Support Vector Machine Inspired Load Forecasting of a State University in Haryana

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 15 | 02 | Page : –
    By

    Dheeraj singh,

  • Neha Khurana,

  • Pradeep Singla,

  1. Assistant Professor, Maharishi Dayanand University, Rohtak,, Haryana, India
  2. Assistant professor, Maharishi Dayanand University, Rohtak,, Haryana, India
  3. Assistant professor, Maharishi Dayanand University, Rohtak,, Haryana, India

Abstract

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Estimating the possible environmental impact and determining probable capital requirements are made easier with a solid grasp of electricity demand. Beginning in the middle of the 20th century, demand forecasting for electric power networks was studied theoretically. Prior to that, the study of demand forecasting had not developed because of the small scale of power networks. With the use of statistical prediction techniques, plans for the electric power industry have been created. For fuel management, maintenance scheduling, and budget planning, electric utility companies require monthly peak and annual load forecasts. The approach based on regression models, neural networks, and support vector machines for load forecasting—that is, the maximum and minimum load of an MDU substation in Rohtak, Haryana—is compared in this paper. Many contributing elements have been investigated and tried. The system created to forecast the greatest and minimum electric demand and consumption in MDU is presented in the study. Comparing the selected system to other methods in the study, it has been found that the SVM-based model forecasts the load forecast the best. The study conducted at Maharshi Dayanand University (MDU) in Rohtak, Haryana, focuses on forecasting the maximum and minimum electrical loads at the university’s substation. It compares three machine learning techniques: regression models, artificial neural networks (ANN), and support vector machines (SVM). The findings indicate that the SVM-based model outperforms the others in predicting both peak and minimum loads. Support Vector Machines are particularly effective in handling nonlinear, high-dimensional datasets, which are common in electrical load forecasting. They are less prone to overfitting compared to neural networks, especially when dealing with limited or noisy data. SVMs also offer robust generalization capabilities, making them suitable for scenarios where accurate predictions are crucial for operational planning and energy management. In contrast, regression models, while straightforward and interpretable, often struggle with capturing complex, nonlinear relationships inherent in load data. Artificial Neural Networks can model nonlinear patterns but require extensive data and careful tuning to avoid overfitting, and they may demand significant computational resources. SVMs balance complexity and performance, offering accurate predictions without the extensive data requirements of ANNs. Accurate load forecasting is vital for MDU’s substation to ensure efficient energy distribution, maintenance scheduling, and budget planning. Implementing an SVM-based forecasting model can lead to better resource allocation and operational efficiency, ultimately supporting the university’s energy management objectives.

Keywords: Load forecasting, neural networks, support vector machines, MDU

How to cite this article:
Dheeraj singh, Neha Khurana, Pradeep Singla. Support Vector Machine Inspired Load Forecasting of a State University in Haryana. Trends in Electrical Engineering. 2025; 15(02):-.
How to cite this URL:
Dheeraj singh, Neha Khurana, Pradeep Singla. Support Vector Machine Inspired Load Forecasting of a State University in Haryana. Trends in Electrical Engineering. 2025; 15(02):-. Available from: https://journals.stmjournals.com/tee/article=2025/view=0


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Ahead of Print Subscription Review Article
Volume 15
02
Received 07/05/2025
Accepted 09/06/2025
Published 10/06/2025
Publication Time 34 Days
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