War has direct impact on our daily lives. Especially the economy.
Moreover due to high gas imports from russia, germany suffers an explosion
in gas prices for the past months, hence the decreased supply from
various reasons.
In order to help households to make better financial decisions and ease
financial uncertainty, in this Project we predict the gas price and gas costs.
Also I try to explain in a clear way what the model is about.
The Story is build around our hobbies.
The 2 regressor variables are:
- x1 = Time (control variable)
- x2 = Gas Storage (in relation to demand and supply. For further information see: https://agsi.gie.eu/historical/DE)
The predictor variable is:
- f (x1, x2) = Gas Price
Moreover an API was implemented, showing information about gas from Germany's largest gas provider Uniper Energy:
Gas consumption for 2-person-househould per year: 10000 kWh:
Gas price (EUR/kWh) from GASPOOL and Trading Hub Europe (THE) - responsible for german gas market.
Data from 1.10.2015 - 01.09.2021 from:
Data from 01.10.2021 - 01.08.2022 from:
https://www.tradinghub.eu/en-gb/Publications/Prices/Reconciliation-price
German Gas Storage Capacity (in TWh)
1.10.2015 - 01.08.2022:
https://agsi.gie.eu/historical/DE
The Data was collected between 01.10.2015 and 15.08.2022
(Further see the raw dataset in Dataset/Gas_Dataset.csv).
The Scatter Plot shows how Gas Storage, Time and Gas Price relates to each other.
Time Legend:
1.45 * 1e9 = 13.12.2015 (Late Autumn)
1.50 * 1e9 = 14.07.2017 (Mid Summer)
1.55 * 1e9 = 12.02.2019 (Mid Winter)
1.60 * 1e9 = 13.09.2020 (Late Summer)
1.65 * 1e9 = 15.04.2022 (Mid Spring)
It seems, that gas prices on the hand are affected by natural causes (change in temperatur hence seasons),
by which the storage capacities fluctuate; i.e. the scatter plot shows, that the storage capacity reaches
Minima (darker color) in colder seasons. However, the storage capacity is a strong indicater for the gas price.
I.e. right after timestamp 1.60 * 1e9 (13.09.2020) marks a turning point. There storage capacities seem to
decrease and stay at a minimum. At the same time, the gas price spikes in an exponential manner.
Before that time, the storage capacity was more or less reaching maxima. This could be due to the COVID-19
situation, where companies reduced trading and economic activity. So, the spike could also be explained by the
increasing demand for gas, hence companies increased their activities again, due to decreasing spread of SarS-Cov2
Virus.
Hence seasonal fluctuations in temperature and the change in Minima and Maxima, as well as the expontential
slope at the end of the timeline, a polynomial regression model seems suitable (despite also a mulitple linear
regression model is used) for forecasting the future gas price.
f(x1, x2) = 5.02 + -6.69e-09 * x1 + 1.25e-09 * x2 + 2.24e-18 * x1^2 + -7.83e-19 * x2^2 + -3.19e-19 *x1 * x2
f(x1, x2) =-0.20 + 1.52e-10 * x1 + -1.032e-10 * x2
Polynomial Function MAE = 0.008515221391126407
Linear Function MAE = 0.010370585662079948
Polynomial Function R Squared = 0.6370797480659653
Linear Function R Squared = 0.3944088902790577
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The model can be used to make predictions for privat households in order to make better financial decisions.
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The model can be used to reduce uncertainty due to gas price fluctuations.
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The gas price forecasting can be better detailed by implementing future possible outcomes, which affect storage i.e.:
- sudden increase by X % in storage by reduced conflicts/wars.
- rising temperates by X % due to climate change decreases need for storage by X %.
- ...
The mentioned outlook affecting storage can be modelled i.e. via Monte Carlo Randomization. For example continue timestamps by N iterations and adjust storage coefficient for each case by N %. I.e.:
1 Case - 1000 timestamps iterations into future: decrease of quarterly storage capacity by 0.5 percent. 2 Case - 2000 timestamps iterations into future: increase of monthly storage capacity by 1.5 percent. 3 Case - 2000 timestamps iterations into future: first 500 iterations decreasing storage capacity by 0.3 percent, sudden spike by 30% for 200 iterations and increasing capacity by 0.5 percent for the last 1300 iterations.
and so on ...
Those Cases can be visualized for easier understanding.
Unlike a traditional forecast, a Monte Carlo-based forecast does not produce a single number,
but instead provides a range of possible outcomes at a probability distribution.
Today_Price = Yesterday_Price * e^r
r = drift + vol * z
drift = mean - 0.5 * var
