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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.
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.
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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.
The absolute percentage error (APE) is calculated after fitting the historical hourly load for each hourly load in training set.
Non-PEV loads are modeled using 15-minute metered historical ERCOT load data from the year 2005, obtained from the Public Utility Commission of Texas.
Historical hourly load duration curve was considered (8760 h).
2) Historical hourly load duration curve was considered (8760 h). 3) The unit cost of energy is assumed to be 80 $/MWh.
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