Submit your paper : editorIJETjournal@gmail.com Paper Title : Predicting Energy Usage Of Electrical Appliances Using Machine Learning ISSN : 2395-1303 Year of Publication : 2022 10.5281/zenodo.7263459 MLA Style: -Cigiri Rashmi, Chinthalapally Sree Harika, Chennoju Chandana, Chava Nuthana Predicting Energy Usage Of Electrical Appliances Using Machine Learning , Volume 8 - Issue 5 September - October 2022 International Journal of Engineering and Techniques (IJET) ,ISSN:2395-1303 , www.ijetjournal.org APA Style: -Cigiri Rashmi, Chinthalapally Sree Harika, Chennoju Chandana, Chava Nuthana Predicting Energy Usage Of Electrical Appliances Using Machine Learning , Volume 8 - Issue 5 September - October 2022 International Journal of Engineering and Techniques (IJET) ,ISSN:2395-1303 , www.ijetjournal.org Abstract The correct analysis of energy consumption by home appliances for future energy management in residential buildings may be a difficult downside because of its high impact on the human close surroundings. during this project, a prediction methodology is given for energy consumption of home appliances in residential buildings. The aim of the project is that the daily power consumption prediction of home appliances supported classification in step with the hourly consumed power of all home appliances getting used in residential buildings. the method consists of 5 stages: knowledge supply, knowledge assortment, feature extraction, prediction, and performance analysis. completely different machine learning algorithms are applied to knowledge containing historical hourly energy consumption of home appliances employed in residential buildings. we've divided knowledge into different coaching and testing ratios and have applied different quantitative and qualitative measures for locating the prediction capability and potency of every formula. Reference 1. Singh S, Roy A, Selvan M. sensible load node for non sensible load beneath sensible grid paradigm: a replacement home energy management system. IEEE Consum negatron magazine 2019;8(2):2227. 2. Haghi A, Toole O. the utilization of sensible meter knowledge to forecast electricity demand. CS229 course paper, 2013. 3. Haider HT, See OH, Elmenreich W. A review of residential demand response of sensible grid. Renew Sustain Energy Rev 2016;59:166–78. 4. K. Zhou, Yang S, Shao Z. social unit monthly electricity consumption pattern mining: A fuzzy clustering-based model and a case study.J Cleaner Prod 2017;141:900–8. 5. Jia Y, Lyu X, Xie P, Xu Z, Chen M. a completely unique retrospect-inspired regime for microgrid real- time energy programing with heterogeneous sources. IEEE Trans sensible Grid 2020. http://dx.doi.org/10.1109/TSG.2020.2999383. 6. Lu H, Li BM, Wei H. A small-world of neural purposeful network from multi conductor recordings throughout a memory task. In: The 2012 international joint conference on neural networks. IEEE; 2012, p. 1–6. 7. H.-x. Zhao, F. Magoules, A review on the prediction of building energy consumption, Renew. Sustain. Energy Rev. sixteen (6) (2012) 3586– 3592. 8. M.G. Fikru, L. Gautier, The impact of weather variation on energy consumption in residential homes, Appl. Energy a hundred and forty four (2015) 19– thirty. Keywords — Predicting Energy Usage Of Electrical Appliances Using Machine Learning |