code

Source code for the smart meters experiments of the paper Hierarchical Probabilistic Forecasting of Electricity Demand with Smart Meter Data by Ben Taieb, Souhaib, Taylor, James, and Hyndman, Rob.

The code computes all the results in Section 6, and produces all the figures in the paper.

Usage

1. Downnload the following files: edrp_elec.csv, edrp_metadata.xlsx and edrp_geography_data.xlsx from https://discover.ukdataservice.ac.uk/catalogue/?sn=7591 (registration required).
2. Save these files in "../rawdata"
3. Apply a preprocessing step and save the results in new Rdata files (slow).

source("init_1_raw_meters.R")
source("init_2_myHierarchy.R")
source("init_3_bottom_meters.R")
source("init_4_aggregated_meters.R")

3. Specify the scenario for the bottom-level forecasts (see run_basef.sh) and run the following script (slow):

./run_basef.sh

3. Specify the scenario for the aggregate-level forecasts (see run_basef.sh) and run the script again (slow):

./run_basef.sh

4. Compute the covariance matrix W for the MinT method.

source("MinT_ecov.R")

5. Compute the permutations (empirical copulas).

source("permutations.R")

6. Merge the results by half hour.

source("makebf_byhalfhour.R")

7. Compute the forecasts for the aggregated series (slow).

./run_aggregation.sh

8. Merge the aggregation results.

source("aggregation_merge.R")

9. Produce the figures with all the accuracy measures.

source("results.R")

To produce all the Figures, run the following scripts:

source("plot_calendar_effects.R")
source("plot_coherency.R")
source("plot_coverage.R")
source("plot_forecasts.R")
source("plot_parameters.R")
source("plot_series.R")
source("plot_tree.R")