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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...
One of the critical requirements in power grid operation and planning is the ability to accurately forecast expected load. This allows for a heightened enhancement in grid operations, energy management, and planning. Load forecasting is historically based on aggregated spatial and temporal consumption data; with the deployment of Advanced Metering Infrastructure (AMI) systems, it can be achieved not...
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...
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