This is the second iteration of https://github.com/paulbarbu/arima-energy-prediction
Implemented in R 3.5.0.
R version 3.5.0 (2018-04-23) -- "Joy in Playing"
This project depends on the following R packages:
- forecast
- foreach
- doParallel
- openssl
- ggplot2
- gridExtra
- knitr
- rmarkdown
On Windows I recommand using RStudio, while on Linux the command line is sufficiently intuitive to work directly there. In RStudio simply open one of the files ending in "report_generator.R" and set the source directory to be the one of the currently opened file.
Now you can run either a snippet of code in RStudio by selecting it or you can just run the whole file. Depending on the file you've selected this may take up to several hours.
The report function is used to create a report file (using knitr) on a small part of the data, this is a detailed
report containing several plots and statistical indicators as to what ARIMA parameters are best suited to forecast the given data.
Once the user is fully aware of the data and its features, the report.full function may be used to generate the forecasts on the full data set.
This is usually what we're interested in, the forecast on a set of data and the accuracy.
One of the most complex calls to report.full is as follows:
dailyD.fcast <- quote(
{cbind(
dummies=getDailyDummies(h, frequency(.), start(.)[[1]]),
fourier(., h=h, K=2)
)}
)
dailyD.fit <- quote(
{cbind(
dummies=getDailyDummies(length(.), frequency(.), start(.)[[1]]),
fourier(., K=2)
)}
)
report.full(model = paste0('Arima(order=c(1, 0, 0), seasonal=c(1, 0, 0), method="CSS", xreg=', paste0(deparse(dailyD.fit), collapse='') ,')'),
series = '1hrs pv1',
transformation = 'identity()',
traindays = 5,
testdays = 2,
xreg = paste0(deparse(dailyD.fcast), collapse=''))
This call shows that an ARIMA(1, 0, 0)(1, 0, 0) with external regressors (daily dummy variables) is used on the pv1 series sub-sampled at one hour intervals.
No transformation is applied on the data since the argument is identity().
The model is consecutively fit on five training days and is evaluated on two test days until the whole data set is forecast.
The quote and deparse functions are used to turn pieces of code into strings and back.
This is needed in order to be able to dinamically change the external regressors used by the ARIMA model.
Both render and render.full will generate a report file (either in HTML or PDF format), the report's source code can be inspected in arima_model.Rmd and in full_forecast_model.Rmd respectively.
At the core of creating a report for the full data is the fullforecast function, which accepts the same parameters as report.full,
the main difference being that the data is not passed in by its name, instead the values in the set are used now (as a ts variable).
Note that the arima_model.Rmd file and full_forecast_model.Rmd files can be run individually from RStudio by setting the correct parameters at the top of the files.
The results of the report.full call are stored in a separate directory, each time series will have its forecasted results in its own directory.
The results are composed of five file types:
- the main .HTML (or .PDF) one, where the accuracy of the model and the forecasted data is plotted. Usually this file is used to inspect the usefulness of the algorithm.
- the .POINTS.CSV one, where the forecasted points in the series are stored.
- the .ADJPOINTS.CSV one, where the forecasted points in the series (adjusted to be only positive) are stored. This file can be used to re-draw the plot or to further investigate details about the forecasts.
- the .RDATA one, where the forecasted points, the adjusted forecasted points together with their respective accuracy measures are held. The only difference between the CSV files is that the accuracies are stored here as well.
- the .TXT one, where information with regards to the full ARIMA model are held. Due to technical reasons the previously mentioned files have their names trimmed to 100 characters and sanitized. This leads to incomplete names or to possibl ename duplication when several models are run. Hence each of the previous files will get a hash at the end of the name (eg. 1f8ab92b4c2080cf824405f705f955e9). Then this TXT file will have exactly that hash name and will contain the unabriged version of the ARIMA parameters used to forecast the data.
As one can see the above files only provide the results of the forecast (either in graphic form, the HTML/PDF file) or in numerical form in the CSV/RDATA files.
The original data is not provided as part of those files since they are stored separately.
For details on the original data, one should look into the datasets.R file.
This file can be directly loaded in R and then access to the time series can be made via the datasets variable.
What this file accomplishes is nothing more than loading the data stored in the data/ directory.
Each TXT file in there holds the correctly sampled (according to the name) version of time series points from the fems20.csv data, for example:
- 2hrs-pv2.txt holds the PV2 time series sampled every two hours
- hourly-pv2.txt holds the PV2 time series sampled at one hour intervals
- pv2.txt holds the original PV2 time series (5 minute sampling rate) from which the other two series were derived
Please note that this is my dissertation project for which I have an exam in July 2019. Please note that I do not claim any rights on the data used in this project. The data is the same one used by Stefan Feilmeier in "Stefan Feilmeier. Loads management based on Photovoltaic and Energy Storage System. MSc Thesis, "Lucian Blaga" University of Sibiu, Supervisor: Árpád Gellért, 2015."
Copyright 2018-2019 (c) Barbu Paul - Gheorghe
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