Autors: Ekonomou, L., Christodoulou, C.A., Mladenov, V. M.
Title: A short-term load forecasting method using artificial neural networks and wavelet analysis
Keywords: Artificial neural networks; Back-propagation algorithm; Deno

Abstract: Load forecasting is an issue of great importance for the reliable operation of the electric power system grids. Various forecasting methodologies have been proposed in the international research bibliography, following different models and mathematical approaches. A precise electric load forecasting results in cost saving and secure operational conditions. Moreover, it can also be helpful in power supply strategy, market research and financing planning. In the current work a methodology based on artificial neural networks methods reinforced by an appropriate wavelet denoising algorithm is implemented, in order to obtain shortterm load forecasting. Real recoded data obtained from the Bulgarian power system grid was used in the analysis. The extracted outcomes indicate the effectiveness of the proposed method, reducing the relative error between real and theoretical data.

References

    Issue

    International Journal of Power Systems, issue 1, pp. 64-68, 2016, France, IARAS, ISBN ISSN: 2367-8976

    Цитирания (Citation/s):
    1. Stratigakos, A., Bachoumis, A., Vita, V. and Zafiropoulos, E., 2021. Short-Term Net Load Forecasting with Singular Spectrum Analysis and LSTM Neural Networks. Energies, 14(14), p.4107. - 2021 - в издания, индексирани в Scopus или Web of Science
    2. Farsi, B., Amayri, M., Bouguila, N. and Eicker, U., 2021. On short-term load forecasting using machine learning techniques and a novel parallel deep LSTM-CNN approach. IEEE Access, 9, pp.31191-31212. - 2021 - в издания, индексирани в Scopus или Web of Science
    3. Memarzadeh, G. and Keynia, F., 2021. Short-term electricity load and price forecasting by a new optimal LSTM-NN based prediction algorithm. Electric Power Systems Research, 192, p.106995. - 2021 - в издания, индексирани в Scopus или Web of Science
    4. Pallonetto, F., Jin, C. and Mangina, E., 2022. Forecast electricity demand in commercial building with machine learning models to enable demand response programs. Energy and AI, 7, p.100121. - 2021 - в издания, индексирани в Scopus или Web of Science
    5. Rafi, Shafiul Hasan, Shohana Rahman Deeba, and Eklas Hossain. "A Short-Term Load Forecasting Method Using Integrated CNN and LSTM Network." IEEE Access 9 (2021): 32436-32448. - 2021 - в издания, индексирани в Scopus или Web of Science
    6. Lopez-Martin, M., Sanchez-Esguevillas, A., Hernandez-Callejo, L., Arribas, J.I. and Carro, B., 2021. Additive Ensemble Neural Network with Constrained Weighted Quantile Loss for Probabilistic Electric-Load Forecasting. Sensors, 21(9), p.2979. - 2021 - в издания, индексирани в Scopus или Web of Science
    7. Sharma, S., Agrawal, R.K. and Tripathi, M.M., 2020, March. Synergism of Recurrent Neural Network and Fuzzy Logic for Short Term Energy Load Forecasting. In 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) (pp. 165-169). IEEE. - 2020 - в издания, индексирани в Scopus или Web of Science
    8. Xie, G., Chen, X. and Weng, Y., 2021. Enhance load forecastability: Optimize data sampling policy by reinforcing user behaviors. European Journal of Operational Research. - 2021 - в издания, индексирани в Scopus или Web of Science
    9. Chen, Z., Zhang, D., Jiang, H., Wang, L., Chen, Y., Xiao, Y., Liu, J., Zhang, Y. and Li, M., 2021. Load Forecasting Based on LSTM Neural Network and Applicable to Loads of “Replacement of Coal with Electricity”. Journal of Electrical Engineering & Technology, pp.1-10. - 2021 - в издания, индексирани в Scopus или Web of Science
    10. Xu, F., Xu, W., Qiu, Y., Wu, M., Wang, R., Li, Y., Fan, P. and Yang, J., 2021, November. A Short-term Load Forecasting Model Based on Neural Network Considering Weather Features. In 2021 IEEE 4th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) (pp. 24-27). IEEE. - 2021 - в издания, индексирани в Scopus или Web of Science
    11. Chopra, A.R. and Nair, N.K., 2021, April. Wavelet-Extreme Learning i-Machine for New Zealand Smart Meter Data. In 2021 IEEE Power and Energy Conference at Illinois (PECI) (pp. 1-8). IEEE. - 2021 - в издания, индексирани в Scopus или Web of Science
    12. Arvanitidis, A.I., Bargiotas, D., Daskalopulu, A., Laitsos, V.M. and Tsoukalas, L.H., 2021. Enhanced Short-Term Load Forecasting Using Artificial Neural Networks. Energies, 14(22), p.7788. - 2021 - в издания, индексирани в Scopus или Web of Science
    13. Hirose, K., Wada, K., Hori, M. and Taniguchi, R.I., 2020. Event Effects Estimation on Electricity Demand Forecasting. Energies, 13(21), p.5839. - 2020 - в издания, индексирани в Scopus или Web of Science
    14. 諏訪達郎, 2021. 日本における海洋空間の利用調整に関する研究. - 2021 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    15. Αρβανιτίδης, Α.Ι.Χ., 2021. Βραχυπρόθεσμη πρόβλεψη φορτίου με τη χρήση τεχνητών νευρωνικών δικτύων (Master's thesis). - 2021 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    16. Zou, Y., Feng, W., Zhang, J. and Li, J., 2022. Forecasting of Short-Term Load Using the MFF-SAM-GCN Model. Energies, vol. 15, issue (9), p.3140. (Google Scholar, Web of Science, IF 3.333) - 2022 - в издания, индексирани в Scopus или Web of Science
    17. Hou, H., Liu, C., Wang, Q., Wu, X., Tang, J., Shi, Y. and Xie, C., 2022. Review of load forecasting based on artificial intelligence methodologies, models, and challenges. Electric Power Systems Research, 210, p.108067, https://doi.org/10.1016/j.epsr.2022.108067 (IF 3.818, Web of Science) - 2022 - в издания, индексирани в Scopus или Web of Science
    18. Pallonetto, F., Jin, C. and Mangina, E., 2022. Forecast electricity demand in commercial building with machine learning models to enable demand response programs. Energy and AI, 7, p.100121, DOI 10.1016/j.egyai.2021.100121 (Scopus, SJR 2.536) - 2022 - в издания, индексирани в Scopus или Web of Science
    19. Bagheri, M., Zadehbagheri, M., Kiani, M.J., Zamani, I. and Nejatian, S., 2022. Using Hybrid Wavelet Approach and Neural Network Algorithm to Forecast Distribution Feeders. Journal of Electrical Engineering & Technology, pp.1-14. https://doi.org/10.1007/s42835-022-01296-9 (Web of Science, Google Scholar) IF 0.998 - 2022 - в издания, индексирани в Scopus или Web of Science
    20. Yin, C. and Mao, S., 2023. Fractional multivariate grey Bernoulli model combined with improved grey wolf algorithm: Application in short-term power load forecasting. Energy, p.126844., ISSN 03605442, DOI 10.1016/j.energy.2023.126844 (Scopus, Google Scholar) SJR 2.041 - 2023 - в издания, индексирани в Scopus или Web of Science
    21. Li, D., Tan, Y., Zhang, Y., Miao, S. and He, S., 2023. Probabilistic forecasting method for mid-term hourly load time series based on an improved temporal fusion transformer model. International Journal of Electrical Power & Energy Systems, 146, p.108743., ISSN 01420615, DOI 10.1016/j.ijepes.2022.108743 (Web of Science, Scopus, Google Scholar) SJR 1.544, IF 5.416 - 2023 - в издания, индексирани в Scopus или Web of Science
    22. Szabó, D., Göcsei, G., Németh, B., Lovrenčić, V., Gubeljak, N., Kovač, M. and Krisper, U., 2023. DLR related model development and performance analysis in the framework of FLEXITRANSTORE. Energy Reports, 9, pp.452-459., ISSN 23524847, DOI 10.1016/j.egyr.2022.11.010 (Web of Science, Scopus, Google Scholar) SJR 0.894, IF 5.258 - 2023 - в издания, индексирани в Scopus или Web of Science
    23. Yang, H., Zhang, X., Chu, Y., Ma, Y. and Zhang, D., 2023. Multi-objective based demand response strategy optimization considering differential demand on reliability of power system. International Journal of Electrical Power & Energy Systems, vol. 152, No.109202, pp. 1-11, ISSN 01420615, DOI 10.1016/j.ijepes.2023.109202 (Web of Science, Scopus, Google Scholar) SJR 1.533, IF 5.2 - 2023 - в издания, индексирани в Scopus или Web of Science
    24. Ogbonna, C.C., Eze, V.H.U., Ikechuwu, E.S., Okafor, O., Anichebe, O.C. and Oparaku, O.U., 2023. Comprehensive Review of Artificial Neural Network Techniques Used for Smart Meter-Embedded Forecasting System. IDOSR Journal of Applied Sciences, vol. 8, issue (1), pp.13-24. (Google Scholar) - 2023 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    25. Li, L., Jing, R., Zhang, Y., Wang, L. and Zhu, L., 2023. Short-Term Power Load Forecasting Based on ICEEMDAN-GRA-SVDE-BiGRU and Error Correction Model. IEEE Access. pp. 110060 – 110074, DOI: 10.1109/ACCESS.2023.3322272, ISSN: 2169-3536 (Scopus, Google Scholar) - 2023 - в издания, индексирани в Scopus или Web of Science
    26. Shen, Q., Mo, L., Liu, G., Zhou, J., Zhang, Y. and Ren, P., 2023. Short-term load forecasting based on multi-scale ensemble deep learning neural network. IEEE Access. DOI: 10.1109/ACCESS.2023.3322167, vol. 11, pp. 111963 – 111975, ISSN: 2169-3536 (Scopus, Google Scholar) - 2023 - в издания, индексирани в Scopus или Web of Science
    27. Ullah, I., Hasanat, S.M., Aurangzeb, K., Alhussein, M., Rizwan, M. and Anwar, M.S., 2023. Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution. PeerJ Computer Science, vol. 9, p.e1487. ISSN 23765992, DOI 10.7717/peerj-cs.1487 (Web of Science, Scopus, Google Scholar) IF 4.2, SJR 0.638 - 2023 - в издания, индексирани в Scopus или Web of Science
    28. Ghoroghi, A., Petri, I., Rezgui, Y. and Alzahrani, A., 2023. A deep learning approach to predict and optimise energy in fish processing industries. ISSN 13640321, DOI 10.1016/j.rser.2023.113653, Renewable and Sustainable Energy Reviews, vol. 186, pp. 1-12, (Web of Science, Scopus, Google Scholar) IF 16.9, SJR 3.232 - 2023 - в издания, индексирани в Scopus или Web of Science
    29. Liu, F., Dong, T., Liu, Q., Liu, Y. and Li, S., 2024. Combining fuzzy clustering and improved long short-term memory neural networks for short-term load forecasting. Electric Power Systems Research, vol. 226, pp. 1-9, ISSN 03787796, DOI 10.1016/j.epsr.2023.109967 (Web of Science, Scopus, Google Scholar) IF 3.9, SJR 1.099 - 2023 - в издания, индексирани в Scopus или Web of Science
    30. Xiong, Q., Liu, M., Li, Y., Zheng, C. and Deng, S., 2023. Short-Term Load Forecasting Based on VMD and Deep TCN-Based Hybrid Model with Self-Attention Mechanism. Applied Sciences, vol. 13, issue (22), pp. 1-20, https://doi.org/10.3390/app132212479 (Web of Science, Google Scholar) IF 2.9 - 2023 - в издания, индексирани в Scopus или Web of Science
    31. Chen, Y., 2023, September. MmSN: Accurate Short-term Load Forecasting via a Multi-module Structural Network Based on Multi-feature Fusion. In 2023 IEEE 6th International Conference on Information Systems and Computer Aided Education (ICISCAE) (pp. 501-506). IEEE. doi: 10.1109/ICISCAE59047.2023.10393783 (Google Scholar) - 2023 - в издания, индексирани в Scopus или Web of Science
    32. Ogbonna CC, Eze VH, Ikechuwu ES, Okafor WO, Anichebe OC, Oparaku OU. Kampala International University, Uganda. E-mail: udoka. eze@ kiu. ac. ug. (Google Scholar) - 2023 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    33. OGUNWUYI, Ogunmakinde Jimoh; OLAIDE, Lawal Akeem; EMMANUEL, Omotayo Mayowa, (2024) “Modelling and Distribution of Electricity Load Forecasting in Nigeria Power System,” (Olu-Ode Community). International Journal of Advanced Engineering and Nano Technology (IJAENT) ISSN: 2347-6389 (Online), Volume-11 Issue-2, DOI: 10.35940/ijaent.A9769.11020224, pp. 1-9 (Google Scholar) - 2024 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    34. Lin, W., Wu, D., & Jenkin, M. (2024). “Electric Load Forecasting for Individual Households via Spatial-temporal Knowledge Distillation,” IEEE Transactions on Power Systems. ISSN 08858950, DOI 10.1109/TPWRS.2024.3393926, pp. 1-13, (Web of Science, Scopus, Google Scholar) SJR 3.827, IF 6.5 - 2024 - в издания, индексирани в Scopus или Web of Science
    35. Ahranjani, Y. K., Beiraghi, M., & Ghanizadeh, R. (2024). “Short time load forecasting for Urmia city using the novel CNN-LTSM deep learning structure,” Electrical Engineering, pp. 1-12. ISSN 09487921, DOI 10.1007/s00202-024-02361-4 (Web of Science, Scopus, Google Scholar) IF 1.7, SJR 0.431 - 2024 - в издания, индексирани в Scopus или Web of Science
    36. Ogunwuyi, O. J., Olaide, L. A., & Emmanuel, O. M. 2024 “Modelling and Distribution of Electricity Load Forecasting in Nigeria Power System,” (Olu-Ode Community). International Journal of Advanced Engineering and Nano Technology (IJAENT) ISSN: 2347-6389 (Online), DOI: 10.35940/ijaent.A9769.11020224 Volume-11 Issue-2 (Google Scholar) - 2024 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    37. Le Kim, K.N., Quoc, T.N. and Thanh, P.N., 2024, September. “Deep Learning Method For Load Forecast In Smart Solar System Based Long Short-Term Memory,” In 2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP) (pp. 1-5). IEEE. ISBN 979-833153149-2, DOI 10.1109/IDAP64064.2024.10710960 (Scopus, Google Scholar) - 2024 - в издания, индексирани в Scopus или Web of Science
    38. Yadav, M., Jamil, M., Rizwan, M. and Kapoor, R., 2023. “Application of Fuzzy‐RBF‐CNN Ensemble Model for Short‐Term Load Forecasting,” Journal of Electrical and Computer Engineering, 2023(1), p.8669796. ISSN 20900147, DOI 10.1155/2023/8669796 (Web of Science, Scopus, Google Scholar) IF 1.5, SJR 0.424 - 2024 - в издания, индексирани в Scopus или Web of Science
    39. Ogunwuyi, O. J., Olaide, L. A., & Emmanuel, O. M. 2024 “Modelling and Distribution of Electricity Load Forecasting in Nigeria Power System,” (Olu-Ode Community). International Journal of Advanced Engineering and Nano Technology (IJAENT) ISSN: 2347-6389 (Online), DOI: 10.35940/ijaent.A9769.11020224 Volume-11 Issue-2 (Google Scholar) - 2024 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    40. Ogbonna, C.C., Eze, V.H.U., Ikechuwu, E.S., Okafor, O., Anichebe, O.C. and Oparaku, O.U., 2023. “A Comprehensive Review of Artificial Neural Network Techniques Used for Smart Meter-Embedded forecasting System,” IDOSR Journal of Applied Science, vol. 8, issue (1), pp.13-24. (Google Scholar) - 2024 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science

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