Prediction of Electric Power Production and Consumption for the CETATEA Building Using Neural Networks
Economic and social development is hardly influenced by electric power production and consumption. In this context of the energy supply pressure, energy production and consumption must be monitored and controlled in an intelligent way. Due to the availability of large data measurements, prediction a...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/16/6259 |
| _version_ | 1797442032588816384 |
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| author | Flaviu Turcu Andrei Lazar Vasile Rednic Gabriel Rosca Ciprian Zamfirescu Emanuel Puschita |
| author_facet | Flaviu Turcu Andrei Lazar Vasile Rednic Gabriel Rosca Ciprian Zamfirescu Emanuel Puschita |
| author_sort | Flaviu Turcu |
| collection | DOAJ |
| description | Economic and social development is hardly influenced by electric power production and consumption. In this context of the energy supply pressure, energy production and consumption must be monitored and controlled in an intelligent way. Due to the availability of large data measurements, prediction algorithms based on neural networks are widely used in accurate power prediction. Firstly, the particularity of our work is represented by the size of the dataset consisting of 4 years of continuous real-time data measurements collected from the CETATEA photovoltaic power plant, a research site for renewable energies located in Cluj-Napoca, Romania. Secondly, the high granularity of the dataset with more than 4.2 million unified production and consumption power values recorded every 30 s guarantees the overall prediction accuracy of the system. Performance metrics used to evaluate the prediction accuracy are the mean bias error, the mean square error, the convergence time of the prediction system, the test performance, and the train mean performance. Test results indicate that the predicted unified electric power production and consumption closely resembles the unified electric power measured values. |
| first_indexed | 2024-03-09T12:35:35Z |
| format | Article |
| id | doaj.art-19c89fcd02924022a7ca7058f34bb3f2 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T12:35:35Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-19c89fcd02924022a7ca7058f34bb3f22023-11-30T22:24:16ZengMDPI AGSensors1424-82202022-08-012216625910.3390/s22166259Prediction of Electric Power Production and Consumption for the CETATEA Building Using Neural NetworksFlaviu Turcu0Andrei Lazar1Vasile Rednic2Gabriel Rosca3Ciprian Zamfirescu4Emanuel Puschita5National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, RomaniaCommunications Department, Technical University of Cluj-Napoca, 26-28 George Baritiu Street, 400027 Cluj-Napoca, RomaniaNational Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, RomaniaNational Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, RomaniaDepartment of Telecommunications, Politehnica University of Bucharest, 1-3, Iuliu Maniu Ave., 061071 Bucharest, RomaniaNational Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, RomaniaEconomic and social development is hardly influenced by electric power production and consumption. In this context of the energy supply pressure, energy production and consumption must be monitored and controlled in an intelligent way. Due to the availability of large data measurements, prediction algorithms based on neural networks are widely used in accurate power prediction. Firstly, the particularity of our work is represented by the size of the dataset consisting of 4 years of continuous real-time data measurements collected from the CETATEA photovoltaic power plant, a research site for renewable energies located in Cluj-Napoca, Romania. Secondly, the high granularity of the dataset with more than 4.2 million unified production and consumption power values recorded every 30 s guarantees the overall prediction accuracy of the system. Performance metrics used to evaluate the prediction accuracy are the mean bias error, the mean square error, the convergence time of the prediction system, the test performance, and the train mean performance. Test results indicate that the predicted unified electric power production and consumption closely resembles the unified electric power measured values.https://www.mdpi.com/1424-8220/22/16/6259power predictionneural networksphotovoltaic panelsreal datasetunified power production and consumption |
| spellingShingle | Flaviu Turcu Andrei Lazar Vasile Rednic Gabriel Rosca Ciprian Zamfirescu Emanuel Puschita Prediction of Electric Power Production and Consumption for the CETATEA Building Using Neural Networks Sensors power prediction neural networks photovoltaic panels real dataset unified power production and consumption |
| title | Prediction of Electric Power Production and Consumption for the CETATEA Building Using Neural Networks |
| title_full | Prediction of Electric Power Production and Consumption for the CETATEA Building Using Neural Networks |
| title_fullStr | Prediction of Electric Power Production and Consumption for the CETATEA Building Using Neural Networks |
| title_full_unstemmed | Prediction of Electric Power Production and Consumption for the CETATEA Building Using Neural Networks |
| title_short | Prediction of Electric Power Production and Consumption for the CETATEA Building Using Neural Networks |
| title_sort | prediction of electric power production and consumption for the cetatea building using neural networks |
| topic | power prediction neural networks photovoltaic panels real dataset unified power production and consumption |
| url | https://www.mdpi.com/1424-8220/22/16/6259 |
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