Energy Load Forecasting Techniques in Smart Grids: A Cross-Country Comparative Analysis

Rachida Hachache (), Mourad Labrahmi, António Grilo, Abdelaali Chaoub, Rachid Bennani, Ahmed Tamtaoui and Brahim Lakssir
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Rachida Hachache: Moroccan Foundation for Advanced Science, Innovation and Research (MAScIR), Ben Guerir 43150, Morocco
Mourad Labrahmi: STRS Laboratory, Institut National des Postes et Telecommunications (INPT), Rabat 10112, Morocco
António Grilo: INESC-ID Lisboa, IST-Universidade de Lisboa, 1000-100 Lisboa, Portugal
Abdelaali Chaoub: STRS Laboratory, Institut National des Postes et Telecommunications (INPT), Rabat 10112, Morocco
Rachid Bennani: Moroccan Foundation for Advanced Science, Innovation and Research (MAScIR), Ben Guerir 43150, Morocco
Ahmed Tamtaoui: STRS Laboratory, Institut National des Postes et Telecommunications (INPT), Rabat 10112, Morocco
Brahim Lakssir: Moroccan Foundation for Advanced Science, Innovation and Research (MAScIR), Ben Guerir 43150, Morocco

Energies, 2024, vol. 17, issue 10, 1-22

Abstract: Energy management systems allow the Smart Grids industry to track, improve, and regulate energy use. Particularly, demand-side management is regarded as a crucial component of the entire Smart Grids system. Therefore, by aligning utility offers with customer demand, anticipating future energy demands is essential for regulating consumption. An updated examination of several forecasting techniques for projecting energy short-term load forecasts is provided in this article. Each class of algorithms, including statistical techniques, Machine Learning, Deep Learning, and hybrid combinations, are comparatively evaluated and critically analyzed, based on three real consumption datasets from Spain, Germany, and the United States of America. To increase the size of tiny training datasets, this paper also proposes a data augmentation technique based on Generative Adversarial Networks. The results show that the Deep Learning-hybrid model is more accurate than traditional statistical methods and basic Machine Learning procedures. In the same direction, it is demonstrated that more comprehensive datasets assisted by complementary data, such as energy generation and weather, may significantly boost the accuracy of the models. Additionally, it is also demonstrated that Generative Adversarial Networks-based data augmentation may greatly improve algorithm accuracy.

Keywords: deep learning; energy consumption; load forecasting; machine learning; smart grids (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: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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