mean_demand_field_pdf.pdf

其實在求出原始和變換後變數的機率分布模型後，應該用MLE去求變數變換前變數的參數$\mu_0$以及變數變換後的共變矩陣$\Sigma$，因為最大亂度部分只應用「變數變換前的平均值以及變數變換後的共變數很重要」這個資訊才是最合理的設定（減少先驗假設）。

Visual Demonstration of the Example

https://www.youtube.com/watch?v=LqjNpgYwPmo

Codes for Producing the Example

trEnD/src/spatial_field at main · TonyYenTWN/trEnD

Data Sources of the Example

3 datasets were essential to infer the electricity demand density field.

  1. Population density field for Norway can be found in the link below

    Norway - Population Density

  2. Electricity demand data for each bidding zone can be found from ENTSO-E transparency platform.

    Data

  3. In addition, one would also need to know the actual region of each bidding zone. The NVE “Nett Konsesjonsområde” (Network Licensing area) dataset contains information of 183 distribution level grids, and was used to map the region of each bidding zone (by assigning each distribution level grid to a bidding zone).

    NVE data nedlast