ABSTRACT
The COVID-19 pandemic triggered a question of how to measure and evaluate adequacy of the applied restrictions. Available studies propose various methods mainly grouped to statistical and machine learning techniques. The current paper joins this line of research by introducing a simple-yet-accurate linear regression model which eliminates effects of weekly cycle, available daylight, temperature, and wind from the electricity consumption data. The model is validated using real data and enables the qualitative analysis of economical impact. © 2022 IEEE.
ABSTRACT
A central issue that was recently discussed within the commercial real estate community is how the COVID-19 pandemic, and possible future pandemic events, will affect the operational performance in the long-term. Various additional factors such as confinement restrictions, lockdowns, and the reduced occupancy levels put operators under significant pressure, and force them to look for energy efficient solutions and find the right balance for the financial side while ensuring healthy environment for clients and tenants. In this paper, we present a preliminary analysis and evaluate the impact of the pandemic using real-life data collected from several commercial buildings in Estonia. The results indicate that after two years of the pandemic the situation stabilized, which may support the claim that the right balance was found at least in the current situation. © 2022 IEEE.
ABSTRACT
Practical work is one of the most important instructional tools in control engineering. To address concerns linked to the cost and space requirements of traditional hands-on laboratories, technology-enabled laboratory modes, such as virtual, remote, and take-home laboratory modes are proposed. Each of these alliterative laboratory modes has its own set of benefits and emphasizes a distinct learning goal. Furthermore, due to lockdown and physical proximity restrictions imposed by policies in response to the COVID-19 pandemic, the employment of these laboratory modes has been quickly increasing. The laboratories' development, operation, and maintenance become more fragmented as a result of these many possibilities. In this study, we propose " ReImagine Lab" as a framework for leveraging digital twins and extended reality technologies to streamline the development and operation of hands-on, virtual, and remote laboratories. By increasing the level of interaction, immersion, and collaboration in technology-enabled laboratory forms, this framework intends to boost student engagement. The benefits of this framework are demonstrated by examining several use cases, and a 37-person "system usability study" is conducted to assess the usability of virtual laboratories employing desktop computers and immersive virtual reality.
ABSTRACT
A central issue that was recently discussed within the power systems community is how the COVID-19 pandemic, and possibly other future pandemics, will affect the integration of renewable energy sources in the long-term. An interesting idea that may shed light on this question is the one of "economic shocks", according to which, if the integration of renewable sources can be described as a dynamic system operating on time-scales of years, then several months of low consumption may be viewed as a negative impulse signal (a shock), which causes reactions and counter-reactions that evolve in a closed feedback loop. In this paper we explore this idea by means of a regression model, which attempts to reflect the relations between fossil-fuel based generation and day-ahead electricity prices and demand in the European market. The results point out to an interesting phenomenon of instability in fossil-fuel based generation following a shock in consumption, which may support the claim that pandemics and other economic shocks may promote the future integration of renewable energy sources.
ABSTRACT
The COVID-19 pandemic and the preventive measures that followed it have significantly impacted generation and consumption patterns in power systems across the globe, calling for new tools to efficiently evaluate generation and load profiles. In this work, we propose one possible tool, which we name the smoothness index, that evaluates a profile based on both its load factor and ramp rate. The proposed index is a modification of the coefficient of variation, also known as the relative standard deviation, and is computed by transforming the profile under study to the frequency domain, and calculating the weighted norm of the signal, where higher frequencies are associated with larger weights. We demonstrate our method by studying the effects of the COVID-19 pandemic and government policies on load and generation profiles in several European countries. © 2021 IEEE.