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

Sivakavi Naga Venkata Bramareswara Rao, Venkata Pavan Kumar Yellapragada (), Kottala Padma, Darsy John Pradeep, Challa Pradeep Reddy, Mohammad Amir and Shady S. Refaat ()
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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

Energies, 2022, vol. 15, issue 17, 1-25

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.

Keywords: ANN training algorithms; cluster microgrids; load demand forecasting; machine learning methods; urban energy community (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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