T. Baker
Virtual-Paralegals LLC,
United States
Keywords: power reliability and usability with artificial generative intelligent and adversarial network
Summary:
ABSTRACT Power Reliability and Usability Artificial General Intelligence (AGI) and Generative Adversarial Network of Power Market. The paper objective to defined the different methodology for the reliability with usage of different indexes. With the AGI and Adversarial Network of Power Market. Heuristic: The digitalization of the FERC policies and regulations of any jurisdiction that is stipulated during the reliability reporting of January 2023. The clustering of datasets which canopy the tracking of the northwest region and specific performance of the balancing authorities-based reliability reporting studies and Electric Bulk Supply (EBS). Whereby all references of Power Market Study domain of the Northwest Region and the with various components. The architecture model and neural network is structured and developed as an architecture of the timeframe Dec 2016 to Nov 2017. The balancing authorities' areas of the Northwest region. The heuristic development of multi-layers of neural networks. The input which integrates the seasons of (winter, spring, fall and summer) based on the reliability reporting indexes (CAIDI, SAIDI, MAIDI, CAIFI, MAIFI, SAIFI) cumulative peaks. Output of Quality of the dataset and result in consumer surge in pricing or number which resulted in the FECR citation and docket number delegate in 2020. Structural/Clustering of the Data: architecture of the January 2023 power market study of the clustering of balancing authority areas de-duplication of authority patterns or behavior of the algorithm dataset and the specification structural of the data of the seasons (winter, summer, spring, fall,) and potential price shifting or to the extreme weather storm of inclement weather circumstance. That is aggregation of indices of the service or product type or SAIDI. During the year the study was conducted from December 2016 to November 2017. Familiarities: Neural Network of the Northwest region and balancing authority areas and the December 2016 and November 2017 studies and the seasons (Winter, Spring, Fall, Summer) Similarities in the reliable reports, price and rates. Natural Language Processing (NLP): All balancing authority of the Northwest regions and within a stipulated time from of the case study of December 2016 to November 2017 text of data via text or a tokenization of writing dataset commands (ex. Northwest, Southwest, East, West included in the study of the citation issued in 2017 and delegated in greater than or less than citation years of 2020). Specific Parameter: December 2016 to November 2017. The need for renewable energy options or alternatives within the specific area that are solely identified by specific balancing authority (ex. Atlas). Power study of the Northwest Region Power market, with the parameters of the Seasons (Summer, Winter, Fall, Spring) and balancing authority areas peak season or inclement weather circumstantial and tracking the restoral in the in event that inclement weather or circumstance may occurs out of the normal as disclosed in the dataset.