Basic Evaluation

.github/workflows/pytest.yaml

@@ -11,4 +11,5 @@ jobs:
       # pytest-cov looks at this folder
       pytest_cov_dir: "quartz_solar_forecast"
       os_list: '["ubuntu-latest"]'
-      python-version: "['3.10','3.11']"
+      python-version: "['3.10','3.11']"
+      extra_commands: echo "HF_TOKEN=${{ vars.HF_TOKEN }}" > .env

README.md

@@ -4,16 +4,17 @@
 <!-- ALL-CONTRIBUTORS-BADGE:END -->
 
 The aim of the project is to build an open source PV forecast that is free and easy to use.
+The forecast provides the expected generation in `kw` for 0 to 48 hours for a single PV site.
+
 Open Climate Fix also provide a commercial PV forecast, please get in touch at [email protected]
 
 The current model uses GFS or ICON NWPs to predict the solar generation at a site
 
-
 ```python
 from quartz_solar_forecast.forecast import run_forecast
 from quartz_solar_forecast.pydantic_models import PVSite
 
-# make input data
+# make a pv site object
 site = PVSite(latitude=51.75, longitude=-1.25, capacity_kwp=1.25)
 
 # run model, uses ICON NWP data by default
@@ -50,6 +51,38 @@ The 9 NWP variables, from Open-Meteo documentation, are mentioned above with the
 - The model is trained on [UK MetOffice](https://www.metoffice.gov.uk/services/data/met-office-weather-datahub) NWPs, but when running inference we use [GFS](https://www.ncei.noaa.gov/products/weather-climate-models/global-forecast) data from [Open-meteo](https://open-meteo.com/). The differences between GFS and UK MetOffice, could led to some odd behaviours.
 - It looks like the GFS data on Open-Meteo is only available for free for the last 3 months. 
 
+## Evaluation
+
+To evaluate the model we use the [UK PV](https://huggingface.co/datasets/openclimatefix/uk_pv) dataset and the [ICON NWP](https://huggingface.co/datasets/openclimatefix/dwd-icon-eu) dataset.
+All the data is publicly available and the evaluation script can be run with the following command
+
+```bash
+python scripts/run_evaluation.py
+```
+
+The test dataset we used is defined in `quartz_solar_forecast/dataset/testset.csv`. 
+This contains 50 PV sites, which 50 unique timestamps. The data is from 2021. 
+
+The results of the evaluation are as follows
+The MAE is 0.1906 kw across all horizons. 
+
+| Horizons | MAE [kw]      | MAE [%] |
+|----------|---------------| ------- |
+| 0        | 0.202 +- 0.03 | 6.2 |
+| 1        | 0.211 +- 0.03 | 6.4 |
+| 2        | 0.216 +- 0.03 | 6.5 |
+| 3 - 4    | 0.211 +- 0.02 |6.3 |
+| 5 - 8    | 0.191 +- 0.01 | 6 |
+| 9 - 16   | 0.161 +- 0.01 | 5 |
+| 17 - 24  | 0.173 +- 0.01 | 5.3 |
+| 24 - 48  | 0.201 +- 0.01 | 6.1 |
+
+
+Notes: 
+- THe MAE in % is the MAE divided by the capacity of the PV site. We acknowledge there are a number of different ways to do this. 
+- it is slightly surprising that the 0-hour forecast horizon and the 24-48 hour horizon have a similar MAE.
+This may be because the model is trained expecting live PV data, but currently in this project we provide no live PV data. 
+
 ## Abbreviations
 
 - NWP: Numerical Weather Predictions

quartz_solar_forecast/data.py

@@ -91,6 +91,12 @@ def get_nwp(site: PVSite, ts: datetime, nwp_source: str = "icon") -> xr.Dataset:
         }
     )
     df = df.set_index("time")
+    data_xr = format_nwp_data(df, nwp_source, site)
+
+    return data_xr
+
+
+def format_nwp_data(df: pd.DataFrame, nwp_source:str, site: PVSite):
     data_xr = xr.DataArray(
         data=df.values,
         dims=["step", "variable"],
@@ -103,11 +109,10 @@ def get_nwp(site: PVSite, ts: datetime, nwp_source: str = "icon") -> xr.Dataset:
     data_xr = data_xr.assign_coords(
         {"x": [site.longitude], "y": [site.latitude], "time": [df.index[0]]}
     )
-
     return data_xr
 
 
-def make_pv_data(site: PVSite, ts) -> xr.Dataset:
+def make_pv_data(site: PVSite, ts: pd.Timestamp) -> xr.Dataset:
     """
     Make fake PV data for the site
 

quartz_solar_forecast/dataset/make_test_set.py

@@ -0,0 +1,133 @@
+"""
+Make a random test set
+
+This takes a random subset of times and for various pv ids and makes a test set
+
+There is an option to odmit timestamps that don't exsits in the ICON dataset:
+https://huggingface.co/datasets/openclimatefix/dwd-icon-eu/tree/main/data
+"""
+import os
+from typing import Optional
+
+import numpy as np
+import pandas as pd
+
+from quartz_solar_forecast.eval.utils import make_hf_filename
+from huggingface_hub import HfFileSystem
+
+test_start_date = pd.Timestamp("2021-01-01")
+test_end_date = pd.Timestamp("2022-01-01")
+
+# this have been chosen from the entire training set. This ideas
+pv_ids = [
+    9531,
+    7174,
+    6872,
+    7386,
+    13607,
+    6330,
+    26841,
+    6665,
+    4045,
+    26846,
+    6494,
+    7834,
+    3543,
+    7093,
+    3864,
+    8412,
+    3454,
+    9765,
+    10585,
+    26942,
+    7721,
+    26804,
+    7551,
+    26861,
+    7568,
+    7338,
+    7410,
+    6967,
+    16480,
+    7241,
+    7593,
+    7557,
+    7757,
+    3094,
+    6800,
+    26905,
+    5512,
+    26840,
+    7595,
+    5803,
+    26876,
+    7846,
+    26786,
+    7580,
+    6629,
+    16477,
+    3489,
+    26796,
+    12761,
+    26903,
+]
+
+np.random.seed(42)
+
+
+def make_test_set(output_file_name: Optional[str] = None, number_of_samples_per_system: int = 50, check_hf_files: bool = False):
+    """
+    Make a test set of random times and pv ids
+
+    :param output_file_name: the name of the file to write the test set to
+    :param number_of_samples_per_system: the number of samples to take per pv id
+    """
+
+    if output_file_name is None:
+        # get the folder where this file is
+        output_file_name = os.path.dirname(os.path.abspath(__file__)) + "/testset.csv"
+
+    ts = pd.date_range(start=test_start_date, end=test_end_date, freq="15min")
+
+    # check that the files are in HF for ICON
+    if check_hf_files:
+        ts = filter_timestamps_if_hf_files_exists(ts)
+
+    test_set = []
+    for pv_id in pv_ids:
+        ts_choice = ts[np.random.choice(len(ts), size=number_of_samples_per_system, replace=False)]
+        test_set.append(pd.DataFrame({"pv_id": pv_id, "timestamp": ts_choice}))
+    test_set = pd.concat(test_set)
+    test_set.to_csv(output_file_name, index=False)
+
+    return test_set
+
+
+def filter_timestamps_if_hf_files_exists(timestamps_full: pd.DatetimeIndex):
+    """
+    Filter the timestamps if the huggingface files exist
+
+    We are checking if the teimstamps, rounded down to the nearest 6 hours,
+    exist in
+    https://huggingface.co/datasets/openclimatefix/dwd-icon-eu/tree/main/data
+
+    """
+    timestamps = []
+    fs = HfFileSystem()
+    # print(fs.ls("datasets/openclimatefix/dwd-icon-eu/data/2022/4/11/", detail=False))
+    for timestamp in timestamps_full:
+        timestamp_floor = timestamp.floor("6H")
+        _, huggingface_file = make_hf_filename(timestamp_floor)
+        huggingface_file = huggingface_file[14:]
+
+        if fs.exists(huggingface_file):
+            timestamps.append(timestamp)
+        else:
+            print(f"Skipping {timestamp} because {huggingface_file} does not exist")
+
+    timestamps = pd.DatetimeIndex(timestamps)
+    return timestamps
+
+
+# To run the script, un comment the following line and run this file
+# make_test_set(check_hf_files=True)