| Title | Modeling, Simulation and Optimization of Power Plant Energy Sustainability for IoT Enabled Smart Cities Empowered With Deep Extreme Learning Machine |
|---|---|
| ID_Doc | 42707 |
| Authors | Abbas, S; Khan, MA; Falcon-Morales, LE; Rehman, A; Saeed, Y; Zareei, M; Zeb, A; Mohamed, EM |
| Title | Modeling, Simulation and Optimization of Power Plant Energy Sustainability for IoT Enabled Smart Cities Empowered With Deep Extreme Learning Machine |
| Year | 2020 |
| Published | |
| Abstract | A smart city is a sustainable and effective metropolitan hub, that offers its residents high excellence of life through appropriate resource management. Energy management is among the most challenging problems in such metropolitan areas due to the difficulty and key role of energy systems. To optimize the benefit from the available megawatt-hours, it is important to predict the maximum electrical power output of a baseload power plant. This paper explores the method of a deep extreme learning machine to create a predictive model that can predict a combined cycle power plant & x2019;s hourly full-load electrical output. An intelligent energy management solution can be achieved by properly monitoring and controlling these resources through the internet of things (IoT). The universe of artificial intelligence has produced many strides through deep learning algorithms and these methods were used for data analysis. Nonetheless, for further accuracy, deep extreme learning machine (DELM) is another candidate to be investigated for analyses of the data sequence. By using the DELM approach, a high level of reliability with a minimum error rate is achieved. The approach shows better results compared to previous investigations since previous studies could not meet the findings up to the mark and unable to predict power plant electrical energy output efficiently. During the investigation, it is shown that the proposed approach has the highest accuracy rate of 98.6 & x0025; with 70 & x0025; of training (33488 samples), 30 & x0025; of test and validation (14352 examples). Simulation results validate the prediction effectiveness of the proposed scheme. |
| https://ieeexplore.ieee.org/ielx7/6287639/8948470/09016175.pdf |
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