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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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| Paper Title: | Towards Sustainable Smart Systems: A Deep Learning Approach to Electric Load Forecasting, Materials Synthesizability, and Machine Learning |
| Authors Name: | Akkari Dhanush , Nikhil Shinde , Syed Nayeemulla , Prajakta Biradar |
| Download E-Certificate: | Download |
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IJRTI_189077
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| Published Paper Id: | IJRTI2401118 |
| Published In: | Volume 9 Issue 1, January-2024 |
| DOI: | https://doi.org/10.5281/zenodo.10580719 |
| Abstract: | The accurate forecasting of electric load in smart grid applications and the prediction of synthesizability for crystalline materials are crucial aspects in decision-making and the advancement of power systems and materials science. This study proposes an integrated framework that combines deep learning techniques with heuristic algorithms to address these challenges. A novel hybrid model is introduced for electric load forecasting, encompassing a data pre-processing and feature selection module based on the modified mutual information (MMI) technique. The training and forecasting module employs a factored conditional restricted Boltzmann machine (FCRBM), designed to capture the non-linear and stochastic behavior inherent in electric load profiles. To optimize the model, a genetic wind-driven (GWDO) optimization algorithm is introduced, facilitating the fine-tuning of adjustable parameters to enhance accuracy. Shifting the focus to materials science, a distinctive approach is taken to predict the synthesizability of crystalline materials by representing atomic structures through three-dimensional pixel-wise images. These images, color-coded by chemical attributes, enable the use of a convolutional encoder to extract latent features related to synthesizability. The model accurately classifies materials into synthesizable crystals and anomalies across diverse crystal structures and chemical compositions. The proposed integrated framework is evaluated using historical hourly load data from three power grids and hypothetical crystals for battery electrode and thermoelectric applications. Comparative analyses with existing forecasting models and synthesizability benchmarks demonstrate the effectiveness of this approach in providing accurate predictions for both electric load and crystalline materials synthesizability. This research contributes to advancing the fields of smart grid management and materials science by offering a unified, data-driven methodology for forecasting and predicting synthesizability. |
| Keywords: | Materials Synthesizability, Electric Load Forecasting, Heuristic Algorithm, Convolutional Encoder, Modified Mutual Information, Smart grid management, Robust predictive models. |
| Cite Article: | "Towards Sustainable Smart Systems: A Deep Learning Approach to Electric Load Forecasting, Materials Synthesizability, and Machine Learning", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 1, page no.787 - 795, January-2024, Available :http://www.ijrti.org/papers/IJRTI2401118.pdf |
| Downloads: | 000205078 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2401118
Registration ID:189077
Published In: Volume 9 Issue 1, January-2024
DOI (Digital Object Identifier): https://doi.org/10.5281/zenodo.10580719
Page No: 787 - 795 Country: Delhi, Delhi, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2401118 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2401118 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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