This paper tackles a new challenge in power big data: how to improve the precision of short-term load forecasting with large-scale data set. The proposed load forecasting method is based on Spark platform and "cl...
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This paper tackles a new challenge in power big data: how to improve the precision of short-term load forecasting with large-scale data set. The proposed load forecasting method is based on Spark platform and "clustering-regression" model, which is implemented by Apache Spark machine learning library (MLlib). Proposed scheme firstly clustering the users with different electrical attributes and then obtains the "load characteristic curve of each cluster", which represents the features of various types of users and is considered as the properties of a regional total load. Furthermore, the "clustering-regression" model is used to forecast the power load of the certain region. Extensive experiments show that the proposed scheme can predict reasonably the short-term power load and has excellent robustness. Comparing with the single-alone model, the proposed method has a higher efficiency in dealing with large-scale data set and can be effectively applied to the power load forecasting.
This paper tackles a new challenge in high precision load forecasting of larger consumer with the background of electric power big data. The proposed short-term power load forecasting method is based on TensorFlow dee...
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ISBN:
(纸本)9781538685495
This paper tackles a new challenge in high precision load forecasting of larger consumer with the background of electric power big data. The proposed short-term power load forecasting method is based on TensorFlow deep learning framwork and clustering-regression model. Proposed scheme firstly clustering the users with different electrical attributes and then obtains the "load curve of each cluster", which is considered as the properties of a regional total load, and represents the features of various types of consumers. Furthermore, the "clustering-regression" model is used to forecast the power load of the certain region, which is implemented by TensorFlow deep learning framework. Extensive experiments show that the proposed scheme can predict reasonably the short-term power load and has excellent robustness. Comparing with the traditional model, the proposed method has a higher efficiency in dealing with large-scale data set and can be effectively applied to the power load forecasting.
This paper tackles a new challenge in high precision load forecasting of larger consumer with the background of electric power big data. The proposed shortterm power load forecasting method is based on TensorFlow deep...
详细信息
This paper tackles a new challenge in high precision load forecasting of larger consumer with the background of electric power big data. The proposed shortterm power load forecasting method is based on TensorFlow deep learning framwork and clustering-regression model. Proposed scheme firstly clustering the users with different electrical attributes and then obtains the "load curve of each cluster", which is considered as the properties of a regional total load, and represents the features of various types of consumers. Furthermore, the "clustering-regression" model is used to forecast the power load of the certain region, which is implemented by TensorFlow deep learning framework. Extensive experiments show that the proposed scheme can predict reasonably the short-term power load and has excellent robustness. Comparing with the traditional model, the proposed method has a higher efficiency in dealing with large-scale data set and can be effectively applied to the power load forecasting.
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