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Short Term Load Forcasting Using Heuristic Algorithm and Support Vector Machine

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Complex, Intelligent, and Software Intensive Systems (CISIS 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 772))

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Abstract

Analysis of data is very important for accurate prediction. Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) is used for load forcasting. Features are selected using PSO and redundant features are removed. Data is divided into training and testing data. Load forecasting is done by using SVM classifier. However, SVM classifier predicts short term load accurately and efficiently. Multiple time testing is done on data for checking accuracy of PSO. SVM shows efficient performance as compared to Principle Component Analysis (PCA).

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Author information

Authors and Affiliations

  1. Abasyn University Department of Computing and Technology, Islamabad, 44000, Pakistan

    Orooj Nazeer & Abdul Basit Majeed Khan

  2. COMSATS Institute of Information Technology, Islamabad, 44000, Pakistan

    Nadeem Javaid

  3. Islamic International University, Islamabad, 44000, Pakistan

    Arif Hussain

  4. The University of Lahore, Gujrat Campus, Gujranwala, 10240, Pakistan

    Tariq Basheer

  5. Mirpur University of Science and Technology, Azad Kashmir, 10250, Pakistan

    Muhammad Mukhtar Ahmed Ratyal

Authors
  1. Orooj Nazeer
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  2. Nadeem Javaid
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  3. Abdul Basit Majeed Khan
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  4. Arif Hussain
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  5. Tariq Basheer
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  6. Muhammad Mukhtar Ahmed Ratyal
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Corresponding author

Correspondence to Nadeem Javaid .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology , Fukuoka, Japan

    Leonard Barolli

  2. Department of Computer Science, COMSATS Institute of Information Technology , Islamabad, Pakistan

    Nadeem Javaid

  3. Department of Information and Communication Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology , Fukuoka, Japan

    Makoto Ikeda

  4. Department of Advanced Sciences, Hosei University , Tokyo, Japan

    Makoto Takizawa

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Nazeer, O., Javaid, N., Khan, A.B.M., Hussain, A., Basheer, T., Ratyal, M.M.A. (2019). Short Term Load Forcasting Using Heuristic Algorithm and Support Vector Machine. In: Barolli, L., Javaid, N., Ikeda, M., Takizawa, M. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2018. Advances in Intelligent Systems and Computing, vol 772. Springer, Cham. https://doi.org/10.1007/978-3-319-93659-8_72

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