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Historical prices from the financial market, weekly price/load information, historical loads and day type are chosen as the explanatory factors.
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That is a heavy historical load for an all but forgotten figure.
First, there are no clear seasonality trends or pattern over the historical load data.
Using accumulated historical load data, the K-means clustering method is applied to obtain typical scenarios.
WT is applied to historical load data to remove the spikes and fluctuations.
For the conventional method, the historical impact factor data include the historical load profile of each day and the historical fuel price of each day.
Usually, the inputs correspond to historical load values, exogenous variables like temperature, day type identification codes and others.
First, we want to point out that our approach is based on a long-term prediction model that relies on historical load patterns.
Due to lack of historical load data, the data of ACL on statutory holidays (e.g., the Spring Festivals, the National Day, etc).
The in-plane stiffness of flat roofs plays crucial role in the structural stability and safety of historical load bearing masonry buildings under lateral hazardous loads.
We use Convolutional Neural Network components to extract rich features from historical load sequence and use Recurrent Components to model the implicit dynamics.
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CEO of Professional Science Editing for Scientists @ prosciediting.com