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All Journal International Journal of Electrical and Computer Engineering
Ladha, Lekshmy Premachandran
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Computer Science & IT Electrical & Electronics Engineering

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Journal : international journal of electrical and computer engineering

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A comparative study of long short-term memory based long-term electrical load forecasting techniques with hyperparameter optimization Mani, Geetha; Seetharaman, Suresh; Kandasamy, Jothinathan; Ladha, Lekshmy Premachandran; Mohandas, Anish John Paul; Sivasubramoniam, Swamy; Renugadevi, Valarmathi Iyappan
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7080-7089

Abstract

Long-term load forecasting (LTLF) is crucial for reliable electricity supply, infrastructure planning, and informed energy policies, ensuring grid stability and efficient resource allocation. Traditional methods, like statistical models and expert judgment, rely on historical data but may struggle with dynamic changes in technology, regulations, and consumer behavior. Addressing challenges such as economic uncertainties, seasonal variations, data quality, and integrating renewable energy requires advanced forecasting models and adaptive strategies. This research aims to develop an efficient LTLF model for the Coimbatore region in Tamil Nadu, India, using long short-term memory (LSTM) networks. While LSTM has limitations in capturing long- term dependencies and requires high data quality and complex management, optimizing hyperparameters, including through the opposition-based hunter- prey optimization (OHPO) technique, is explored to enhance its predictive performance. The results show that the proposed OHPO-configured LSTM model for LTLF achieves superior performance compared to other techniques, with a mean square error (MSE) of 0.25, root mean square error (RMSE) of 0.5 and mean absolute percentage error (MAPE) of 0.27. This research underscores the significance of improving LTLF precision for informed decision-making in infrastructure planning and energy policy formulation.
Developing a mathematical model for predicting ultimate tensile strength to identify optimal machining parameters Thilagham, Kancheepuram Thirumal; Ladha, Lekshmy Premachandran; Tiwary, Anand Prakash; Haribhau, Munde Kashinath; Dudhajirao, Darade Pradipkumar; Kumar, Shailseh Ranjan
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7116-7125

Abstract

Identifying the ultimate tensile strength (UTS) for friction stir welded joints between AA6082-T6 and AA2014-T87 is crucial for ensuring material compatibility, optimizing welding parameters, and assessing mechanical performance. This information helps engineers design safer, more reliable structures and optimize the welding process, improving the utilization of these aluminum alloys in high-performance applications. Traditional methods for identifying UTS face challenges such as material variability, precise experimental setup, the influence of welding parameters, and are time-consuming and costly. This research aims to develop a mathematical model capable of identifying the UTS based on given inputs, specifically optimal tilt angle, travel speed, and rotational speed. The developed model is further utilized to identify the optimal machining parameters. Processing this manually or through trial and error is time-consuming and complex, highlighting the need to incorporate optimization techniques to determine the optimal parameters efficiently. This research involves several optimization techniques, among which the evolved wild horse optimization (EWHO) performs better, achieving a mean square error of 0.45. This is superior performance compared to other optimization techniques and employed prediction models. This approach saves time, reduces complexity, and enhances precision compared to manual or trial-and-error methods, ultimately improving the efficiency and reliability of material processing.
Co-Authors Dudhajirao, Darade Pradipkumar Geetha Mani, Geetha Haribhau, Munde Kashinath Kandasamy, Jothinathan Kumar, Shailseh Ranjan Mohandas, Anish John Paul Renugadevi, Valarmathi Iyappan Seetharaman, Suresh Sivasubramoniam, Swamy Thilagham, Kancheepuram Thirumal Tiwary, Anand Prakash