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In smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analysis to recent machine learning approach and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management including individual load forecasting is becoming critical. In this paper, we propose deep neural network (DNN) based...
We consider the problem of power demand forecasting in residential micro-grids. Previous approaches rely on ARMA models and, recently, on neural network architectures that are however solely used to perform one-step ahead predictions. Here, we propose an original forecasting technique based on non-linear, autoregressive (NAR) neural networks. Our architecture allows for parallel and efficient training...
Electric load profile forecasting is among the most important tasks in power system operation. This task has been performed reasonably well with minimal disruption from renewables over the past decades. The expected high penetration of renewables in the system is challenging this classical task. Moreover, the blossom of demand side management programs warrants the necessity of load profile forecasting...
Grey system forecasting theory model and nonlinear autoregressive (NAR) neural network model for forecasting the number of electric vehicles (EVs) in the city of Shenzhen are established in this paper separately. The number of EVs from 2006 to 2015 was used as the raw data in two models. The effectiveness of the two models are evaluated by various criteria. Afterward, the rationality, precision and...
Loss of system integrity is a critical situation where the electric utility ceases to have control over the level of frequency and voltage in the sub-networks (which is also termed as system islands). In the smart grid environment, operation statuses are timely exposed leveraging SCADA and WAM systems. However, in facing with large amount of data and information, it is challenging for system operators...
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