A Network of Neural Model for Small Term Load Prediction Using Novel Feedforward (FITNET) Network of Neural Model
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Abstract
Load forecasting is a challenging task in the setting of modern power systems, which have risen in complexity as conventional and non-conventional energy sources have been integrated into an increasingly varied energy environment. Utility companies are under growing pressure to not just provide cost-effective and adequate power generation, but also to maintain system dependability for today's discriminating customers. While there are several load forecasting systems, neural network-based techniques appear as a potential alternative due to their ability to reveal hidden subtleties within the input/output load data connection, resulting in fewer predicting mistakes. Artificial neural networks (ANNs)-based short-term load prediction methods have become more widely used, successfully overcoming issues related to weather, temperature, humidity, precipitation, air pressure, and the shifting patterns of human and industrial activity. This has made accurate load forecasting easier. We present FITNET, a novel feedforward neural network model designed for short-term load prediction (STLF), as our contribution to this effort. FITNET is Zunique in that it can adjust to events occurring in real time and allows training with a wide range of input kinds and sequences. We collected data from the ISO New England NE-Pool region over a period of four and a half years and combined it into a single, coherent dataset. Important inputs include time-related components and meteorological characteristics, such as day and night, dew point, and dry bulb temperature, with weekdays having a substantial impact on the output data. To improve the performance of the ANN model, we carefully examined alternate neuron configurations, using the Levenberg-Marquardt backpropagation approach for training. Extensive testing of our suggested model across both weekly and daily load forecasting methodologies continually shows outstanding efficiency, with the ANN model constantly having a forecasting MAPE of less than 1%. This finding emphasizes the model's stability and its potential to considerably improve the dependability and cost-effectiveness of power generation in today's complex and ever-changing energy landscape.
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Pakistan Journal Emerging Science and Technologies (PJEST) in collaboration with Govt. Islamia Graduate College Civil Lines Lahore, Pakistan is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
M. W. Ashraf, S. Tayyaba, and N. Afzulpurkar, "Micro electromechanical systems (MEMS) based microfluidic devices for biomedical applications," International journal of molecular sciences, vol. 12, no. 6, pp. 3648-3704, 2011. https://doi.org/10.3390/ijms12063648
E. Stemme and G. Stemme, "A valveless diffuser/nozzle-based fluid pump," Sensors and Actuators A: physical, vol. 39, no. 2, pp. 159-167, 1993. https://doi.org/10.1016/0924-4247(93)80213-Z
M. J. Afzal, S. Tayyaba, M. W. Ashraf, M. K. Hossain, M. J. Uddin, and N. Afzulpurkar, "Simulation, fabrication and analysis of silver based ascending sinusoidal microchannel (ASMC) for implant of varicose veins," Micromachines, vol. 8, no. 9, p. 278, 2017. https://doi.org/10.3390/mi8090278
M. J. Afzal, M. W. Ashraf, S. Tayyaba, M. K. Hossain, and N. Afzulpurkar, "Sinusoidal Microchannel with Descending Curves for Varicose Veins Implantation," Micromachines, vol. 9, no. 2, p. 59, 2018. https://doi.org/10.3390/mi9020059
S. Tayyaba, M. W. Ashraf, Z. Ahmad, N. Wang, M. J. Afzal, and N. Afzulpurkar, "Fabrication and Analysis of Polydimethylsiloxane (PDMS) Microchannels for Biomedical Application," Processes, vol. 9, no. 1, p. 57, 2021. https://doi.org/10.3390/pr9010057
Czapaj, Rafał, Jacek Kamiński, and Maciej Sołtysik. "A Review of Auto-Regressive Methods Applications to Short-Term Demand Forecasting in Power Systems." Energies 15, no. 18 (2022): 6729.
https://doi.org/10.3390/en15186729
R. Hu, S. Wen, Z. Zeng, and T. Huang, “A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm”, Neurocomputing, vol. 221, pp. 24-31, 2017. https://doi.org/10.1016/j.neucom.2016.09.027
S. Khatoon, and A.K. Singh, “Analysis and comparison of various methods available for load forecasting: An overview”, In 2014 Innovative Applications of Computational Intelligence on Power, Energy and Controls with their impact on Humanity (CIPECH), pp. 243-247, Nov. 2014. https://doi.org/10.1109/CIPECH.2014.7019112
K. H. Baesmat, and A. Shiri, “A new combined method for future energy forecasting in electrical networks”, International Transactions on Electrical Energy Systems, vol. 29, no. 3, pp. 2749, 2019. https://doi.org/10.1002/etep.2749
K. G. Boroojeni, M. H. Amini, S. Bahrami, S. S. Iyengar, A. I. Sarwat, and O. Karabasoglu, “A novel multi-time-scale modeling for electric power demand forecasting: From short-term to medium-term horizon”, Electric Power Systems Research, vol. 142, pp. 58-73, 2017. https://doi.org/10.1016/j.epsr.2016.08.031
K. Zor, O. Timur, and A. Teke, “A state-of-the-art review of artificial intelligence techniques for short-term electric load forecasting”, In2017 6th International Youth Conference on Energy (IYCE), pp. 1-7, June 2017. https://doi.org/10.1109/IYCE.2017.8003734
L. Suganthi, and A. A. Samuel, “Energy models for demand forecasting-A review”, Renewable and sustainable energy reviews, vol. 16, no. 2, pp. 1223-1240, 2012. https://doi.org/10.1016/j.rser.2011.08.014
S. Ryu, J. Noh, and H. Kim, “Deep neural network-based demand-side short term load forecasting”, Energies, vol. 10, no. 1, pp. 3, 2017. https://doi.org/10.3390/en10010003
K. B. Debnath, and M. Mourshed, “Forecasting methods in energy planning models”, Renewable and Sustainable Energy Reviews, vol. 88, pp. 297-325, 2018. https://doi.org/10.1016/j.rser.2018.02.002
F. Rodrigues, C. Caldeira, and J. M. F. Calado, “Neural networks applied to short term load forecasting: A case study”, In Smart Energy Control Systems for Sustainable Buildings, pp. 173-197, 2017. https://doi.org/10.1007/978-3-319-52076-6_8
M. Mordjaoui, S. Haddad, A. Medoued, and A. Laouafi, “Electric load forecasting by using dynamic neural network”, International journal of hydrogen energy, vol. 42, no. 28, pp. 17655-17663, 2017. https://doi.org/10.1016/j.ijhydene.2017.03.101
S. Matthew, and S. Satyanarayana, “An overview of short-term load forecasting in electrical power system using fuzzy controller”, In 2016 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), pp. 296-300, Sep. 2016. https://doi.org/10.1109/ICRITO.2016.7784969
D. K. Chaturvedi, and S. A. Premdayal, “Short Term Load Forecasting (STLF) Using Generalized Neural Network (GNN) Trained with Adaptive GA”, In International Conference on Swarm, Evolutionary, and Memetic Computing, pp. 132-143, Dec. 2013. https://doi.org/10.1007/978-3-319-03756-1_12
Haque, S. A., & Islam, M. A. (2021). Artificial Neural Network-Based Short-Term Load Forecasting for Mymensingh Area of Bangladesh. International Journal of Electrical and Computer Engineering, 15(3), 99-103.
Anand, A., & Suganthi, L. (2018). Hybrid GA-PSO optimization of artificial neural network for forecasting electricity demand. Energies, 11(4), 728. https://doi.org/10.3390/en11040728
Shafiei Chafi, Z., & Afrakhte, H. (2021). Short-Term Load Forecasting Using Neural Network and Particle Swarm Optimization (PSO) Algorithm. Mathematical Problems in Engineering, 2021. https://doi.org/10.1155/2021/5598267
Haque, S. A., & Islam, M. A. (2021). Artificial Neural Network-Based Short-Term Load Forecasting for Mymensingh Area of Bangladesh. International Journal of Electrical and Computer Engineering, 15(3), 99-103.
Zheng, X., Ran, X., & Cai, M. (2020). Short-term load forecasting of power system based on neural network intelligent algorithm. IEEE Access. https://doi.org/10.1109/ACCESS.2020.3021064
Mordjaoui, M., Haddad, S., Medoued, A., & Laouafi, A. (2017). Electric load forecasting by using dynamic neural network. International journal of hydrogen energy, 42(28), 17655-17663. https://doi.org/10.1016/j.ijhydene.2017.03.101
Mohammad, S., & Hasan, M. K. An Effective Artificial Neural Network based Power Load Prediction Algorithm. International Journal of Computer Applications, 975, 8887.
Nguyen, T. A., Ly, H. B., Mai, H. V. T., & Tran, V. Q. (2021). On the Training Algorithms for Artificial Neural Network in Predicting the Shear Strength of Deep Beams. Complexity, 2021. https://doi.org/10.1155/2021/5548988
Rodrigues, F., Cardeira, C., & Calado, J. M. F. (2017). Neural networks applied to short term load forecasting: A case study. In Smart Energy Control Systems for Sustainable Buildings (pp. 173-197). Springer, Cham. https://doi.org/10.1007/978-3-319-52076-6_8
Buitrago, J., & Asfour, S. (2017). Short-term forecasting of electric loads using nonlinear autoregressive artificial neural networks with exogenous vector inputs. Energies, 10(1), 40. https://doi.org/10.3390/en10010040