Important tips

1. Project componets

1.1 Data

In the data directory, the orignial input file is 'Table_Fundamentalmodell_InOut.xlsx' and 'holidays'. The 'preprocessed price.csv' is generated with Python script 'preprocessing.py', which shifts the time series variables to generate lagged variables, and adds time relevant variables like hour, weekday, holiday. Last but not least, preprocessing of auxiliary variables. The '2013-2018Label.csv' file is genarated with 'predictionresultslabelling.py'. In this file contains the information of all predictions of different models. And the prediction errors, final submitted electricity prices (fundamental model + residual forecsting) are included. The labelling function labels the best model in each hour, 0 for direct MLP, 1 for SARIMAX, 2 for recursive MLP and 3 for Encoder-Decoder. The 'sampledays2018.json' contains the days selected in 2018 for hyperparameter optimization of the neural network based models. The sampledays2018 is generated with function 'generatedays' in 'analytics.py'

1.2 Config

The config directory contains three config file. Each config file contains the corresponding parameters and hyperparameters for the neural network models. The parameter 'verbose' indicates whether the training process shoudld be printed out. 0 for no print, 1 for print.

1.3 FiguresInTheThesis

This directory contains the figures in the thesis. These figures are generated with the file 'FiguresInTheThesis.ipynb', which could be excuted with Jupyter Notebook

1.4 Prediction

The prediction directory contains the residual prediction of different models. Each model directory contains predictions from 2013 to 2018. And in each .csv file contains the actual residual value (actual), predicted residual value (prediction), and the error of the prediction (error).

1.5 Visualisation

The visualisation directory contains basic visualisation of the data. The code for visualisation is in the 'visualisation.py' file

2. Code for each model

2.1 Naive model

We have for each model a code file. The naive model does not need specific code. We have the results direct generated 'predictionresultslabelling' file.

2.2 direct mlp & recursive mlp model

For both models, feature selection options are considered. There are two specific feature selection method, miselection and mrmrensemble selection. The mrmrensemble method is a package from R. We call the R function in Python with the rstring. So, if the mrmrensemble is used to select variables, the line
'en <- mRMR.ensemble(data=feature_data,target_indices=c(1),solution_count=1,feature_count=37)' must be manually changed. feature_count is the number of features to be selected. The default value for feature selection is 'None', which means no further feature selection from the 'features' in the config file. The features in the corresponding config file are also the final features used in the final predictions. The both model could be multiprocessed, which mean forecasting several days together. With the 'Parallel(n_jobs=-1)(delayed(run)(item) for item in [list(range(90)), list(range(90, 180)), list(range(180, 270)), list(range(270, 365))])' the forecasting for one year could be excuted in four processes. If only with 'run(list(range(365)))', multiprocessing is not used.

2.3 Encoder-Decoder model

The encoder-decoder model needs more time for excution, if there is no GPU accleration. To reduce the running time, one option is to reduce the patience in the config file. So the total excuted epochs would be reduced.

2.4 sarimax model

The sarimax model contains the grid search process. In each day, nine models are fitted. The search process needs about 10 minutes. So for one year prediction, it need 60-70 hours.

2.5 knn model

The knn model need very little time. For each year, only 10 minutes are needed.