Friday, August 16, 2024

Natural Gas Forecast Q4 2024 and 2025

 What are the major factors for natural gas in Q4 2024 and 2025

  • Seasonality in demand for natural gas
    • Dec 24 - Feb 25 winter very high demand
    • Mar 25 - May 25 spring low demand
    • Jun 25 - Aug 25 summer high demand
    • Sep 25 - Nov 25 autumn low demand
  • How to de-risk in case Middle East reduce supply?
  • How to de-risk in case Ukraine war deescalated/ escalates?
  • How to de-risk in case of Middle East war
  • Geopolitical issue over claim of South China Sea (10 dash line)
  • Major tech breakthrough in natural gas extraction, transportation, storage, usage



https://en.macromicro.me/collections/4854/mm-natural-gas/39414/global-natural-gas-prices

https://www.naturalgasworld.com/ukraine-prepared-to-store-and-re-export-gas-to-eu-this-winter-operator-says-106991

- European natural gas price is expected to remain low due to high storage as of Aug 2024, due to milder-than-usual weather. Storage is 58% full at end of Mar. 
- Geopolitical tension not easing (Israel war, Ukraine war)


EU bans on Russian LNG will lead to more Russian LNG to Asia






















Forecast Natural Gas Consumption
https://www.mdpi.com/1996-1073/15/1/348#
Factors to consider when predicting natural gas consumption. 
Other factors: weather change, holiday seasons, day of week, time which people spend in homes




It is worth noticing that in summer, i.e., 120–250 and 500–630 data points, changes in temperature and wind speed do not generate large changes in consumption. It can be assumed that fluctuations in gas consumption in this period depend more on the day of the week (working and non-working) and the activity of industrial consumers.

In summer, daily and hourly difference of natural gas consumption is great because of thermal inertia of buildings


3 biggest factors to predict natural gas consumption on a specific day: previous day's consumption, air temperature, month 

70-30 training-test split


RF is better than MLR and DNN at (1) using today's data to predict today's gas consumption, and (2) using yesterday's data to predict today's gas consumption

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