Generalized stability of artificial emotional neural network in predicting domestic power peak demand

Main Article Content

Suthasinee Iamsa-at
Punyaphol Horata
Khamron Sunat

Abstract

Predicting an optimal domestic power peak demand is very important for long-term electricity construction planning as the electricity cannot be stored permanently. If the prediction can give a yield close to the actual demand, the electricity suppliers can save their construction costs and provide their customers with a lower cost of electricity. However, accurate predictions still require improvement. This work, therefore, presented the predicting problem using a modified artificial emotional neural network (AENN) based on an improved JAYA optimizer. This study also applied extreme learning machine (ELM) to compute the expanded feature in the AENN. A real case study of Thailand’s power peak demand was considered, which was prepared using a rolling mechanism, to demonstrate the performance of a developed predicting model when contrasted with state-of-the-art of AENN models, artificial neural network with Levenberg-Marquardt, AENN methods based on winner-take-all approach, and improved brain emotional learning-based AENN model. Performance analyses demonstrated that the proposed model provided improvements in performance and generalized stability over the comparative models.

Downloads

Download data is not yet available.

Article Details

How to Cite
Iamsa-at, S., Horata, P., & Sunat, K. (2022). Generalized stability of artificial emotional neural network in predicting domestic power peak demand. Science, Engineering and Health Studies, 16, 22020004. https://doi.org/10.14456/sehs.2022.30
Section
Physical sciences

References

Akay, D., and Atak, M. (2007). Grey prediction with rolling mechanism for electricity demand forecasting of Turkey. Energy, 32(9), 1670-1675.

Beyca, O. F., Ervural, B. C., Tatoglu, E., Ozuyar, P. G., and Zaim, S. (2019). Using machine learning tools for forecasting natural gas consumption in the province of Istanbul. Energy Economics, 80, 937-949.

Chen, B. J., and Chang, M. W. (2004). Load forecasting using support vector machines: A study on EUNITE competition 2001. IEEE Transactions on Power Systems, 19(4), 1821-1830.

Dai, S., Niu, D., and Li, Y. (2018). Forecasting of energy consumption in China based on ensemble empirical mode decomposition and least squares support vector machine optimized by improved shuffled frog leaping algorithm. Applied Sciences, 8(5), 678.

Debnath, K. B., and Mourshed, M. (2018). Forecasting methods in energy planning models. Renewable and Sustainable Energy Reviews, 88, 297-325.

Elattar, E. E., Goulermas, J., and Wu, Q. H. (2010). Electric load forecasting based on locally weighted support vector regression. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(4), 438-447.

Electricity Generating Authority of Thailand. (2020) Statistics Demand Annual. [Online URL: https://www.egat.co.th/home/statistics-demand-annual/] accessed on August 1, 2020. [in Thai]

Hong, W. C. (2009). Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model. Energy Conversion and Management, 50(1), 105-117.

Hu, Z., Bao, Y., and Xiong, T. (2013). Electricity load forecasting using support vector regression with memetic algorithms. The Scientific World Journal, 292575.

Huang, G. B., Zhu, Q. Y., and Siew, C. K. (2006). Extreme learning machine: Theory and applications. Neurocomputing, 70(1-3), 489-501.

Kazemzadeh, M. R., Amjadian, A., and Amraee, T. (2020). A hybrid data mining driven algorithm for long term electric peak load and energy demand forecasting. Energy, 204 (6), 117948.

LeDoux, J. E. (1989). Cognitive-emotional interactions in the brain. Cognition & Emotion, 3(4), 267-289.

Lee, K. Y., Cha, Y. T., and Park, J. H. (1992). Short-term load forecasting using an artificial neural network. IEEE Transactions on Power Systems, 7(1), 124-132.

Li, H., Guo, S., Zhao, H., Su, C., and Wang, B. (2012). Annual electric load forecasting by a least squares support vector machine with a fruit fly optimization algorithm. Energies, 5(11), 4430-4445.

Li, H. Z., Guo, S., Li, C. J., and Sun, J. Q. (2013). A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm. Knowledge-Based Systems, 37, 378-387.

Lotfi, E., and Akbarzadeh-T, M. R. (2013). Brain emotional learning-based pattern recognizer. Cybernetics and Systems, 44(5), 402-421.

Lotfi, E., and Akbarzadeh-T, M. R. (2016). A winner-take-all approach to emotional neural networks with universal approximation property. Information Sciences, 346-347, 369-388.

Lotfi, E., Khosravi, A., Akbarzadeh-T, M. R., and Nahavandi, S. (2014). Wind power forecasting using emotional neural networks. In 2014 IEEE International Conference on Systems, Man, and Cybernetics, pp. 311-316. California, USA.

McNeil, M. A., Karali, N., and Letschert, V. (2019). Forecasting Indonesia's electricity load through 2030 and peak demand reductions from appliance and lighting efficiency. Energy for Sustainable Development, 49(7), 65-77.

Mei, Y., Tan, G., and Liu, Z. (2017). An improved brain-inspired emotional learning algorithm for fast classification. Algorithms, 10(2), 70.

Nourani, V., Gökçekuş, H., Umar, I. K., and Najafi, H. (2020). An emotional artificial neural network for prediction of vehicular traffic noise. Science of the Total Environment, 707, 136134.

Nourani, V., Molajou, A., Uzelaltinbulat, S., and Sadikoglu, F. (2019). Emotional artificial neural networks (EANNs) for multi-step ahead prediction of monthly precipitation; case study: Northern Cyprus. Theoretical and Applied Climatology, 138, 1419-1434.

Office of the National Economic and Social Development Board. (2019). The Twelfth National Economic and Social Development Plan (2017-2021). [Online URL: https://www.nesdc.go.th/nesdb_en/ewt_dl_link.php?nid=4345] accessed on August 1, 2020.

Park, D. C., El-Sharkawi, M. A., Marks, R. J., Atlas, L. E., and Damborg, M. J. (1991). Electric load forecasting using an artificial neural network. IEEE Transactions on Power Systems, 6(2), 442-449.

Quan, H., Srinivasan, D., and Khosravi, A. (2013). Short-term load and wind power forecasting using neural network-based prediction intervals. IEEE Transactions on Neural Networks and Learning Systems, 25(2), 303-315.

Rao, R. V. (2016). Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7(1), 19-34.

Sarduy, J. R. G., Di Santo, K. G., and Saidel, M. A. (2016). Linear and non-linear methods for prediction of peak load at University of São Paulo. Measurement, 78, 187-201.

Schmidt, W. F., Kraaijveld, M. A., and Duin, R. P. (1992). Feed forward neural networks with random weights. In Proceedings of the 11th IAPR International Conference on Pattern Recognition: Vol. II. Conference B: Pattern Recognition Methodology and Systems, pp. 1-4. The Hague, Netherlands.

Sutthichaimethee, J., and Kubaha, K. (2018). Forecasting energy-related carbon dioxide emissions in Thailand’s construction sector by enriching the LS-ARIMAXi-ECM model. Sustainability, 10(10), 3593.

Wang, J., Lu, S., Wang, S. H., and Zhang, Y. D. (2021). A review on extreme learning machine. Multimedia Tools and Applications. [Online URL: https://link.springer.com/article/10.1007/s11042-021-11007-7] accessed on December 22, 2022.

Warid, W., Hizam, H., Mariun, N., and Abdul-Wahab, N. I. (2016). Optimal power flow using the Jaya algorithm. Energies, 9(9), 678.

Yu, K., Liang, J. J., Qu, B. Y., Chen, X., and Wang, H. (2017). Parameters identification of photovoltaic models using an improved JAYA optimization algorithm. Energy Conversion and Management, 150, 742-753.

Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50(17), 159-175.

Zhang, H., Yang, C., and Qiao, J. (2022). Emotional neural network based on improved CLPSO algorithm for time series prediction. Neural Processing Letters, 54(2), 1131-1154.

Zhao, J., Lin, C. M., and Chao, F. (2019). Wavelet fuzzy brain emotional learning control system design for MIMO uncertain nonlinear systems. Frontiers in Neuroscience, 12, 918.

Zhao, H., and Guo, S. (2014). Annual energy consumption forecasting based on PSOCA-GRNN model. Abstract and Applied Analysis, 17630.

Zhao, H., and Guo, S. (2016). An optimized grey model for annual power load forecasting. Energy, 107, 272-286.

Zhao, H., Zhao, H., and Guo, S. (2016). Using GM (1, 1) optimized by MFO with rolling mechanism to forecast the electricity consumption of inner Mongolia. Applied Sciences, 6(1), 20.