A neural network model-based approach for power data collection and load forecasting accuracy improvement

[1] Wang, Z., Li, L., Yang, X., Guan, M., Li, Y., & Zhou, B. (2019, February). Fault diagnosis and operation and maintenance of PV components based on BP neural network with data cloud acquisition. In IOP Conference Series: Earth and Environmental Science (Vol. 227, No. 5, p. 052063). IOP Publishing. Wang Z. Li L. Yang X. Guan M. Li Y. Zhou B. ( 2019 , February ). Fault diagnosis and operation and maintenance of PV components based on BP neural network with data cloud acquisition . In IOP Conference Series: Earth and Environmental Science (Vol. 227 , No. 5 , p. 052063 ). IOP Publishing . Search in Google Scholar

[2] Tuttle, J. F., Vesel, R., Alagarsamy, S., Blackburn, L. D., & Powell, K. (2019). Sustainable NOx emission reduction at a coal-fired power station through the use of online neural network modeling and particle swarm optimization. Control Engineering Practice, 93, 104167. Tuttle J. F. Vesel R. Alagarsamy S. Blackburn L. D. Powell K. ( 2019 ). Sustainable NOx emission reduction at a coal-fired power station through the use of online neural network modeling and particle swarm optimization . Control Engineering Practice , 93 , 104167 . Search in Google Scholar

[3] Lin, Z., Liu, X., & Collu, M. (2020). Wind power prediction based on high-frequency SCADA data along with isolation forest and deep learning neural networks. International Journal of Electrical Power & Energy Systems, 118, 105835. Lin Z. Liu X. Collu M. ( 2020 ). Wind power prediction based on high-frequency SCADA data along with isolation forest and deep learning neural networks . International Journal of Electrical Power & Energy Systems , 118 , 105835 . Search in Google Scholar

[4] Ronay, K., Bica, D., & Munteanu, C. (2017). Micro-grid development using artificial neural network for renewable energy forecast and system control. Procedia Engineering, 181, 818-823. Ronay K. Bica D. Munteanu C. ( 2017 ). Micro-grid development using artificial neural network for renewable energy forecast and system control . Procedia Engineering , 181 , 818 - 823 . Search in Google Scholar

[5] Liu, Y., Cheng, H., Kong, X., Wang, Q., & Cui, H. (2019). Intelligent wind turbine blade icing detection using supervisory control and data acquisition data and ensemble deep learning. Energy Science & Engineering, 7(6), 2633-2645. Liu Y. Cheng H. Kong X. Wang Q. Cui H. ( 2019 ). Intelligent wind turbine blade icing detection using supervisory control and data acquisition data and ensemble deep learning . Energy Science & Engineering , 7 ( 6 ), 2633 - 2645 . Search in Google Scholar

[6] Sharifian, A., Ghadi, M. J., Ghavidel, S., Li, L., & Zhang, J. (2018). A new method based on Type-2 fuzzy neural network for accurate wind power forecasting under uncertain data. Renewable energy, 120, 220-230. Sharifian A. Ghadi M. J. Ghavidel S. Li L. Zhang J. ( 2018 ). A new method based on Type-2 fuzzy neural network for accurate wind power forecasting under uncertain data . Renewable energy , 120 , 220 - 230 . Search in Google Scholar

[7] Roni, M. H. K., & Khan, M. A. G. (2017, December). An artificial neural network based predictive approach for analyzing environmental impact on combined cycle power plant generation. In 2017 2nd International Conference on Electrical & Electronic Engineering (ICEEE) (pp. 1-4). IEEE. Roni M. H. K. Khan M. A. G. ( 2017 , December ). An artificial neural network based predictive approach for analyzing environmental impact on combined cycle power plant generation . In 2017 2nd International Conference on Electrical & Electronic Engineering (ICEEE) (pp. 1 - 4 ). IEEE . Search in Google Scholar

[8] Zhuang, W., Jiang, W., Xia, M., & Liu, J. (2024). Dynamic generative residual graph convolutional neural networks for electricity theft detection. IEEE Access, 12, 42737-42750. Zhuang W. Jiang W. Xia M. Liu J. ( 2024 ). Dynamic generative residual graph convolutional neural networks for electricity theft detection . IEEE Access , 12 , 42737 - 42750 . Search in Google Scholar

[9] He, Q., Pang, Y., Jiang, G., & Xie, P. (2020). A spatio-temporal multiscale neural network approach for wind turbine fault diagnosis with imbalanced SCADA data. IEEE transactions on industrial informatics, 17(10), 6875-6884. He Q. Pang Y. Jiang G. Xie P. ( 2020 ). A spatio-temporal multiscale neural network approach for wind turbine fault diagnosis with imbalanced SCADA data . IEEE transactions on industrial informatics , 17 ( 10 ), 6875 - 6884 . Search in Google Scholar

[10] Alawnah, S., & Sagahyroon, A. (2017). Modeling of smartphones’ power using neural networks. EURASIP Journal on Embedded Systems, 2017, 1-11. Alawnah S. Sagahyroon A. ( 2017 ). Modeling of smartphones’ power using neural networks . EURASIP Journal on Embedded Systems , 2017 , 1 - 11 . Search in Google Scholar

[11] Godiño, J. A. V., & Aguilar, F. J. J. E. (2024). Joint data reconciliation and artificial neural network based modelling: Application to a cogeneration power plant. Applied Thermal Engineering, 236, 121720. Godiño J. A. V. Aguilar F. J. J. E. ( 2024 ). Joint data reconciliation and artificial neural network based modelling: Application to a cogeneration power plant . Applied Thermal Engineering , 236 , 121720 . Search in Google Scholar

[12] Haq, M. R., & Ni, Z. (2019). A new hybrid model for short-term electricity load forecasting. IEEE access, 7, 125413-125423. Haq M. R. Ni Z. ( 2019 ). A new hybrid model for short-term electricity load forecasting . IEEE access , 7 , 125413 - 125423 . Search in Google Scholar

[13] Pelletier, F., Masson, C., & Tahan, A. (2016). Wind turbine power curve modelling using artificial neural network. Renewable Energy, 89, 207-214. Pelletier F. Masson C. Tahan A. ( 2016 ). Wind turbine power curve modelling using artificial neural network . Renewable Energy , 89 , 207 - 214 . Search in Google Scholar

[14] Donadio, L., Fang, J., & Porté-Agel, F. (2021). Numerical weather prediction and artificial neural network coupling for wind energy forecast. Energies, 14(2), 338. Donadio L. Fang J. Porté-Agel F. ( 2021 ). Numerical weather prediction and artificial neural network coupling for wind energy forecast . Energies , 14 ( 2 ), 338 . Search in Google Scholar

[15] Lin, G., Feng, H., Feng, X., Wen, H., Li, Y., Hong, S., & Ni, Z. (2021). Electricity theft detection in power consumption data based on adaptive tuning recurrent neural network. Frontiers in Energy Research, 9, 773805. Lin G. Feng H. Feng X. Wen H. Li Y. Hong S. Ni Z. ( 2021 ). Electricity theft detection in power consumption data based on adaptive tuning recurrent neural network . Frontiers in Energy Research , 9 , 773805 . Search in Google Scholar

[16] Yazici, I., Beyca, O. F., & Delen, D. (2022). Deep-learning-based short-term electricity load forecasting: A real case application. Engineering Applications of Artificial Intelligence, 109, 104645. Yazici I. Beyca O. F. Delen D. ( 2022 ). Deep-learning-based short-term electricity load forecasting: A real case application . Engineering Applications of Artificial Intelligence , 109 , 104645 . Search in Google Scholar

[17] Xu, Z., Li, C., & Yang, Y. (2020). Fault diagnosis of rolling bearing of wind turbines based on the variational mode decomposition and deep convolutional neural networks. Applied Soft Computing, 95, 106515. Xu Z. Li C. Yang Y. ( 2020 ). Fault diagnosis of rolling bearing of wind turbines based on the variational mode decomposition and deep convolutional neural networks . Applied Soft Computing , 95 , 106515 . Search in Google Scholar

[18] Heydari, A., Majidi Nezhad, M., Neshat, M., Garcia, D. A., Keynia, F., De Santoli, L., & Bertling Tjernberg, L. (2021). A combined fuzzy GMDH neural network and grey wolf optimization application for wind turbine power production forecasting considering SCADA data. Energies, 14(12), 3459. Heydari A. Majidi Nezhad M. Neshat M. Garcia D. A. Keynia F. De Santoli L. Bertling Tjernberg L. ( 2021 ). A combined fuzzy GMDH neural network and grey wolf optimization application for wind turbine power production forecasting considering SCADA data . Energies , 14 ( 12 ), 3459 . Search in Google Scholar

[19] Jazayeri, K., Jazayeri, M., & Uysal, S. (2016). Comparative analysis of Levenberg-Marquardt and Bayesian regularization backpropagation algorithms in photovoltaic power estimation using artificial neural network. In Advances in Data Mining. Applications and Theoretical Aspects: 16th Industrial Conference, ICDM 2016, New York, NY, USA, July 13-17, 2016. Proceedings 16 (pp. 80-95). Springer International Publishing. Jazayeri K. Jazayeri M. Uysal S. ( 2016 ). Comparative analysis of Levenberg-Marquardt and Bayesian regularization backpropagation algorithms in photovoltaic power estimation using artificial neural network . In Advances in Data Mining. Applications and Theoretical Aspects: 16th Industrial Conference, ICDM 2016 , New York, NY, USA , July 13-17 , 2016 . Proceedings 16 (pp. 80 - 95 ). Springer International Publishing . Search in Google Scholar

[20] Imani, M. (2021). Electrical load-temperature CNN for residential load forecasting. Energy, 227, 120480. Imani M. ( 2021 ). Electrical load-temperature CNN for residential load forecasting . Energy , 227 , 120480 . Search in Google Scholar

[21] Ridwana, I., Nassif, N., & Choi, W. (2020). Modeling of building energy consumption by integrating regression analysis and artificial neural network with data classification. Buildings, 10(11), 198. Ridwana I. Nassif N. Choi W. ( 2020 ). Modeling of building energy consumption by integrating regression analysis and artificial neural network with data classification . Buildings , 10 ( 11 ), 198 . Search in Google Scholar

[22] Irwansyah, A., Muhammad, E., Arifin, F., Iman, B. N., & Hermawan, H. (2023). Power consumption predictive analytics and automatic anomaly detection based on CNN-LSTM neural networks. Jurnal Rekayasa Elektrika, 19(4). Irwansyah A. Muhammad E. Arifin F. Iman B. N. Hermawan H. ( 2023 ). Power consumption predictive analytics and automatic anomaly detection based on CNN-LSTM neural networks . Jurnal Rekayasa Elektrika , 19 ( 4 ). Search in Google Scholar

[23] Zhao, Y., Li, W., Zhang, J., Jiang, C., & Chen, S. (2023). Real-time energy consumption prediction method for air-conditioning system based on long short-term memory neural network. Energy and Buildings, 298, 113527. Zhao Y. Li W. Zhang J. Jiang C. Chen S. ( 2023 ). Real-time energy consumption prediction method for air-conditioning system based on long short-term memory neural network . Energy and Buildings , 298 , 113527 . Search in Google Scholar

[24] Kim, Y., Son, H. G., & Kim, S. (2019). Short term electricity load forecasting for institutional buildings. Energy Reports, 5, 1270-1280. Kim Y. Son H. G. Kim S. ( 2019 ). Short term electricity load forecasting for institutional buildings . Energy Reports , 5 , 1270 - 1280 . Search in Google Scholar

[25] Ma, Y., Zhou, M., Zhang, J., Zhang, Z., Wang, J., Zhao, Y., & Cheng, H. (2024, April). Design and Implementation of a Unified Power Data Measurement and Acquisition System Based on Deep Neural Network. In 2024 Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC) (pp. 321-325). IEEE. Ma Y. Zhou M. Zhang J. Zhang Z. Wang J. Zhao Y. Cheng H. ( 2024 , April ). Design and Implementation of a Unified Power Data Measurement and Acquisition System Based on Deep Neural Network . In 2024 Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC) (pp. 321 - 325 ). IEEE . Search in Google Scholar

[26] Yan, D., ZheLong, W., CongCong, L., HongXia, Z., & Jian, L. X. (2017, October). Study on fault diagnosis of electric energy data acquire system based on the BP neural network. In 2017 Chinese Automation Congress (CAC) (pp. 251-254). IEEE. Yan D. ZheLong W. CongCong L. HongXia Z. Jian L. X. ( 2017 , October ). Study on fault diagnosis of electric energy data acquire system based on the BP neural network . In 2017 Chinese Automation Congress (CAC) (pp. 251 - 254 ). IEEE . Search in Google Scholar

[27] Ciancetta, F., Bucci, G., Fiorucci, E., Mari, S., & Fioravanti, A. (2020). A new convolutional neural network-based system for NILM applications. IEEE Transactions on Instrumentation and Measurement, 70, 1-12. Ciancetta F. Bucci G. Fiorucci E. Mari S. Fioravanti A. ( 2020 ). A new convolutional neural network-based system for NILM applications . IEEE Transactions on Instrumentation and Measurement , 70 , 1 - 12 . Search in Google Scholar

[28] Kisvari, A., Lin, Z., & Liu, X. (2021). Wind power forecasting–A data-driven method along with gated recurrent neural network. Renewable Energy, 163, 1895-1909. Kisvari A. Lin Z. Liu X. ( 2021 ). Wind power forecasting–A data-driven method along with gated recurrent neural network . Renewable Energy , 163 , 1895 - 1909 . Search in Google Scholar

[29] Lee, H., Kim, J., Park, J. H., & Chung, S. H. (2023). Power system transient stability assessment using convolutional neural network and saliency map. Energies, 16(23), 7743. Lee H. Kim J. Park J. H. Chung S. H. ( 2023 ). Power system transient stability assessment using convolutional neural network and saliency map . Energies , 16 ( 23 ), 7743 . Search in Google Scholar

[30] Zhou, L., Ouyang, X., Ying, H., Han, L., Cheng, Y., & Zhang, T. (2018, October). Cyber-attack classification in smart grid via deep neural network. In Proceedings of the 2nd international conference on computer science and application engineering (pp. 1-5). Zhou L. Ouyang X. Ying H. Han L. Cheng Y. Zhang T. ( 2018 , October ). Cyber-attack classification in smart grid via deep neural network . In Proceedings of the 2nd international conference on computer science and application engineering (pp. 1 - 5 ). Search in Google Scholar

[31] Oprea, S. V., Pîrjan, A., Căruțașu, G., Petroșanu, D. M., Bâra, A., Stănică, J. L., & Coculescu, C. (2018). Developing a mixed neural network approach to forecast the residential electricity consumption based on sensor recorded data. Sensors, 18(5), 1443. Oprea S. V. Pîrjan A. Căruțașu G. Petroșanu D. M. Bâra A. Stănică J. L. Coculescu C. ( 2018 ). Developing a mixed neural network approach to forecast the residential electricity consumption based on sensor recorded data . Sensors , 18 ( 5 ), 1443 . Search in Google Scholar

[32] Miraki, A., Parviainen, P., & Arghandeh, R. (2024). Electricity demand forecasting at distribution and household levels using explainable causal graph neural network. Energy and AI, 16, 100368. Miraki A. Parviainen P. Arghandeh R. ( 2024 ). Electricity demand forecasting at distribution and household levels using explainable causal graph neural network . Energy and AI , 16 , 100368 . Search in Google Scholar

[33] Zhang, J., Wei, Y. M., Li, D., Tan, Z., & Zhou, J. (2018). Short term electricity load forecasting using a hybrid model. Energy, 158, 774-781. Zhang J. Wei Y. M. Li D. Tan Z. Zhou J. ( 2018 ). Short term electricity load forecasting using a hybrid model . Energy , 158 , 774 - 781 . Search in Google Scholar

[34] Aguilar Madrid, E., & Antonio, N. (2021). Short-term electricity load forecasting with machine learning. Information, 12(2), 50. Aguilar Madrid E. Antonio N. ( 2021 ). Short-term electricity load forecasting with machine learning . Information , 12 ( 2 ), 50 . Search in Google Scholar

[35] Liu, Y., Wang, W., & Ghadimi, N. (2017). Electricity load forecasting by an improved forecast engine for building level consumers. Energy, 139, 18-30. Liu Y. Wang W. Ghadimi N. ( 2017 ). Electricity load forecasting by an improved forecast engine for building level consumers . Energy , 139 , 18 - 30 . Search in Google Scholar

[36] Solyali, D. (2020). A comparative analysis of machine learning approaches for short-/long-term electricity load forecasting in Cyprus. Sustainability, 12(9), 3612. Solyali D. ( 2020 ). A comparative analysis of machine learning approaches for short-/long-term electricity load forecasting in Cyprus . Sustainability , 12 ( 9 ), 3612 . Search in Google Scholar

[37] Abdul Azeem,Idris Ismail,Syed Sheeraz Mohani,Kamaluddeen Usman Danyaro,Umair Hussain,Shahroz Shabbir & Rahimi Zaman Bin Jusoh. (2025). Mitigating concept drift challenges in evolving smart grids: An adaptive ensemble LSTM for enhanced load forecasting. Energy Reports1369-1383. Abdul Azeem Idris Ismail Syed Sheeraz Mohani Kamaluddeen Usman Danyaro Umair Hussain Shahroz Shabbir Rahimi Zaman Bin Jusoh ( 2025 ). Mitigating concept drift challenges in evolving smart grids: An adaptive ensemble LSTM for enhanced load forecasting . Energy Reports 1369 - 1383 . Search in Google Scholar

[38] Liang Liu,Silin Peng & Zhijun Wu. (2025). Detection of CIFA using SMOTEBoost and LSTM in NDN. Computers & Security104251-104251. Liang Liu Silin Peng Zhijun Wu ( 2025 ). Detection of CIFA using SMOTEBoost and LSTM in NDN . Computers & Security 104251 - 104251 . Search in Google Scholar

[39] Hayato Oka,Keiko Ono & Adamidis Panagiotis. (2024). Attention-Based PSO-LSTM for Emotion Estimation Using EEG. Sensors(24),8174-8174. Hayato Oka Keiko Ono Adamidis Panagiotis ( 2024 ). Attention-Based PSO-LSTM for Emotion Estimation Using EEG . Sensors ( 24 ), 8174 - 8174 . Search in Google Scholar

[40] Van Hien Nguyen,Tri Cuong Do & Kyoung Kwan Ahn. (2024). Implementing PSO-LSTM-GRU Hybrid Neural Networks for Enhanced Control and Energy Efficiency of Excavator Cylinder Displacement. Mathematics(20),3185-3185. Van Hien Nguyen Tri Cuong Do Kyoung Kwan Ahn ( 2024 ). Implementing PSO-LSTM-GRU Hybrid Neural Networks for Enhanced Control and Energy Efficiency of Excavator Cylinder Displacement . Mathematics ( 20 ), 3185 - 3185 . Search in Google Scholar