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Asmita Marathe

Bio: Asmita Marathe is an academic researcher. The author has contributed to research in topics: Computer science & Irradiance. The author has an hindex of 1, co-authored 2 publications receiving 1 citations.

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Journal ArticleDOI
TL;DR: A deep neural network (DNN) for forecasting the intra-day solar irradiance, photovoltaic PV plants, regardless of whether or not they have energy storage, can benefit from the work being done here.
Abstract: In this paper, we introduce a deep neural network (DNN) for forecasting the intra-day solar irradiance, photovoltaic PV plants, regardless of whether or not they have energy storage, can benefit from the work being done here. The proposed DNN utilises a number of different methodologies, two of which are cloud motion analysis and machine learning, in order to make forecasts regarding the climatological conditions of the future. In addition to this, the accuracy of the model was evaluated in light of the data sources that were easily accessible. In general, four different cases have been investigated. According to the findings, the DNN is capable of making more accurate and reliable predictions of the incoming solar irradiance than the persistent algorithm. This is the case across the board. Even without any actual data, the proposed model is considered to be state-of-the-art because it outperforms the current NWP forecasts for the same time horizon as those forecasts. When making predictions for the short term, using actual data to reduce the margin of error can be helpful. When making predictions for the long term, however, weather information can be beneficial.

1 citations

Proceedings ArticleDOI
22 Feb 2023
TL;DR: In this article , a two-directional dc-dc converter was used to charge a light bulb as the load to evaluate the effectiveness of the directional power converter developed as part of the project as well as the functionality of energy control technologies.
Abstract: Our main focus was on creating a Two directional power converter for a standalone photo-voltaic energy-generating technology as well as technology energy control when a lead-acid energy storage device is used to control the energy supply. We utilized a light bulb as the load to evaluate the effectiveness of the directional power converter developed as part of our project as well as the functionality of energy control technologies. The suggested technology was composed of an energy control technology, a lead-acid battery, a bulb, a two-directional buck-boost converter, an utmost power point tracking controller, and these components. An energy control technique was developed to grow the rate at which the Photo-voltaic power producing technology consumed energy. The circuits are intended to charge the battery between upper and lower voltage limits, as well as to continuously check the battery's state of charge and add or release current as necessary. The bidirectional buck-boost converter's ability to function as a charge controller on its own, along with the use of particular BUCK and BOOST converter properties to optimize the home application, is the primary distinction between the method used in the proposed technology and other techniques used in the past. The battery is charged and discharged using a bidirectional dc-dc converter. Measurement data are employed to support the viability of a photovoltaic air conditioning technology.

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TL;DR: In this paper , an investigation of the identification, examination and autonomous methods of diabetes mellitus from six completely various sides viz. datasets of Diabetes Mellitus, preprocessing procedures, attribute extraction, machine learning based analysis, classifying and prediction of Diabetes mellitus, and evaluating the results is presented.
Abstract: Diabetes Mellitus is considered to be a state evoked by unmonitored polygenic disorder which will cause various organs collapse in sufferers. An investigation of the identification, examination and autonomous methods of Diabetes Mellitus from six completely various sides viz. datasets of Diabetes Mellitus, preprocessing procedures, attribute extraction, machine learning based analysis, classifying and prediction of Diabetes Mellitus, and evaluating the results. Machine Learning Associate in Nursing computer science is advancing, which permits the first prediction and diagnosing the Diabetes Mellitus over an automatic method that is superior than a nonautomatic detection. There are various reports which are revealed on automated Diabetes Mellitus prediction, identification, examination and autonomous procedure through machine learning and artificial intelligence procedures and also three current analysis problems within the department of Diabetes Mellitus prediction are recorded. In this it provides the Diabetes Mellitus prediction procedures demonstrate importance to the research community utilized within a range of automated Diabetes Mellitus prediction and self supervision.