[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117289-en":3,"doc-seo-117289-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117289,687207024478,"Liam","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",6,"Technology","SMART GRID USING MACHINE LEARNING - Journal of Science and Technology","Energy waste and inefficient distribution drive the need for systems that can adapt generation and delivery in real time. This paper presents a smart grid approach enhanced with machine learning to address these challenges. Solar data is used for energy generation prediction, supporting production and utility companies in estimating generation and planning distribution. The proposed web application visualizes energy generation and user logs, aiming to reduce blackout risk and minimize energy wastage.","Journal of Science and Technology  \nISSN: 2456-5660 Volume 8, Issue 07 (July-2023)  \n [www.jst.org.in DOI:](www.jst.org.in DOI:)[https://doi.org/10.46243/jst.2023.v8.i07.pp120-123](https://doi.org/10.46243/jst.2023.v8.i07.pp120-123)   \nSMART GRID USING MACHINE LEARNING  \n1.P. SREELATHA,2. PATNAMSHETTY PRAGNYA,3. KADIYAM SAI SWETHA,4. ESLAVATH PRIYANKA,5. K. MAHA SRI LAKSHMI  \n1.ASSISTANT PROFESSOR, DEPARTMENT OF EEE, MALLAREDDY ENGINEERING COLLEGE FOR WOMEN,  \nHYDERABAD  \n2,3,4&5. UGSCHOLAR, DEPARTMENT OF EEE, MALLAREDDY ENGINEERING COLLEGE FOR WOMEN, HYDERABAD  \nTo Cite this Article  \n.P. SREELATHA, . PATNAMSHETTY PRAGNYA. KADIYAM SAI SWETHA, ESLAVATH PRIYANKA, K. MAHA SRI LAKSHMI,“SMART GRID USING MACHINE LEARNING” Journal of Science and Technology, Vol. 08, Issue 07,-July 2023, pp120-123  \nArticle Info  \nReceived: 02-06-2023 Revised: 05-07-2023 Accepted: 15-07-2023 Published: 25-07-2023  \nABSTRACT-In today‟s world, wastage of energy has become more frequent which leads to build a system which will take another way to maintain power generation and make proper distribution of energy and that system is called as SMART GRID.In this paper we will be making a Smart Grid which will overcome these issues by using Machine Learning. We will be using solar data power generation prediction by machine learning. This paper will help production or utility company to get knowledge of how much they have generate and distribute. By his paper blackout and energy wastage can be reduced.we developed the application which shows energy generation and user log.  \nINDEXED TERMS: Smart Grid, Blackout, Machine Learning, analysis; visualization.  \nI. INTRODUCTION:  \nThe Electric Grid which is used for Delivering an Electricity through the largest interconnected network build by Humans. The Transition from Actual Electric Grid towards Sustainable, Efficient and Flexible Electricity Network requires Complex Methods. Moreover Urbanization indicates that the total Necessity of an Energy will Increase in Future while atthe same time Penetration of Renewable Energy Resource will also Increase.Smart Grid are becoming popular every coming day as they are fast, reliable and efficient. Their popularity is among users as well as in utility companies. The Smart Grid represented as an electric system which uses information, two-way communication technologies, and computational intelligence which is integrated across electricity generation, transmission, substations, distribution and consumption then achieved a system which is clean, safe, secure, reliable, resilient, efficient, and sustainable [1].The Smart Grid represent an Opportunity to transform the Energy industry into an Era of Reliability, Scalability and Efficiency that will Increase our Economic Wealth and Environmental Health.Smart Grid helps in Conserving an Energy Reducing cost , Increasing Reliability and Transparency which makes it more Efficient. As can be figured, that huge quantity of humans demands an equal quantity of energy to fulfil there usual duties for their daily life. Therefore, electricity demand is in a way to grow at the same rate as number of human beings are growing on Earth, and since the Earth has limited, resources, it becomes of major importance to rationalize the use of energy, and also trying to use renewable energies that will provide electricity which can be consumed with lower impact on the planet [7] . To make Smart grid more Efficient we are using Machine Learning.  \nPublished by: Longman Publishers  [www.jst.org.in](www.jst.org.in)  \nPage | 120  \nThe proposed system provides a Web Application which provides an interface to visualize the relationship between the temperature, irradiance and the energy generation prediction for Present as well as Future Using Weather Data like Temperature for utility companies and also gives the overall list of data of users.  \nLITERATURE REVIEW Model which will balance conflicting requirements for high prediction accuracy, low computing tim","cbCaiod1Bbb3Qee9","https://ap.wps.com/l/cbCaiod1Bbb3Qee9","pdf",732441,1,4,"English","en",105,"# Introduction\n# Literature Review","[{\"question\":\"What problem does the smart grid approach address in the paper?\",\"answer\":\"It targets frequent energy wastage and the resulting difficulties in maintaining proper power generation and distribution, aiming to reduce blackouts and improve efficiency.\"},{\"question\":\"How does the paper apply machine learning in the smart grid?\",\"answer\":\"It uses machine learning with solar data to predict power generation, enabling utilities to estimate how much energy will be generated and better manage distribution.\"},{\"question\":\"What outputs does the proposed system provide?\",\"answer\":\"It develops a web application that visualizes the relationship among weather variables (such as temperature and irradiance) and predicted energy generation, and also shows overall user data logs.\"}]","SMART GRID USING MACHINE LEARNING - 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