Electricity Consumption Prediction Using Machine Learning
The use of electricity has a significant impact on the environment, energy distribution costs, and energy management since it directly impacts these costs. Long-standing techniques have inherent limits in terms of accuracy and scalability when it comes to predicting power usage. It is now feasible t...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/28/e3sconf_icmed-icmpc2023_01048.pdf |
| _version_ | 1797807828199538688 |
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| author | Reddy G. Vijendar Aitha Lakshmi Jaswitha Poojitha Ch. Shreya A. Naga Reddy D. Krithika Meghana G. Sai. |
| author_facet | Reddy G. Vijendar Aitha Lakshmi Jaswitha Poojitha Ch. Shreya A. Naga Reddy D. Krithika Meghana G. Sai. |
| author_sort | Reddy G. Vijendar |
| collection | DOAJ |
| description | The use of electricity has a significant impact on the environment, energy distribution costs, and energy management since it directly impacts these costs. Long-standing techniques have inherent limits in terms of accuracy and scalability when it comes to predicting power usage. It is now feasible to properly anticipate power use using previous data thanks to improvements in machine learning techniques. In this paper, we provide a machine learning-based method for forecasting power use. In this study, we investigate a number of machine learning techniques, including linear regression, K Nearest Neighbours, XGBOOST, random forest, and artificial neural networks(ANN), to forecast power usage. Using historical electricity use data received from a power utility business, we trained and assessed these models. The data is a year’s worth of hourly power use that has been pre-processed to address outliers and missing numbers. Various assessment measures, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2), were used to assess the performance of the models [19]. The outcomes demonstrate that the suggested method may accurately forecast power use. The K Nearest Neighbours(KNN) model outperformed all others in terms of performance, with a 90.92% accuracy rate for predicting agricultural production |
| first_indexed | 2024-03-13T06:28:30Z |
| format | Article |
| id | doaj.art-0af0c30725814450b90ca6632aed3c8c |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-13T06:28:30Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-0af0c30725814450b90ca6632aed3c8c2023-06-09T09:11:30ZengEDP SciencesE3S Web of Conferences2267-12422023-01-013910104810.1051/e3sconf/202339101048e3sconf_icmed-icmpc2023_01048Electricity Consumption Prediction Using Machine LearningReddy G. Vijendar0Aitha Lakshmi Jaswitha1Poojitha Ch.2Shreya A. Naga3Reddy D. Krithika4Meghana G. Sai.5Department of Information Technology, GRIETDepartment of Information Technology, GRIETDepartment of Information Technology, GRIETDepartment of Information Technology, GRIETDepartment of Information Technology, GRIETDepartment of Information Technology, GRIETThe use of electricity has a significant impact on the environment, energy distribution costs, and energy management since it directly impacts these costs. Long-standing techniques have inherent limits in terms of accuracy and scalability when it comes to predicting power usage. It is now feasible to properly anticipate power use using previous data thanks to improvements in machine learning techniques. In this paper, we provide a machine learning-based method for forecasting power use. In this study, we investigate a number of machine learning techniques, including linear regression, K Nearest Neighbours, XGBOOST, random forest, and artificial neural networks(ANN), to forecast power usage. Using historical electricity use data received from a power utility business, we trained and assessed these models. The data is a year’s worth of hourly power use that has been pre-processed to address outliers and missing numbers. Various assessment measures, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2), were used to assess the performance of the models [19]. The outcomes demonstrate that the suggested method may accurately forecast power use. The K Nearest Neighbours(KNN) model outperformed all others in terms of performance, with a 90.92% accuracy rate for predicting agricultural productionhttps://www.e3s-conferences.org/articles/e3sconf/pdf/2023/28/e3sconf_icmed-icmpc2023_01048.pdfknnannrandom forestxgboost regressor |
| spellingShingle | Reddy G. Vijendar Aitha Lakshmi Jaswitha Poojitha Ch. Shreya A. Naga Reddy D. Krithika Meghana G. Sai. Electricity Consumption Prediction Using Machine Learning E3S Web of Conferences knn ann random forest xgboost regressor |
| title | Electricity Consumption Prediction Using Machine Learning |
| title_full | Electricity Consumption Prediction Using Machine Learning |
| title_fullStr | Electricity Consumption Prediction Using Machine Learning |
| title_full_unstemmed | Electricity Consumption Prediction Using Machine Learning |
| title_short | Electricity Consumption Prediction Using Machine Learning |
| title_sort | electricity consumption prediction using machine learning |
| topic | knn ann random forest xgboost regressor |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/28/e3sconf_icmed-icmpc2023_01048.pdf |
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