Autors: Stošovic M. A., Radivojevic N., Ivanova, M. S.
Title: Electricity Consumption Prediction in an Electronic System Using Artificial Neural Networks
Keywords: artificial neural networks; gated recurrent unit; energy con

Abstract: In this paper, a method for electricity consumption prediction based on artificial neural networks is proposed. The data obtained are measured for a period of over 2 years and then separated to four seasons, so different models are developed for each season. Five different network structures (ordinary RNN, LSTM, GRU, bidirectional LSTM, bidirectional GRU) for five different values of horizon, i.e., input data (one day, two days, four days, one week, two weeks) are examined. Performance indices, such as mean absolute percentage error (MAPE), root mean square error (RMSE), mean absolute error (MAE) and mean square error (MSE), are used in order to obtain qualitative and quantitative comparisons among the obtained models. The results show that the modifications of recurrent neural networks perform much better than ordinary recurrent neural networks. GRU and LSTMB structures with horizons of 168h and 336h are found to have the best performances.

References

    Issue

    MDPI Electronics, vol. 11, issue 21, 2022, Switzerland, Multidisciplinary Digital Publishing Institute (MDPI), EISSN 2079-9292/https://doi.org/10.3390/electronics11213506

    Copyright MDPI

    Цитирания (Citation/s):
    1. Starace G, Tiwari A, Colangelo G, Massaro A. Advanced Data Systems for Energy Consumption Optimization and Air Quality Control in Smart Public Buildings Using a Versatile Open Source Approach. Electronics. 2022; 11(23):3904. https://doi.org/10.3390/electronics11233904 - 2022 - в издания, индексирани в Scopus или Web of Science
    2. Chapagain K, Gurung S, Kulthanavit P, Kittipiyakul S. Short-Term Electricity Demand Forecasting Using Deep Neural Networks: An Analysis for Thai Data. Applied System Innovation. 2023; 6(6):100. https://doi.org/10.3390/asi6060100 - 2023 - в издания, индексирани в Scopus или Web of Science
    3. KUMAR, SANJEEV and Singhal, Prateek Kumar and Singh, Nishant Kumar and Katta, Vijay Subhash, Electricity Consumption Prediction Using Neural Network-Based Approaches: A Case Study of Hindustan College of Science and Technology, Mathura Campus. Available at SSRN: https://ssrn.com/abstract=4634918 or http://dx.doi.org/10.2139/ssrn.4634918 - 2023 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    4. R. K. Shiwakoti, C. Charoenlarpnopparut, and K. Chapagain, A Deep Learning Approach for Short-Term Electricity Demand Forecasting: Analysis of Thailand Data, Applied Sciences 14(10): 3971, 2024. https://doi.org/10.3390/app14103971. - 2024 - в издания, индексирани в Scopus или Web of Science
    5. Zec, L., Mikulović, J., Žarković, M., Application of artificial neural network to power consumption forecasting for the Sarajevo region. Electrical Engineering, 2024. https://doi.org/10.1007/s00202-024-02696-y. - 2024 - в издания, индексирани в Scopus или Web of Science
    6. Arifin, B., Nugroho, A. A., Performance Enhancement of Electric Power Steering: A Review, 2024 11th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), Yogyakarta, Indonesia, 631-637, 2024. doi: 10.1109/EECSI63442.2024.10776048. - 2024 - в издания, индексирани в Scopus или Web of Science
    7. Briatore, F., Russo, M. L., Literature Review on AI for Energy Forecasting, 2024 4th International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Male, Maldives, 1-6, 2024. doi: 10.1109/ICECCME62383.2024.10796050. - 2024 - в издания, индексирани в Scopus или Web of Science
    8. Braggio, M., Russo, M. L., A Bibliometric Analysis on Artificial Intelligence in the Energy Forecasting, 2024 4th International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Male, Maldives, 1-6, 2024. doi: 10.1109/ICECCME62383.2024.10796773. - 2024 - в издания, индексирани в Scopus или Web of Science
    9. Mosca, M., Mosca, R., Russo, M. L., Optimizing Industrial Energy Management: Advanced Simulators Enhanced With Machine Learning For Improved Forecast Accuracy, 2024 4th International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Male, Maldives, 1-6, 2024. doi: 10.1109/ICECCME62383.2024.10796531. - 2024 - в издания, индексирани в Scopus или Web of Science

    Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus