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Demand‐side management (DSM) enables customers to decide consciously on how to seek and obtain power from the grid. The prevailing method available in DSM is load shifting. The grid is assisted through reducing load demands during the peak hours and altering the demand time into the off‐peak hours in a manner that the consumption sources could be m...
Citations
... This can yield the result of increasing customer satisfaction (Li et al., 2019). Otherwise, these projects cannot be preferable, the sustainability of these projects would be in jeopardy (Ghaffari and Afsharchi, 2021). Caputo et al. (2018) aimed to identify the critical factors of the performance improvement regarding smart grid projects. ...
Smart grid systems help increase RWJ projects (RWJ) so that environmentally friendly energy production can be generated. However, efficient technologies should be implemented to ensure the sustainability of smart grid systems. This study aims to evaluate renewable-friendly smart grid technologies regarding distributed energy investment projects by using a hybrid picture fuzzy rough decision-making approach. Firstly, selected criteria are weighted using the multi stepwise weight assessment ratio analysis (M-SWARA) method based on picture fuzzy rough sets (PFRSs). Subsequently, different renewable-friendly smart grid technologies are ranked with the complex proportional assessment (COPRAS) technique by using PFRSs. It is determined that research and development play the most critical role with respect to the renewable-friendly smart grid technologies for distributed energy investment projects. On the other side, cost is another essential factor for this issue. It is also identified that direct current links are the most important renewable-friendly smart grid technology alternative. Priorities should be given to the development of research and development studies on renewable energies to increase the efficiency of smart grid systems. In this context, private sector companies have a very important role. Similarly, incentives provided by governments to RWJ research and development studies should be increased. Within the scope of these studies, new technologies for RWJ types should be emphasized. In this context, new technologies for all RWJ alternatives should be followed comprehensively. Increasing research and development for such investments will also make smart grid systems more successful.
... Machine Learning (ML) approaches have been significantly used to design detection and prediction systems (Ghaffari & Afsharchi, 2020). Detection systems extract features of a phenomenon, like a disease, and detect the possibility of occurring by having some observations and witnesses. ...
To manage the propagation of infectious diseases, particularly fast-spreading pandemics, it is necessary to provide information about possible infected places and individuals, however, it needs diagnostic tests and is time-consuming and expensive. To smooth these issues, and motivated by the current Coronavirus disease (COVID-19) pandemic, in this paper, we propose a learning-based system and a hidden Markov model (i) to assess hazardous places of a contagious disease, and (ii) to predict the probability of individuals’ infection. To this end, we track the trajectories of individuals in an environment. For evaluating the models and the approaches, we use the Covid-19 outbreak in an urban environment as a case study. Individuals in a closed population are explicitly represented by their movement trajectories over a period of time. The simulation results demonstrate that by adjusting the communicable disease parameters, the detector system and the predictor system are able to correctly assess the hazardous places and determine the infection possibility of individuals and cluster them accurately with high probability, i.e., on average more than 96%. In general, the proposed approaches to assessing hazardous places and predicting the infection possibility of individuals can be applied to contagious diseases by tailoring them to the influential features of the disease.
