ABSTRACT
Internet of things is progressing very rapidly and involving multiple domains of everyday life including environment, governance, healthcare system, transportation system, energy management system, etc. smart city is a platform for collecting and storing the information that is accessed through various sensor-based IoT devices and make their information available in required and authorized domains. This interoperability can be achieved by semantic web technology. In this paper, I have reviewed multiple papers related to IoT in Smart Cities and presented a comparison among the semantic parameters. Moreover, I've presented my future domain of research which is about delivering the COVID-19 patients report to the concerned domains by the healthcare system domain. © 2023 IEEE.
ABSTRACT
Owing to the COVID-19 pandemic, many companies have introduced working from home to avoid the risk of infection. In this study, we conducted questionnaire surveys and analysed the building energy management system (BEMS) in an office building where the number of employees working from home increased after the onset of the pandemic. The influence of working from home on the indoor environment satisfaction and the variability in energy consumption at home and office was determined. The indoor environment satisfaction was significantly higher when working from home than when working at the office. In 2020, the total energy consumption at home and office decreased by 30% in April and increased by 22% in August compared to the previous year. To work from home while saving energy regardless of the season, it is necessary to reduce office energy consumption by decreasing the number of workers present at the office. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.
ABSTRACT
Manufacturing facilities use about 35% of the domestic energy in the United States every year. Implementing an effective energy management system (EnMS) is one of the most important approaches to improve energy efficiency. However, the implementation of EnMS is low for many countries (including the US) and even for energy-intensive sectors. The reasons for the low implementation rate of energy management systems had been investigated by multiple researchers, but very few studies have focused on the barriers and challenges of implementing ISO 50001-based energy management systems. To contribute to this understudied area, this paper discusses the implementation and outcomes of the first Better Plants 50001 Ready Virtual In-plant Training. This paper first provides an overview of 50001 Ready and the 50001 Ready Navigator Tool. Then, it provides details on this training event and its outcomes. Finally, it discusses findings from the responses to 40 live polling questions about the status of the 25 tasks of the 50001 Ready Navigator for participating companies, key components of the participating manufacturing companies’ energy management systems, and challenges and barriers that these companies are facing. The findings suggest that although many companies understood the importance of an effective energy management system, about half of them do not understand the required resources for building energy management systems, and most of them have only just begun establishing these systems and need more assistance and resources in multiple areas. More specifically, more assistance is necessary for the following: (1) improving corporate management’s understanding of the time and resources needed to build an EnMS as well as the benefits;(2) creating linear regression models for more accurate energy performance tracking;(3) understanding energy use, collecting and analyzing energy performance data;(4) optimizing equipment operational controls, and creating action plans.
ABSTRACT
Nowadays, large-scale natural disasters have frequently occurred at many places. To deal with the interruption of electricity supply due to natural disasters, many companies require to enhance resilience of office buildings. Introducing remote work is one of the ways to control their power demand. However, the existing disaster energy simulation models have not considered in detail how building users use electricity in the case of a grid outage, so the introduction of remote work could not be considered. This study proposes a model in which business damage costs are incurred according to the reduction in the number of employees in the office. In the proposed model, the power demand is broken down by the priority of working in the office. Using the cost model, we construct the simulation method that minimizes the cost of business damage in the case of a disaster by moving to remote work. We apply operational data to our simulation model assuming offices before and during the COVID-19 pandemic, and conduct simulations of business continuity for 72 hours in the case of a sudden grid outage. From the results, it was found that the cost of business damage can be suppressed when remote work is regularly introduced during COVID-19. © 2022 IEEJ-IAS.
ABSTRACT
During this decade, many countries have experienced natural and accidental disasters, such as typhoons, floods, earthquakes, and nuclear plant accidents, causing catastrophic damage to infrastructures. Since the end of 2019, all countries of the world are struggling with the COVID-19 and pursuing countermeasures, including inoculation of vaccine, and changes in our lifestyle and social structures. All these experiences have made the residents in the affected regions keenly aware of the need for new infrastructures that are resilient and autonomous, so that vital lifelines are secured during calamities. A paradigm shift has been taking place toward reorganizing the energy social service management in many countries, including Japan, by effective use of sustainable energy and new supply schemes. However, such new power sources and supply schemes would affect the power grid through intermittency of power output and the deterioration of power quality and service. Therefore, new social infrastructures and novel management systems to supply energy and social service will be required. In this paper, user-friendly design, operation and control assist tools for resilient microgrids and autonomous communities are proposed and applied to the standard microgrid to verify its effectiveness and performance. © 2022
ABSTRACT
Electricity demand has been disrupted in various countries since many governments imposed comprehensive social restriction policies to control the COVID-19 pandemic. Obtaining accurate electricity consumption predictions in this highly uncertain period is particularly important for building operators to improve the corresponding operational planning efficacy. Nevertheless, developing accurate electricity consumption prediction models for buildings within the COVID-19 context is a nontrivial task. Correspondingly, this research focuses on incorporating publicly available internet data (i.e., Google Trends, Google Mobility, and COVID-19 data) to develop accurate electricity consumption prediction models for microgrid-based buildings during the COVID 19 pandemic. For this purpose, we developed extreme gradient boosting (XGBoost), support vector regression (SVR), and autoregressive integrated moving average with explanatory variable (ARIMAX) models. As a case study, we analyzed a real-life electricity consumption dataset of a six-floor microgrid-designed educational building at a technological university in Bandung, West Java, Indonesia. The findings show that incorporating publicly online data positively impacts prediction accuracy. The accuracy increases, even more when we use the lagged value of the predictors. XGBoost models utilizing lagged historical values of the electricity consumption, Google Trends, and COVID-19 data of the previous days is the best performing model. However, adding more lagged predictors does not necessarily increase SVR models’accuracy. Lastly, the ARIMAX models become the worst-performing models compared to XGBoost and SVR models. Author
ABSTRACT
The paper considers a residential consumer living in Ontario, who is a customer of Hydro One, an electricity transmission and distribution service provider. The customer is the owner of a smart home with smart appliances who can participate in any demand response (DR) program by any communication medium. The customer has a solar photovoltaic set up on the roof, a battery energy storage system, and an electric vehicle with some thermostatically and non-thermostatically controlled appliances. The price tariffs of the country are rapidly changing due to the economic attack of Covid-19. These changing tariffs are considered as scenarios, and the effect of the tariffs on the customer’s electricity bill is analyzed. The scenarios are formulated as a simple mixed integer linear programming problem. All the events are optimized by different DR programs using the CPLEX solver of GAMS software for minimization of the cost. This paper also investigates the peak to average ratio of the power demand in each scenario. Different strategies for controlling the power transfer from the grid are employed as DR programs, and the results are analyzed in detail. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
This article reviews energy management schemes for smart homes integrated with renewable energy resources in the context of the COVID-19 pandemic. The incorporation of distributed renewable energy system has initiated an acute transition from the traditional centralized energy management system to independent demand responsive energy systems. Renewable energy-based Smart Home Energy Management Systems (SHEMSs) play a vital role in the residential sector with the increased and dynamic electricity demand during the COVID-19 pandemic to enhance the efficacy, sustainability, economical benefits, and energy conservation for a distribution system. In this regard, the reviews of various energy management schemes for smart homes appliances and associated challenges has been presented. Different energy scheduling controller techniques have also been analyzed and compared in the COVID-19 framework by reviewing several cases from the literature. The utilization and benefits of renewable-based SHEMS have also been discussed. In addition, both micro and macro-level socio-economic implications of COVID-19 on SHEMSs are discussed. A conclusion has been drawn given the strengths and limitations of different energy scheduling controllers and optimization techniques in the context of the COVID-19 pandemic. It is observed that renewable-energy-based SHEMS with improved multi-objective meta-heuristic optimization algorithms employing artificial intelligence are better suited to deal with the dynamic residential energy demand in the pandemic. It is hoped that this review, as a fundamental platform, will facilitate the researchers aiming to investigate the performance of energy management and demand response schemes for further improvement, especially during the pandemic.
ABSTRACT
The COVID-19 pandemic has rapidly changed our lives. While the global impacts of the pandemic are shocking, the implications for energy burdens, climate policy, and energy efficiency are salient. This study examines income differences in the acceptance of and willingness to pay for home energy management systems during the COVID-19 pandemic among 632 residents in New York. Additionally, this study examines energy profiles, energy burdens, climate change issues, risk perceptions, and social-psychological factors. Compared with low-income households, our findings suggest that high-income households use more energy, have higher utility bills during quarantine mandates, perceive a higher risk of COVID-19 infection, and perceive climate change issues to be better than before. Low-income households, however, experience the highest energy burdens. Regarding HEMS acceptance, high-income households are more willing to adopt energy and well-being-promoting features of HEMS and more willing to pay a higher monthly fee for all the features than other income groups. Overall, participants were more willing to pay a higher price for the energy features than the well-being-promoting features. Low-income households indicate lower social norms, personal norms, and perceived behavioral control over adopting HEMS; they also perceive HEMS to be more difficult to use and less useful. Higher-income households express a higher trust in utilities than low-income households. Surprisingly, cost concerns, technology anxiety, and cybersecurity concerns relating to HEMS do not differ across income groups. This paper addresses the interactions among technology attributes and social-psychological and demographic factors, and provides policy implications and insights for future research.