Demand Response (DR) has gained popularity in recent years as a practical strategy to increase the sustainability of energy systems while reducing associated costs. Despite this, Artificial Intelligence (AI) and Machine Learning (ML), have recently developed as critical technologies for demand-side management and response due to the high complexity of tasks associated with DR, as well as huge amount of data management to take decisions very near to real time implications. Selecting the best group of users to respond, learning their attitude toward consumptions and their priorities, price optimization, monitoring and control of devices, learning to engage more and more consumers in the DR schemes, and learning how to remunerate them fairly and economically are all problems that can be tackled with the help of AI techniques. This study presents an overview of AI approaches used for DR applications. Both the Artificial Intelligence and Machine Learning algorithm(s) are employed while discussing commercial efforts (from both new and existing businesses) and large-scale innovation projects that have applied AI technologies for energy DR. Different kind of DR programs implemented in different countries are also discussed. Moreover, it also discusses the application of blockchain for DR schemes in smart grid paradigm. Discussion of the strengths and weaknesses of the evaluated AI methods for various DR tasks, as well as suggestions for further study, round out the work.INDEX TERMS Artificial intelligence, blockchain, demand response, demand side management, demand response, Internet of Things (IoT), smart grids, machine learning.

No abstract

No abstract

File labeling techniques have a long history in analyzing the anthological trends in computational linguistics. The situation becomes worse in the case of files downloaded into systems from the Internet. Currently, most users either have to change file names manually or leave a meaningless name of the files, which increases the time to search required files and results in redundancy and duplications of user files. Currently, no significant work is done on automated file labeling during the organization of heterogeneous user files. A few attempts have been made in topic modeling. However, one major drawback of current topic modeling approaches is better results. They rely on specific language types and domain similarity of the data. In this research, machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus. A different file labeling technique has also been used to get the meaningful and `cohesive topic of the files. The results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

No abstract

Introduction: The future medical education demands newer strategies to enforce positive teaching change. The Pakistan Medical & Dental Council (PM&DC) has added outcomes such as leadership and management, professionalism, communication skills, and personal professional development. However, the level of difficulty and the importance of the contents need to be identified for the undergraduate medical and dental students.

No abstract

Wind energy generation provides one of the best and economical solution to the growing power demand. Doubly Fed induction generator is one of the most important variable speed wind generators. Integrating advanced controllers improves its performance and efficiency. The purpose of this research work is to optimize the wind energy conversion and efficient wind power extraction by controlling rotor current. Four controllers that are PI, Fuzzy, Hybrid Fuzzy-PI and Neural network based Adaptive Neuro Fuzzy controller are designed and implemented on DFIG. Controllers’ performance is assessed in terms of transients in rotor current i.e., percentage overshoot, settling time and steady state error under varying wind speed operation. Comparison of the results demonstrates that Adaptive Neuro Fuzzy controller outperforms as compared to that of Hybrid Fuzzy-PI, Fuzzy and conventional PI regarding transient response and maximum power extraction efficiency.