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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.
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