Artificial Neural Network-Based Short-Term Load Forecasting for Mymensingh Area of Bangladesh
Electrical load forecasting is considered to be one of the most indispensable parts of a modern-day electrical power system. To ensure a reliable and efficient supply of electric energy, special emphasis should have been put on the predictive feature of electricity supply. Artificial Neural Network-based approaches have emerged to be a significant area of interest for electric load forecasting research. This paper proposed an Artificial Neural Network model based on the particle swarm optimization algorithm for improved electric load forecasting for Mymensingh, Bangladesh. The forecasting model is developed and simulated on the MATLAB environment with a large number of training datasets. The model is trained based on eight input parameters including historical load and weather data. The predicted load data are then compared with an available dataset for validation. The proposed neural network model is proved to be more reliable in terms of day-wise load forecasting for Mymensingh, Bangladesh.
[1] T. K. Chheepa and T. Manglani, “A Critical review on employed techniques for short term load forecasting”, International Research Journal of Engineering and Technology, vol. 4,no. 6, June 2017.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] K. B. Debnath, and M. Mourshed, “Forecasting methods in energy planning models”, Renewable and Sustainable Energy Reviews, vol. 88, pp. 297-325, 2018.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] A. Anand and L. Suganthi, “Hybrid GA-PSO optimization of artificial neural network for forecasting electricity demand”, Energies, MDPI, Open Access Journal, vol. 11, no. 4, pp. 1-5, March 2018.
[15] F. Zanek, J. Franco and M. I. M. G. Jorge, “Forecasting Residential Electrical Consumption for the City of Salta, Argentina”, In XX Simposio Argentino de Inteligencia Artificial (ASAI 2019)-JAIIO 48 (Salta), 2019.
[16] N. Patel, R. Patel, and A. Gupta, “Advanced Neural Network Applied In Engineering Science”, International Journal of Engineering Sciences & Research Technology, (2014).
[17] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proc. IEEE Int. Conf. Neural Netw. (ICNN), Nov. 1995, vol. 4, pp. 1942–1948.
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[19] R. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in Proc. 6th Int. Symp. Micro Machine and Human Science (MHS), Oct. 1995, pp. 39–43.
[20] Mirjalili, S.; Hashim, S.Z.M.; Sardroudi, H.M. Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm. Appl. Math. Comput. 2012, 218, 11125–11137.
[21] Zhang Caiqing, Lin Ming and Tang Mingyang, “BP Neural Network Optimized with PSO Algorithm for Daily Load Forecasting, in Information Management, Innovation Management, and Industrial Engineering”, IEEE Conference Publications Vol.3, 2008, PP. 82 – 85.
[22] Zhang Caiqing, Lin Ming and Tang Mingyang, “BP Neural Network Optimized with PSO Algorithm for Daily Load Forecasting, in Information Management, Innovation Management, and Industrial Engineering”, IEEE Conference Publications Vol.3, 2008, PP. 82 – 85.
[23] Power Grid Company of Bangladesh (PGCB), http://pgcb.gov.bd/site/page/0dd38e19-7c70-4582-95ba-078fccb609a8/-?fbclid=IwAR3TtV-k9JYKzlks7pvpxrRXbaay6qc4rmGWvLq-AP-QGtzEGbS07r_G-Jw, accessed on 23rd September 2020.
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[1] T. K. Chheepa and T. Manglani, “A Critical review on employed techniques for short term load forecasting”, International Research Journal of Engineering and Technology, vol. 4,no. 6, June 2017.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] K. B. Debnath, and M. Mourshed, “Forecasting methods in energy planning models”, Renewable and Sustainable Energy Reviews, vol. 88, pp. 297-325, 2018.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] A. Anand and L. Suganthi, “Hybrid GA-PSO optimization of artificial neural network for forecasting electricity demand”, Energies, MDPI, Open Access Journal, vol. 11, no. 4, pp. 1-5, March 2018.
[15] F. Zanek, J. Franco and M. I. M. G. Jorge, “Forecasting Residential Electrical Consumption for the City of Salta, Argentina”, In XX Simposio Argentino de Inteligencia Artificial (ASAI 2019)-JAIIO 48 (Salta), 2019.
[16] N. Patel, R. Patel, and A. Gupta, “Advanced Neural Network Applied In Engineering Science”, International Journal of Engineering Sciences & Research Technology, (2014).
[17] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proc. IEEE Int. Conf. Neural Netw. (ICNN), Nov. 1995, vol. 4, pp. 1942–1948.
[18] A. P. Engelbrecht, “Particle swarm optimization: Where does it belong?,” in Proc. IEEE Swarm Intell. Symp., May 2006, pp. 48–54.
[19] R. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in Proc. 6th Int. Symp. Micro Machine and Human Science (MHS), Oct. 1995, pp. 39–43.
[20] Mirjalili, S.; Hashim, S.Z.M.; Sardroudi, H.M. Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm. Appl. Math. Comput. 2012, 218, 11125–11137.
[21] Zhang Caiqing, Lin Ming and Tang Mingyang, “BP Neural Network Optimized with PSO Algorithm for Daily Load Forecasting, in Information Management, Innovation Management, and Industrial Engineering”, IEEE Conference Publications Vol.3, 2008, PP. 82 – 85.
[22] Zhang Caiqing, Lin Ming and Tang Mingyang, “BP Neural Network Optimized with PSO Algorithm for Daily Load Forecasting, in Information Management, Innovation Management, and Industrial Engineering”, IEEE Conference Publications Vol.3, 2008, PP. 82 – 85.
[23] Power Grid Company of Bangladesh (PGCB), http://pgcb.gov.bd/site/page/0dd38e19-7c70-4582-95ba-078fccb609a8/-?fbclid=IwAR3TtV-k9JYKzlks7pvpxrRXbaay6qc4rmGWvLq-AP-QGtzEGbS07r_G-Jw, accessed on 23rd September 2020.
[24] AccuWeather web portal, https://www.accuweather.com/en/bd/mymensingh/27871/january-weather/27871?year=2014, accessed on 23rd September 2020.
[25] Power Grid Company of Bangladesh (PGCB), http://pgcb.gov.bd/site/page/0dd38e19-7c70-4582-95ba-078fccb609a8/-?fbclid=IwAR3TtV-k9JYKzlks7pvpxrRXbaay6qc4rmGWvLq-AP-QGtzEGbS07r_G-Jw, accessed on 23rd September 2020.
@article{"International Journal of Electrical, Electronic and Communication Sciences:80259", author = "S. M. Anowarul Haque and Md. Asiful Islam", title = "Artificial Neural Network-Based Short-Term Load Forecasting for Mymensingh Area of Bangladesh", abstract = "Electrical load forecasting is considered to be one of the most indispensable parts of a modern-day electrical power system. To ensure a reliable and efficient supply of electric energy, special emphasis should have been put on the predictive feature of electricity supply. Artificial Neural Network-based approaches have emerged to be a significant area of interest for electric load forecasting research. This paper proposed an Artificial Neural Network model based on the particle swarm optimization algorithm for improved electric load forecasting for Mymensingh, Bangladesh. The forecasting model is developed and simulated on the MATLAB environment with a large number of training datasets. The model is trained based on eight input parameters including historical load and weather data. The predicted load data are then compared with an available dataset for validation. The proposed neural network model is proved to be more reliable in terms of day-wise load forecasting for Mymensingh, Bangladesh.", keywords = "Load forecasting, artificial neural network, particle swarm optimization.", volume = "15", number = "3", pages = "54-5", }
