Decoupling Weather Influence from User Habits for an Optimal Electric Load Forecast System
AbstractThe balance between production and consumption in a smart grid with high penetration of renewable sources and in the presence of energy storage systems benefits from an accurate load prediction. A general approach to load forecasting is not possible because of the additional complication due to the increasing presence of distributed and usually unmeasured photovoltaic production. Various methods are proposed in the literature that can be classified into two classes: those that predict by separating the portion of load due to consumption habits from the part of production due to local weather conditions, and those that attempt to predict the load as a whole. The characteristic that should lead to a preference for one approach over another is obviously the percentage of penetration of distributed production. The study site discussed in this document is the grid of Borkum, an island located in the North Sea. The advantages in terms of reducing forecasting errors for the electrical load, which can be obtained by using weather information, are explained. In particular, when comparing the results of different approaches gradually introducing weather forecasts, it is clear that the correct functional dependency of production has to be taken into account in order to obtain maximum yield from the available information. Where possible, this approach can significantly improve the quality of the forecasts, which in turn can improve the balance of a network—especially if energy storage systems are in place. View Full-Text
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Massidda, L.; Marrocu, M. Decoupling Weather Influence from User Habits for an Optimal Electric Load Forecast System. Energies 2017, 10, 2171.
Massidda L, Marrocu M. Decoupling Weather Influence from User Habits for an Optimal Electric Load Forecast System. Energies. 2017; 10(12):2171.Chicago/Turabian Style
Massidda, Luca; Marrocu, Marino. 2017. "Decoupling Weather Influence from User Habits for an Optimal Electric Load Forecast System." Energies 10, no. 12: 2171.
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