Global horizontal irradiance forecasting code and data
Description of this data
There are four main folders in the project: code, data, models and logdir.
This folder contains all the data used from the two studied locations: Loc.1 (latitude=40.4º, longitude=6.0º) and Loc.2 (latitude=39.99º, longitude=-0.06º).
Sorted by year, month and day, each location has three kinds of data:
• The files named as just a number are 151x151 irradiance estimates matrices centered in the same location obtained from http://msgcpp.knmi.nl. The spatial resolution is 0.03º for both latitude and longitude.
• The files named Real_ are the irradiance measurements at the location
• The files named CopernicusClear_ are the clear sky estimates from the CAMS McClear model
Each file contains the 96 15-minute samples for the same day in Matlab format and UTC time.
All the python scripts used to train the neural networks and perform the forecasts. The main files are:
• tf1.yml: List of the modules and versions used. A clean Anaconda environment created from this file can run all the code in the project.
• learnRadiation.py: The script to train a new model. Changing the variables “paper_model_name” and “location”. The first variable selects the kind of model to fit and the second one the training location.
• predictOnly.py: Loads a trained model and performs the forecast. Notice that the model and location must match the ones used to train the model stored in the “training_path” folder
This folder contains all the trained models and their forecasting results. There is also a training folder to contain the last trained model.
This folder stores Tensorboard files during training
How to train and test a model
A new model can be trained using “learnRadiation.py”. This script has three parameters
• location: Selects the location where the model will be trained (LOC1 or LOC2)
• paper_model_name: This sets the inputs to match the ones used in the models from the article.
• training_path: The folder to save the trained model
Then the “predictOnly.py” script allows performing the forecasts. It is important to set the same parameters as in the “learnRadiation.py” script. This program will generate the predictions and save them in the model folder. It also plots some days, which can be modified at the bottom of the script.
For instance for LOC2 and model TOA & all real we would run:
"python learnRadiation.py TOAallreal LOC2 training"
This will train the neural network and save the results in the folder models/training.
After this, we would generate the results and plot some days using:
“python predictOnly.py TOAallreal LOC2 training”
This will save the forecasts and real values in the training folder and show figures with 1 to 6 hour forecasts
The models used for the article can also be evaluated by using predictOnly.py and targeting their folders. For instance, to evaluate the TOA & all real model used in the article, this command must be used:
“python predictOnly.py TOAallreal LOC2 RtoaAllReal”
Experiment data files
Cite this dataset
Segarra-Tamarit, Jorge; Perez, Emilio; Beltran, Hector; Perez, Javier (2020), “Global horizontal irradiance forecasting code and data”, Mendeley Data, v5 http://dx.doi.org/10.17632/dcj3yvzybr.5
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The files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International licence.