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As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation...
The performance of prediction models is often based on “abstract metrics” that estimate the model’s ability to limit residual errors between the observed and predicted values. However, meaningful evaluation and selection of prediction models for end-user domains requires holistic and application-sensitive performance measures. Inspired by energy consumption prediction models used in the emerging “big...
Scalable stream processing and continuous dataflow systems are gaining traction with the rise of big data due to the need for processing high velocity data in near real time. Unlike batch processing systems such as MapReduce and workflows, static scheduling strategies fall short for continuous data flows due to the variations in the input data rates and the need for sustained throughput. The elastic...
The management of reservoir simulation models has been an important need of engineers in petroleum industry. However, due to data sharing among reservoir simulation models, data replication is common and poses many challenges to model management, including management efficiency and data consistency. In this paper, we propose a data component based methodology to manage reservoir simulation models...
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