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Accurate prediction of user consumption is a key part not only in understanding consumer flexibility and behavior patterns, but in the design of robust and efficient energy saving programs as well. Existing prediction methods usually have high relative errors that can be larger than 30% and have difficulties accounting for heterogeneity between individual users. In this paper, we propose a method...
While adding new capabilities, the distributed energy resource proliferation raises great concern about challenges such as dynamic fluctuations of voltages. For example, in a volatile setting with highly uncertain renewable generation and customer consumption, it is challenging to provide reliable power and voltage prediction for operational planning purposes to mitigate risks, e.g., over-voltages...
Understanding consumer flexibility and behavior patterns is becoming increasingly vital to the design of robust and efficient energy saving programs. Accurate prediction of consumption is a key part to this understanding. Existing prediction methods usually have high relative errors that can be larger than 30%. In this paper, we explore sparsity in users' past data and relationship between different...
Successful enrollment of customers in intervention-based demand side energy management (DSM) programs, such as energy efficiency and installation of PV panels, depends on having accurate estimates of the benefits of these programs available and communicated to the customers. The program benefits may include long-term financial savings, and their contribution to managing supply and demand in transition...
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