Probabilistic Aggregated Load Forecasting with Fine-grained Smart Meter Data

Author(s):  
Yi Wang ◽  
Leandro Von Krannichfeldt ◽  
Gabriela Hug
2020 ◽  
pp. 271-285
Author(s):  
Yi Wang ◽  
Qixin Chen ◽  
Chongqing Kang

Author(s):  
Joao Viana ◽  
Ricardo J. Bessa ◽  
Joao Sousa
Keyword(s):  

2015 ◽  
Vol 6 (2) ◽  
pp. 911-918 ◽  
Author(s):  
Franklin L. Quilumba ◽  
Wei-Jen Lee ◽  
Heng Huang ◽  
David Y. Wang ◽  
Robert L. Szabados

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4751 ◽  
Author(s):  
Shaohao Xie ◽  
Fangguo Zhang ◽  
Huizhi Lin ◽  
Yangtong Tian

The smart meter is one of the most important components of the smart grid, which enables bi-directional communication between electric power providers and in-home appliances. However, the fine-grained metering mechanism that reports real-time electricity usage to the provider may result in some privacy and security issues for the owner of the smart meter. In this paper, we propose a new secure and anonymous smart metering scheme based on the technique of direct anonymous attestation and identity-based signatures. We utilize the trusted platform module to realize the tamper resistance of the smart meter. Moreover, our scheme is able to detect malfunctioning meters in which data is reported more than once in a time period. Finally, the performance and security results show that our proposed scheme is efficient and satisfies the security requirements of the smart grid communication system.


Author(s):  
Ajay Kumar ◽  
Parveen Poon Terang ◽  
Vikram Bali

Electrical load forecasting is an essential feature in power systems planning, operation and control. The non-linearity and non-stationary nature of the data, however, poses a challenge in terms of accuracy. This article explores a deep learning technique, a long short-term memory recurrent neural network-based framework to tackle this tricky issue. The proposed machine learning model framework is tested on real time residential smart meter data showing promising results. A web application has also been developed to allow consumers to have access to greater levels of information and facilitate decision-making at their end. The performance of the proposed model is also comprehensively compared to other methods in the field of load forecasting showing more accurate results for the function of forecasting of load on short term basis.


2021 ◽  
pp. 635-643
Author(s):  
A. L. Amutha ◽  
R. Annie Uthra ◽  
J. Preetha Roselyn ◽  
R. Golda Brunet

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