В роботі проаналізовано основні особливості оброблення великих обсягів даних в промислових smart grid системах. Визначено переваги розподілених обчислень для ефективнішого аналізу інформації. Досліджено рекомендаційні алгоритми, що дозволяють обробляти великі дані швидше та точніше. Запропоновано метод розподіленої матричної факторизації для надання рекомендацій користувачам smart grid систем, що передбачає обмін публічними даними між пристроями, обробляючи приватні локально. Визначено переваги запропонованої розподіленої рекомендаційної моделі, зокрема гнучкість при зміні параметрів, краща точність обчислень внаслідок взаємообміну результатами між вузлами, висока швидкість оброблення даних, а також масштабованість.
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