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Ashoorzadeh, Alieh, Toloie Eshlaghy, Abbas, Afshar Kazemi, Mohammad Ali. (1403). Electrical Load Forecasting Using a Hybrid Large Margin Nearest Neighbor Method. هنرهای کاربردی, 4(1), 37-49. doi: 10.22075/mseee.2025.35788.1184
Alieh Ashoorzadeh; Abbas Toloie Eshlaghy; Mohammad Ali Afshar Kazemi. "Electrical Load Forecasting Using a Hybrid Large Margin Nearest Neighbor Method". هنرهای کاربردی, 4, 1, 1403, 37-49. doi: 10.22075/mseee.2025.35788.1184
Ashoorzadeh, Alieh, Toloie Eshlaghy, Abbas, Afshar Kazemi, Mohammad Ali. (1403). 'Electrical Load Forecasting Using a Hybrid Large Margin Nearest Neighbor Method', هنرهای کاربردی, 4(1), pp. 37-49. doi: 10.22075/mseee.2025.35788.1184
Ashoorzadeh, Alieh, Toloie Eshlaghy, Abbas, Afshar Kazemi, Mohammad Ali. Electrical Load Forecasting Using a Hybrid Large Margin Nearest Neighbor Method. هنرهای کاربردی, 1403; 4(1): 37-49. doi: 10.22075/mseee.2025.35788.1184

Electrical Load Forecasting Using a Hybrid Large Margin Nearest Neighbor Method

Journal of Modeling and Simulation in Electrical and Electronics Engineering
دوره 4، شماره 1 - شماره پیاپی 15، مرداد 2024، صفحه 37-49    اصل مقاله (800.48 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22075/mseee.2025.35788.1184
نویسندگان
Alieh Ashoorzadeh1؛ Abbas Toloie Eshlaghy* 2؛ Mohammad Ali Afshar Kazemi3
1Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.
3Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
تاریخ دریافت: 12 آبان 1403،  تاریخ بازنگری: 17 دی 1403،  تاریخ پذیرش: 05 اسفند 1403 
چکیده
Load forecasting is a key component of electric utility operations and planning. Because of today's highly developed electricity markets and rapidly growing power systems, load forecasting is becoming an essential part of power system operation scheduling. This paper proposes a new short-term load forecasting model based on the large margin nearest neighbor (LMNN) classification algorithm to improve prediction accuracy. The accuracy of many classification methods, such as k-nearest neighbor (k-NN), is significantly influenced by the technique used to calculate sample distances. The Mahalanobis distance is one of the most widely used methods for calculating distance. Numerous techniques have been used to enhance k-NN performance in recent years, including LMNN. Our proposed approach aims to solve the local optimum problem of LMNN, compute data similarities, and optimize the cost function that establishes the distances between instances. Before using gradient descent to determine the ideal parameter values for the cost function, we employ a genetic algorithm to shrink the size of the solution space. Additionally, our method's forecasting errors are contrasted with those of the BPNN and ARMA models. The comparative findings show how well the recommended forecasting model performs in short-term load forecasting.
کلیدواژه‌ها
Short-Term Load Forecasting؛ Large Margin Nearest Neighbor؛ Distance learning؛ Genetic Algorithm
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