[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125597-en":3,"doc-seo-125597-105":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":4,"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},125597,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Predicting Power Consumption of Individual Household using Machine Learning Algorithms - paper","Climate change and rising electricity use are tightly connected, since household demand increases with more connected devices. Many users lack awareness of their real consumption, leading to unexpected bills and adding pressure on power systems, especially after lockdown. This work proposes a machine-learning time-series model that learns from historical user data and usage patterns, then forecasts the next month’s consumption and bill to support earlier planning and cost reduction.","Predicting Power Consumption of Individual Household using Machine Learning Algorithms  \nSobhana M1, Smitha Chowdary Ch2, D.N.V.S.L.S. Indira3, Gaddameedi Dinesh Kumar4  \n1Senior Assistant Professor  \nDepartment of Computer Science and Engineering  \nV R Siddhartha Engineering College  \nVijayawada, Andhra Pradesh, India  \n[sobhana@vrsiddhartha.ac.in](sobhana@vrsiddhartha.ac.in)  \n2Professor  \nDepartment of Computer Science and Engineering,  \nKoneru Lakshmaiah Education Foundation  \nVaddeswaram, Guntur 522302, A.P., India  \n[smithacsc@gmail.com](smithacsc@gmail.com)  \n3Associate Professor  \nDepartment of Information Technology,  \nSeshadri RaoGudlavalleru Engineering College  \nGudlavalleru, Andhra Pradesh, India.  \n[indiragamini@gmail.com](indiragamini@gmail.com)  \n4Department of Computer Science and Engineering,  \nKoneru Lakshmaiah Education Foundation  \nVaddeswaram, Guntur 522302, A.P., India  \n[dineshzee@gmail.com](dineshzee@gmail.com)  \nAbstract—Climate change, as known, is the dangerous environmental effect we are going to face in the near future and electricity contributes the majority of its part in overcoming climate change as per the trends. Usage of electricity is widely increasing all over the world mainly as an alternative to the use of fossil fuels. In households the usage is rapidly increasing day by day, owing to the increase in the number of devices running on electricity. As we have observed mainly after the relaxation of the lockdown the bills received by households, especially in cities were unhappy and have left most of the people aghast. It is evident that users have no idea about the power they consume. In this work, a model to forecast the electricity bill of household users based on the previous trends and usage patterns by making use of machine learning techniques has been proposed. The historical data of the user is studied and the learning is done iteratively to improve the accuracy of the model. The model can then be used to forecast the consumption beforehand.  \nKeywords-—Power consumption, Time series forecasting, Bill forecasting, ARIMA, Seasonality.  \nI. INTRODUCTION  \nAs human beings evolved through early ages, they have invented something or the other to make lives easier day by day. One such invention that first came into the limelight is the concept of ionization (light) approximately 13 billion years ago. Over time, the needs of humans have increased and increased which led to the invention of electricity for various kinds of purposes in daily life, in such a way that in the present situation there is no mankind if there is no electricity [1] . The household consumer is not aware of the usage of electricity that in turn affects him economically and the climate [2] . Especially households have been our area of interest because the disruptions caused in the power (outages, swells, sags, and unbalanced) due to the non-uniform usage pattern of residential users are huge [3] . Especially after the  \nrelaxation of lockdown in a country such as India where the population is high, every household was worried mostly about a common situation i.e. electricity bills. Our idea is to help people have a long-term analysis on the usage of electricity to help cut down unnecessary costs in bills. So, a time-series model has been implemented to accurately predict the future month’s bill. Initially, each module makes use of the historical data of the consumer, then it constructs a prediction system to forecast the future value. The predicted value is displayed to the user giving them an idea what will be the usage of their electricity in the next month. Electricity is also non-storable, whatever is produced needs to be consumed immediately [4] . Proper production planning is required for this purpose. Forecasting methods are being employed by various agencies to identify the demand and to produce only what is required  \n[5] . Every industry strives towards leaving a low carbon footprint and conventional power con","cbCaiaKnDsOBY1LH","https://ap.wps.com/l/cbCaiaKnDsOBY1LH","pdf",268556,1,6,"English","en",105,"# Introduction\n## Literature Survey\n## Proposed System\n## Methodology\n## Results and Analysis\n## Performance Evaluation\n## Conclusion","[{\"question\":\"Why is predicting household electricity power consumption important?\",\"answer\":\"Households often lack visibility into their actual consumption, which can cause unfavorable electricity bills and contribute to wider environmental and power-supply issues due to rising demand and non-uniform usage patterns.\"},{\"question\":\"How does the proposed forecasting model work?\",\"answer\":\"The approach studies each user’s historical data, iteratively trains a prediction system using machine learning time-series learning, and then outputs the forecast for future monthly consumption and bill.\"},{\"question\":\"Why is ARIMA used in the methodology?\",\"answer\":\"ARIMA is described as a widely used time-series forecasting technique that identifies patterns from previous data, enabling accurate predictions on test data through training and subsequent forecasting.\"}]","Predicting Power Consumption of Individual Household using Machine Learning Algorithms - 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