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دسته بندی:
اینترنت اشیاء - Internet of Things
سال انتشار:
2022
عنوان انگلیسی مقاله:
Federated learning with hyperparameter-based clustering for electrical load forecasting
ترجمه فارسی عنوان مقاله:
یادگیری فدرال با خوشهبندی مبتنی بر فراپارامتر برای پیشبینی بار الکتریکی
منبع:
ScienceDirect- Elsevier- Internet of Things, 17 (2022) 100470: doi:10:1016/j:iot:2021:100470
نویسنده:
Nastaran Gholizadeh
چکیده انگلیسی:
Electrical load prediction has become an integral part of power system operation. Deep learning
models have found popularity for this purpose. However, to achieve a desired prediction
accuracy, they require huge amounts of data for training. Sharing electricity consumption data
of individual households for load prediction may compromise user privacy and can be expensive
in terms of communication resources. Therefore, edge computing methods, such as federated
learning, are gaining more importance for this purpose. These methods can take advantage of
the data without centrally storing it. This paper evaluates the performance of federated learning
for short-term forecasting of individual house loads as well as the aggregate load. It discusses the
advantages and disadvantages of this method by comparing it to centralized and local learning
schemes. Moreover, a new client clustering method is proposed to reduce the convergence time
of federated learning. The results show that federated learning has a good performance with a
minimum root mean squared error (RMSE) of 0.117 kWh for individual load forecasting.
Keywords: Federated learning | Electricity load forecasting | Edge computing | LSTM | Decentralized learning
قیمت: رایگان
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