed to be successfully implemented in the construction industry. An alternative emerging technology, Extensible Markup Language (XML), and its applicability to streamline business processes and to improve data interchange methods within the construction industry are analysed, as is the EDI technology to identify the strategic advantages that XML technology provides to overcome the barriers to implementation. In addition, the successful implementation of XML‐based automated data interchange platforms for a large organization, and the proposed benefits thereof, are presented as a case study.Information technology, electronic data interchange, extensible markup language,