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Merge pull request #208 from openclimatefix/NWP_patch
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fix 60 min hardcode NWP resolution in multimodal.py
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@AUdaltsova
AUdaltsova authored Jun 7, 2024
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212 changes: 42 additions & 170 deletions README.md
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# PVNet 2.1

[![test-release](https://github.com/openclimatefix/PVNet/actions/workflows/test-release.yml/badge.svg)](https://github.com/openclimatefix/PVNet/actions/workflows/test-release.yml)
[![Python Bump Version & release](https://github.com/openclimatefix/PVNet/actions/workflows/release.yml/badge.svg)](https://github.com/openclimatefix/PVNet/actions/workflows/release.yml)

This project is used for training PVNet and running PVnet on live data.
This project is used for training PVNet and running PVNet on live data.

PVNet2 is a multi-modal late-fusion model that largely inherits the same architecture from
[PVNet1.0](https://github.com/openclimatefix/predict_pv_yield). The NWP and
[PVNet1.0](https://github.com/openclimatefix/predict_pv_yield). The NWP (Numerical Weather Prediction) and
satellite data are sent through some neural network which encodes them down to
1D intermediate representations. These are concatenated together with the GSP
1D intermediate representations. These are concatenated together with the GSP (Grid Supply Point)
output history, the calculated solar coordinates (azimuth and elevation) and the
GSP ID which has been put through an embedding layer. This 1D concatenated
feature vector is put through an output network which outputs predictions of the
Expand Down Expand Up @@ -56,7 +56,7 @@ pip install ".[dev]"

## Getting started with running PVNet

Before running any code in within PVNet, copy the example configuration to a
Before running any code in PVNet, copy the example configuration to a
configs directory:

```
Expand All @@ -74,14 +74,14 @@ suggested locations for downloading such datasets below:

**GSP (Grid Supply Point)** - Regional PV generation data\
The University of Sheffield provides API access to download this data:
https://www.solar.sheffield.ac.uk/pvlive/api/
https://www.solar.sheffield.ac.uk/api/

Documentation for querying generation data aggregated by GSP region can be found
here:
https://docs.google.com/document/d/e/2PACX-1vSDFb-6dJ2kIFZnsl-pBQvcH4inNQCA4lYL9cwo80bEHQeTK8fONLOgDf6Wm4ze_fxonqK3EVBVoAIz/pub#h.9d97iox3wzmd

**NWP (Numerical weather predictions)**\
OCF maintains a Zarr formatted version the German Weather Service's (DWD)
OCF maintains a Zarr formatted version of the German Weather Service's (DWD)
ICON-EU NWP model here:
https://huggingface.co/datasets/openclimatefix/dwd-icon-eu which includes the UK

Expand Down Expand Up @@ -121,212 +121,84 @@ cp -r configs.example configs

### Set up and config example for batch creation

We will use the example of creating batches using data from gcp:
`/PVNet/configs/datamodule/configuration/gcp_configuration.yaml`
Ensure that the file paths are set to the correct locations in
`gcp_configuration.yaml`.
We will use the following example config file for creating batches: `/PVNet/configs/datamodule/configuration/example_configuration.yaml`. Ensure that the file paths are set to the correct locations in `example_configuration.yaml`: search for `PLACEHOLDER` to find where to input the location of the files. You will need to comment out or delete the parts of `example_configuration.yaml` pertaining to the data you are not using.

`PLACEHOLDER` is used to indcate where to input the location of the files.

For OCF use cases, file locations can be found in `template_configuration.yaml` located alongside `gcp_configuration.yaml`.
When creating batches, an additional datamodule config located in `PVNet/configs/datamodule` is passed into the batch creation script: `streamed_batches.yaml`. Like before, a placeholder variable is used when specifying which configuration to use:

In these configurations you can update the train, val & test periods to cover the data you have access to.


With your configuration in place, you can proceed to create batches. PVNet uses
[hydra](https://hydra.cc/) which enables us to pass variables via the command
line that will override the configuration defined in the `./configs` directory.

When creating batches, an additional config is used which is passed into the batch creation script. This is the datamodule config located `PVNet/configs/datamodule`.

For this example we will be using the `streamed_batches.yaml` config. Like before, a placeholder variable is used when specifing which configuration to use:

`configuration: "PLACEHOLDER.yaml"`
```yaml
configuration: "PLACEHOLDER.yaml"
```
This should be given the whole path to the config on your local machine, such as for our example it should be changed to:
This should be given the whole path to the config on your local machine, for example:
`configuration: "/FULL-PATH-TO-REPO/PVNet/configs/datamodule/configuration/gcp_configuration.yaml"`
`
```yaml
configuration: "/FULL-PATH-TO-REPO/PVNet/configs/datamodule/configuration/example_configuration.yaml"
```
Where `FULL-PATH-TO-REPO` represent the whole path to the PVNet repo on your local machine.

This is also where you can update the train, val & test periods to cover the data you have access to.

### Running the batch creation script

Run the save_batches.py script to create batches if setting parameters in the datamodule config (`streamed_batches.yaml` in this example):
Run the `save_batches.py` script to create batches with the parameters specified in the datamodule config (`streamed_batches.yaml` in this example):

```
```bash
python scripts/save_batches.py
```
or with the following example arguments to override config:
PVNet uses
[hydra](https://hydra.cc/) which enables us to pass variables via the command
line that will override the configuration defined in the `./configs` directory, like this:

```
```bash
python scripts/save_batches.py datamodule=streamed_batches datamodule.batch_output_dir="./output" datamodule.num_train_batches=10 datamodule.num_val_batches=5
```

In this function the datamodule argument looks for a config under `PVNet/configs/datamodule`. The examples here are either to use "premade_batches" or "streamed_batches".

Its important that the dates set for the training, validation and testing in the datamodule (`streamed_batches.yaml`) config are within the ranges of the dates set for the input features in the configuration (`gcp_configuration.yaml`).
`scripts/save_batches.py` needs a config under `PVNet/configs/datamodule`. You can adapt `streamed_batches.yaml` or create your own in the same folder.

If downloading private data from a gcp bucket make sure to authenticate gcloud (the public satellite data does not need authentication):
If downloading private data from a GCP bucket make sure to authenticate gcloud (the public satellite data does not need authentication):

```
gcloud auth login
```

For files stored in multiple locations they can be added as list. For example from the gcp_configuration.yaml file we can change from satellite data stored on a bucket:
Files stored in multiple locations can be added as a list. For example, in the `example_configuration.yaml` file we can supply a path to satellite data stored on a bucket:

```yaml
satellite:
satellite_zarr_path: gs://solar-pv-nowcasting-data/satellite/EUMETSAT/SEVIRI_RSS/v4/2020_nonhrv.zarr
```

To satellite data hosted by Google:
Or to satellite data hosted by Google:

```yaml
satellite:
satellite_zarr_paths:
- "gs://public-datasets-eumetsat-solar-forecasting/satellite/EUMETSAT/SEVIRI_RSS/v4/2020_nonhrv.zarr"
- "gs://public-datasets-eumetsat-solar-forecasting/satellite/EUMETSAT/SEVIRI_RSS/v4/2021_nonhrv.zarr"
```
Datapipes is currently set up to use 11 channels from the satellite data, the 12th of which is HRV and is not included in these.

Datapipes are currently set up to use 11 channels from the satellite data, the 12th of which is HRV and is not included in these.


### Training PVNet

How PVNet is run is determined by the extensive configuration in the config
files. The following configs have been tested to work using batches of data
created using the steps and batch creation config mentioned above.
You should create the following configs before trying to train a model locally,
as so:
In `configs/datamodule/local_premade_batches.yaml`:

```yaml
_target_: pvnet.data.datamodule.DataModule
configuration: null
batch_dir: "./output" # where the batches are saved
num_workers: 20
prefetch_factor: 2
batch_size: 8
```

In `configs/model/local_multimodal.yaml`:
files. The configs stored in `PVNet/configs.example` should work with batches created using the steps and batch creation config mentioned above.

```yaml
_target_: pvnet.models.multimodal.multimodal.Model
output_quantiles: [0.02, 0.1, 0.25, 0.5, 0.75, 0.9, 0.98]
#--------------------------------------------
# NWP encoder
#--------------------------------------------
nwp_encoders_dict:
ukv:
_target_: pvnet.models.multimodal.encoders.encoders3d.DefaultPVNet
_partial_: True
in_channels: 10
out_features: 256
number_of_conv3d_layers: 6
conv3d_channels: 32
image_size_pixels: 24
#--------------------------------------------
# Sat encoder settings
#--------------------------------------------
# Ignored as premade batches were created without satellite data
# sat_encoder:
# _target_: pvnet.models.multimodal.encoders.encoders3d.DefaultPVNet
# _partial_: True
# in_channels: 11
# out_features: 256
# number_of_conv3d_layers: 6
# conv3d_channels: 32
# image_size_pixels: 24
add_image_embedding_channel: False
#--------------------------------------------
# PV encoder settings
#--------------------------------------------
pv_encoder:
_target_: pvnet.models.multimodal.site_encoders.encoders.SingleAttentionNetwork
_partial_: True
num_sites: 349
out_features: 40
num_heads: 4
kdim: 40
pv_id_embed_dim: 20
#--------------------------------------------
# Tabular network settings
#--------------------------------------------
output_network:
_target_: pvnet.models.multimodal.linear_networks.networks.ResFCNet2
_partial_: True
fc_hidden_features: 128
n_res_blocks: 6
res_block_layers: 2
dropout_frac: 0.0
embedding_dim: 16
include_sun: True
include_gsp_yield_history: False
#--------------------------------------------
# Times
#--------------------------------------------
# Foreast and time settings
history_minutes: 60
forecast_minutes: 120
min_sat_delay_minutes: 60
sat_history_minutes: 90
pv_history_minutes: 60
# These must be set for each NWP encoder
nwp_history_minutes:
ukv: 60
nwp_forecast_minutes:
ukv: 120
# ----------------------------------------------
# Optimizer
# ----------------------------------------------
optimizer:
_target_: pvnet.optimizers.EmbAdamWReduceLROnPlateau
lr: 0.0001
weight_decay: 0.01
amsgrad: True
patience: 5
factor: 0.1
threshold: 0.002
```
Make sure to update the following config files before training your model:

In `configs/local_trainer.yaml`:

```yaml
_target_: lightning.pytorch.trainer.trainer.Trainer
accelerator: cpu # Important if running on a system without a supported GPU
devices: auto
min_epochs: null
max_epochs: null
reload_dataloaders_every_n_epochs: 0
num_sanity_val_steps: 8
fast_dev_run: false
accumulate_grad_batches: 4
log_every_n_steps: 50
```
1. In `configs/datamodule/local_premade_batches.yaml`:
- update `batch_dir` to point to the directory you stored your batches in during batch creation
2. In `configs/model/local_multimodal.yaml`:
- update the list of encoders to reflect the data sources you are using. If you are using different NWP sources, the encoders for these should follow the same structure with two important updates:
- `in_channels`: number of variables your NWP source supplies
- `image_size_pixels`: spatial crop of your NWP data. It depends on the spatial resolution of your NWP; should match `nwp_image_size_pixels_height` and/or `nwp_image_size_pixels_width` in `datamodule/example_configs.yaml`, unless transformations such as coarsening was applied (e. g. as for ECMWF data)
3. In `configs/local_trainer.yaml`:
- set `accelerator: 0` if running on a system without a supported GPU

And finally update `defaults` in the main `./configs/config.yaml` file to use
If creating copies of the config files instead of modifying existing ones, update `defaults` in the main `./configs/config.yaml` file to use
your customised config files:

```yaml
Expand All @@ -350,7 +222,7 @@ python run.py

## Backtest

If you have succesfully trained a PVNet model and have a saved model checkpoint you can create a backtest using this, e.g. forecasts on historical data to evaluate forecast accuracy/skill. This can be done by running one of the scripts in this repo such as [the UK gsp backtest script](scripts/backtest_uk_gsp.py) or the [the pv site backtest script](scripts/backtest_sites.py), further info on how to run these are in each backtest file.
If you have successfully trained a PVNet model and have a saved model checkpoint you can create a backtest using this, e.g. forecasts on historical data to evaluate forecast accuracy/skill. This can be done by running one of the scripts in this repo such as [the UK GSP backtest script](scripts/backtest_uk_gsp.py) or the [the pv site backtest script](scripts/backtest_sites.py), further info on how to run these are in each backtest file.


## Testing
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4 changes: 4 additions & 0 deletions configs.example/model/multimodal.yaml
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Expand Up @@ -89,6 +89,10 @@ nwp_history_minutes:
nwp_forecast_minutes:
ukv: 480
ecmwf: 480
# Optional; defaults to 60, so must be set for data with different time resolution
nwp_interval_minutes:
ukv: 60
ecmwf: 60

# ----------------------------------------------
# Optimizer
Expand Down
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