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Data-Driven Load Forecasting Using Machine Learning and Meteorological Data

Aishah Alrashidi, Ali Mustafa Qamar*

Department of Computer Science, College of Computer, Qassim University, Buraydah, Saudi Arabia

* Corresponding Author: Ali Mustafa Qamar. Email: email

Computer Systems Science and Engineering 2023, 44(3), 1973-1988. https://doi.org/10.32604/csse.2023.024633

Abstract

Electrical load forecasting is very crucial for electrical power systems’ planning and operation. Both electrical buildings’ load demand and meteorological datasets may contain hidden patterns that are required to be investigated and studied to show their potential impact on load forecasting. The meteorological data are analyzed in this study through different data mining techniques aiming to predict the electrical load demand of a factory located in Riyadh, Saudi Arabia. The factory load and meteorological data used in this study are recorded hourly between 2016 and 2017. These data are provided by King Abdullah City for Atomic and Renewable Energy and Saudi Electricity Company at a site located in Riyadh. After applying the data pre-processing techniques to prepare the data, different machine learning algorithms, namely Artificial Neural Network and Support Vector Regression (SVR), are applied and compared to predict the factory load. In addition, for the sake of selecting the optimal set of features, 13 different combinations of features are investigated in this study. The outcomes of this study emphasize selecting the optimal set of features as more features may add complexity to the learning process. Finally, the SVR algorithm with six features provides the most accurate prediction values to predict the factory load.

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Cite This Article

A. Alrashidi and A. M. Qamar, "Data-driven load forecasting using machine learning and meteorological data," Computer Systems Science and Engineering, vol. 44, no.3, pp. 1973–1988, 2023.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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