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https://github.com/adamouization/solar-irradiance-forecasting

Predicting short-term solar irradiance using deep learning and statistical methods on the Folsom dataset

https://github.com/adamouization/solar-irradiance-forecasting

Science Score: 23.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
  • ✓
    Academic publication links
    Links to: zenodo.org
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (11.3%) to scientific vocabulary

Keywords

arima arima-forecasting data-science deep-learning irradiance irradiance-forecasting machine-learning python
Last synced: 6 months ago · JSON representation

Repository

Predicting short-term solar irradiance using deep learning and statistical methods on the Folsom dataset

Basic Info
  • Host: GitHub
  • Owner: Adamouization
  • License: agpl-3.0
  • Language: Jupyter Notebook
  • Default Branch: master
  • Homepage:
  • Size: 4.77 MB
Statistics
  • Stars: 0
  • Watchers: 2
  • Forks: 0
  • Open Issues: 1
  • Releases: 0
Topics
arima arima-forecasting data-science deep-learning irradiance irradiance-forecasting machine-learning python
Created over 2 years ago · Last pushed over 2 years ago
Metadata Files
Readme License

README.md

Short-Term Solar Irradiance Forecasting using LSTMs GitHub issues License: AGPL v3

Python Jupyter Notebook Keras TensorFlow Pandas NumPy Matplotlib scikit-learn

Project Goal

Solar energy is a rapidly growing source of renewable energy, contributing significantly to global sustainability efforts. It depends on solar irradiance, which is the amount of solar energy received per unit area, measured using GHI (global irradiance). Accurate solar irradiance forecasting is crucial for: * optimising energy production * designing, planning and operational management of solar energy farms.

The goal of this project is to build a predictive model that can accurately predict future irradiance.

The objective is to leverage the various historical data provided in the "A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods" dataset to build a solution that can accurately forecast irradiance for the next 20 minutes.

Preliminary LSTM result

image

Setup

Create a virtual environment:

python -m venv <PATH>/Solar-Irradiance-Forecasting source <PATH>/Solar-Irradiance-Forecasting/bin/activate

Install requirements:

cd Solar-Irradiance-Forecasting pip install -r env/requirements-light.txt

Download data: python src/utils/donwload_zenodo_data.py

Open relevant Jupyternotebooks in src/

License

Contact

  • Email: adam[at]jaamour[dot]com
  • Website: www.adam.jaamour.com
  • Linktree: https://linktr.ee/adamouization

Owner

  • Name: Adam Jaamour
  • Login: Adamouization
  • Kind: user
  • Location: United Kingdom
  • Company: @NewDayTechnology

💻 Data Scientist @NewDayTechnology 🧠 MSc AI @ Uni of St Andrews 📓 BSc Computer Science @ Uni of Bath 💼 Former SWE @ Scuderia Alpha Tauri F1 Team

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  • Total issue authors: 1
  • Total pull request authors: 0
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  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Dependencies

env/requirements-light.txt pypi
  • keras-tuner *
  • matplotlib *
  • numpy *
  • openpyxl *
  • pandas *
  • pmdarima *
  • prophet *
  • statsmodels *
  • tensorflow *
  • tqdm *
env/requirements.txt pypi
  • Babel ==2.10.3
  • Cython ==3.0.2
  • HeapDict ==1.0.1
  • Jinja2 ==3.1.2
  • Keras-Preprocessing ==1.1.2
  • LunarCalendar ==0.0.9
  • Markdown ==3.4.1
  • MarkupSafe ==2.1.1
  • Pillow ==9.2.0
  • PyJWT ==2.5.0
  • PyMeeus ==0.5.12
  • PyQt5 ==5.15.7
  • PyQt5-sip ==12.11.0
  • PySocks ==1.7.1
  • PyYAML ==6.0
  • Pygments ==2.13.0
  • Send2Trash ==1.8.0
  • Unidecode ==1.3.4
  • Werkzeug ==2.2.2
  • absl-py ==1.2.0
  • aiobotocore ==2.3.4
  • aiohttp ==3.8.3
  • aioitertools ==0.11.0
  • aiosignal ==1.2.0
  • anyio ==3.6.1
  • appdirs ==1.4.4
  • argon2-cffi ==21.3.0
  • argon2-cffi-bindings ==21.2.0
  • asn1crypto ==1.5.1
  • asttokens ==2.0.8
  • astunparse ==1.6.3
  • async-timeout ==4.0.2
  • attrs ==22.1.0
  • backcall ==0.2.0
  • backports.functools-lru-cache ==1.6.4
  • beautifulsoup4 ==4.11.1
  • bleach ==5.0.1
  • blinker ==1.4
  • bokeh ==2.4.3
  • botocore ==1.24.21
  • brotlipy ==0.7.0
  • cachetools ==5.2.0
  • certifi ==2022.9.24
  • cffi ==1.15.1
  • charset-normalizer ==2.0.12
  • click ==8.0.4
  • cloudpickle ==2.2.0
  • cmdstanpy ==1.1.0
  • convertdate ==2.4.0
  • croniter ==0.3.36
  • cryptography ==36.0.2
  • cycler ==0.11.0
  • cytoolz ==0.12.0
  • dask ==2022.3.0
  • dask-cuda ==0
  • dask-saturn ==0.4.2
  • debugpy ==1.6.3
  • decorator ==5.1.1
  • defusedxml ==0.7.1
  • distributed ==2022.3.0
  • docker ==5.0.3
  • docker-pycreds ==0.4.0
  • entrypoints ==0.4
  • ephem ==4.1.4
  • et-xmlfile ==1.1.0
  • executing ==1.1.0
  • fastjsonschema ==2.16.2
  • flatbuffers ==1.12
  • flit_core ==3.7.1
  • fonttools ==4.37.3
  • frozenlist ==1.3.1
  • fsspec ==2022.5.0
  • gast ==0.4.0
  • google-auth ==2.11.1
  • google-auth-oauthlib ==0.4.6
  • google-pasta ==0.2.0
  • graphviz ==0.20.1
  • grpcio ==1.46.3
  • h5py ==3.7.0
  • holidays ==0.31
  • idna ==3.4
  • importlib-metadata ==4.11.4
  • importlib-resources ==5.9.0
  • ipykernel ==6.13.0
  • ipython ==8.5.0
  • ipython-genutils ==0.2.0
  • ipywidgets ==7.7.0
  • jedi ==0.18.1
  • jmespath ==1.0.1
  • joblib ==1.3.2
  • json5 ==0.9.5
  • jsonschema ==4.16.0
  • jupyter-client ==7.3.4
  • jupyter-server ==1.19.0
  • jupyter_core ==4.11.1
  • jupyterlab-pygments ==0.2.2
  • jupyterlab-widgets ==1.1.1
  • jupyterlab_server ==2.15.2
  • keras ==2.9.0
  • keras-tuner ==1.3.5
  • kiwisolver ==1.4.4
  • kt-legacy ==1.0.5
  • libclang ==14.0.6
  • llvmlite ==0.38.1
  • locket ==1.0.0
  • lxml ==4.9.1
  • lz4 ==4.0.0
  • marshmallow ==3.18.0
  • marshmallow-oneofschema ==3.0.1
  • matplotlib ==3.5.2
  • matplotlib-inline ==0.1.6
  • mistune ==0.8.4
  • msgpack ==1.0.4
  • multidict ==6.0.2
  • munkres ==1.1.4
  • mypy-extensions ==0.4.3
  • natsort ==8.2.0
  • nbclient ==0.5.13
  • nbconvert ==6.5.3
  • nbformat ==5.6.1
  • nest-asyncio ==1.5.5
  • notebook ==6.4.12
  • numba ==0.55.2
  • numpy ==1.21.6
  • oauthlib ==3.2.1
  • openpyxl ==3.1.2
  • opt-einsum ==3.3.0
  • oscrypto ==1.3.0
  • packaging ==21.3
  • pandas ==1.4.2
  • pandocfilters ==1.5.0
  • parso ==0.8.3
  • partd ==1.3.0
  • patsy ==0.5.3
  • pendulum ==2.1.2
  • pexpect ==4.8.0
  • pickleshare ==0.7.5
  • pip ==22.2.2
  • pkgutil_resolve_name ==1.3.10
  • ply ==3.11
  • pmdarima ==2.0.3
  • prefect ==0.15.13
  • prefect-saturn ==0.6.0
  • prometheus-client ==0.14.1
  • prompt-toolkit ==3.0.31
  • prophet ==1.1.4
  • protobuf ==3.19.5
  • psutil ==5.9.2
  • ptyprocess ==0.7.0
  • pure-eval ==0.2.2
  • pyOpenSSL ==21.0.0
  • pyarrow ==6.0.1
  • pyasn1 ==0.4.8
  • pyasn1-modules ==0.2.7
  • pycparser ==2.21
  • pycryptodomex ==3.15.0
  • pynvml ==11.4.1
  • pyparsing ==3.0.9
  • pyrsistent ==0.18.1
  • python-box ==6.0.2
  • python-dateutil ==2.8.2
  • python-slugify ==6.1.2
  • pytz ==2022.2.1
  • pytzdata ==2020.1
  • pyu2f ==0.1.5
  • pyzmq ==24.0.1
  • requests ==2.28.1
  • requests-oauthlib ==1.3.1
  • rsa ==4.9
  • ruamel.yaml ==0.17.21
  • ruamel.yaml.clib ==0.2.6
  • s3fs ==2022.5.0
  • scikit-learn ==1.3.0
  • scipy ==1.11.2
  • setuptools ==59.8.0
  • sip ==6.6.2
  • six ==1.16.0
  • sniffio ==1.3.0
  • snowflake-connector-python ==2.7.7
  • sortedcontainers ==2.4.0
  • soupsieve ==2.3.2.post1
  • stack-data ==0.5.1
  • statsmodels ==0.14.0
  • tabulate ==0.8.10
  • tblib ==1.7.0
  • tensorboard ==2.9.0
  • tensorboard-data-server ==0.6.0
  • tensorboard-plugin-wit ==1.8.1
  • tensorflow ==2.9.1
  • tensorflow-estimator ==2.9.0
  • tensorflow-io-gcs-filesystem ==0.27.0
  • termcolor ==2.0.1
  • terminado ==0.15.0
  • text-unidecode ==1.3
  • threadpoolctl ==3.2.0
  • tinycss2 ==1.1.1
  • toml ==0.10.2
  • toolz ==0.12.0
  • tornado ==6.1
  • tqdm ==4.66.1
  • traitlets ==5.4.0
  • typing_extensions ==4.3.0
  • unicodedata2 ==14.0.0
  • urllib3 ==1.26.11
  • voila ==0.3.5
  • wcwidth ==0.2.5
  • webencodings ==0.5.1
  • websocket-client ==1.4.1
  • websockets ==10.3
  • wheel ==0.37.1
  • widgetsnbextension ==3.6.1
  • wrapt ==1.14.1
  • yarl ==1.7.2
  • zict ==2.2.0
  • zipp ==3.8.1