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Day-Ahead Load Demand Forecasting in Urban Community Cluster Microgrids Using Machine Learning Methods

Author

Listed:
  • Sivakavi Naga Venkata Bramareswara Rao

    (Department of Electrical and Electronics Engineering, Sir C. R. Reddy College of Engineering, Eluru 534007, India)

  • Venkata Pavan Kumar Yellapragada

    (School of Electronics Engineering, VIT-AP University, Amaravati 522237, India)

  • Kottala Padma

    (Department of Electrical Engineering, Andhra University College of Engineering (A), Visakhapatnam 530003, India)

  • Darsy John Pradeep

    (School of Electronics Engineering, VIT-AP University, Amaravati 522237, India)

  • Challa Pradeep Reddy

    (School of Computer Science and Engineering, VIT-AP University, Amaravati 522237, India)

  • Mohammad Amir

    (Department of Electrical Engineering, Faculty of Engineering and Technology, Jamia Millia Islamia Central University, Delhi 243601, India)

  • Shady S. Refaat

    (Department of Electrical Engineering, Texas A&M University, Doha P.O. Box 23874, Qatar
    School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK)

Abstract

The modern-day urban energy sector possesses the integrated operation of various microgrids located in a vicinity, named cluster microgrids, which helps to reduce the utility grid burden. However, these cluster microgrids require a precise electric load projection to manage the operations, as the integrated operation of multiple microgrids leads to dynamic load demand. Thus, load forecasting is a complicated operation that requires more than statistical methods. There are different machine learning methods available in the literature that are applied to single microgrid cases. In this line, the cluster microgrids concept is a new application, which is very limitedly discussed in the literature. Thus, to identify the best load forecasting method in cluster microgrids, this article implements a variety of machine learning algorithms, including linear regression (quadratic), support vector machines, long short-term memory, and artificial neural networks (ANN) to forecast the load demand in the short term. The effectiveness of these methods is analyzed by computing various factors such as root mean square error, R-square, mean square error, mean absolute error, mean absolute percentage error, and time of computation. From this, it is observed that the ANN provides effective forecasting results. In addition, three distinct optimization techniques are used to find the optimum ANN training algorithm: Levenberg–Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient. The effectiveness of these optimization algorithms is verified in terms of training, test, validation, and error analysis. The proposed system simulation is carried out using the MATLAB/Simulink-2021a ® software. From the results, it is found that the Levenberg–Marquardt optimization algorithm-based ANN model gives the best electrical load forecasting results.

Suggested Citation

  • Sivakavi Naga Venkata Bramareswara Rao & Venkata Pavan Kumar Yellapragada & Kottala Padma & Darsy John Pradeep & Challa Pradeep Reddy & Mohammad Amir & Shady S. Refaat, 2022. "Day-Ahead Load Demand Forecasting in Urban Community Cluster Microgrids Using Machine Learning Methods," Energies, MDPI, vol. 15(17), pages 1-25, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6124-:d:895783
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    References listed on IDEAS

    as
    1. Sivakavi Naga Venkata Bramareswara Rao & Yellapragada Venkata Pavan Kumar & Darsy John Pradeep & Challa Pradeep Reddy & Aymen Flah & Habib Kraiem & Jawad F. Al-Asad, 2022. "Power Quality Improvement in Renewable-Energy-Based Microgrid Clusters Using Fuzzy Space Vector PWM Controlled Inverter," Sustainability, MDPI, vol. 14(8), pages 1-20, April.
    2. Athanasios Ioannis Arvanitidis & Dimitrios Bargiotas & Aspassia Daskalopulu & Vasileios M. Laitsos & Lefteri H. Tsoukalas, 2021. "Enhanced Short-Term Load Forecasting Using Artificial Neural Networks," Energies, MDPI, vol. 14(22), pages 1-14, November.
    3. Jaime Buitrago & Shihab Asfour, 2017. "Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Vector Inputs," Energies, MDPI, vol. 10(1), pages 1-24, January.
    4. Md Jamal Ahmed Shohan & Md Omar Faruque & Simon Y. Foo, 2022. "Forecasting of Electric Load Using a Hybrid LSTM-Neural Prophet Model," Energies, MDPI, vol. 15(6), pages 1-18, March.
    5. Andrea Maria N. C. Ribeiro & Pedro Rafael X. do Carmo & Patricia Takako Endo & Pierangelo Rosati & Theo Lynn, 2022. "Short- and Very Short-Term Firm-Level Load Forecasting for Warehouses: A Comparison of Machine Learning and Deep Learning Models," Energies, MDPI, vol. 15(3), pages 1-24, January.
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