EMD-API - Wind Energy Index Service

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Comissioning of On-Shore Wind Turbines in Denmark.

Introduction

The wind-energy-index service is available as a global service - providing reliable wind-index information for any part of the world. The service is available from a REST / OPENAPI interface. This page describes how to install the service - and how to consume it from a python client. Resources for the OpenAPI standard and the data model are here:

Please note:

  • This EMD-API introduction is aimed at programmers, modellers or analysts who are working with machine-driven interfaces and workflows, typically using programming languages like python or R.
  • Also note, that we provide a python (jupyter notebook) example to get you kick-started in using our API-services and to integrate towards your own services and tools.

Access

The API is currently (December 2020) in beta-release. To see more documentation and to access the data-services, please visit the API through the following URL's:

  • EMD-API Overview (Wiki) - here.
  • EMD-API Main Page (API) - here.
  • EMD-API Wind Energy Index UI (API) - here.

Any tehnical questions on our Wind Energy Index Serviuces can be addressed to our Senior Wind Energy Consultant Henrik S. Pedersen: hsp@emd.dk.

Data Model - Wind Energy Index Serivice

The EMD wind-energy-index service is documented in a REST based API using the OpenAPI Specification. You can view the interfaces and download the interfaces as json or yaml, here. The service provides the following functionality:

  • List Turbines: Full list of available turbines with their identification ID. The list is private an will return all turbines connected to your account.
  • Turbine Details: Information on turbine details: Name, data-availability, hub-height, position-latitude, position-longitude, power-curve, control-strategy, rated-power, training-start, training-end, time-zone.
  • Wind Energy Index Data: Month-wind-index, month-anomality-index, month-predicted-production, 3-month-running-average-index., 12-month-running-average-index
    • Request data for all months between 1990 and present
    • Request data for specific month between 1990 and present

Python - Installation

The simplest way to use the EMDAPI with python is to install the client software in a virtual environment. If you are using CONDA or MINICONDA, we recommend that you create a new virtual environment and use a recent 3.x version of python. When the virtual environment is created, then activate the environment.

Open your Anaconda Prompt. Copy-paste the following lines: 

conda create -n emdapiwindindex python=3.8.5
conda activate emdapiwindindex

Install the required packages needed in order to do data-science and use the examples provided within the jupyter notebooks. We have have validated this setup using specific package versions (used in the commands below).

In the Anaconda Prompt, copy-paste the following lines, one by one: 

conda install -c conda-forge pandas=1.1.0 numpy=1.19.1
conda install -c conda-forge jupyter=1.0.0 ipykernel=5.3.4

Download the zip-file holding the OpenAPI python client for the emdapi wind-index-service.
Unpack the file and install it within your virtual environment:

In the Anaconda Prompt: Move to the folder, where you have unpacked the zipped file. Copy-paste the following line: 

python setup.py install

Make sure that the new emdapi virtual enviroment (python-kernel) is available to be used with jupyter-notebook environment: 

python -m ipykernel install --user --name=emdapiwindindex
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