Ease of pulling weather data online
by using Python for Energy Consumption Pattern Analysis and Demand Forecasting
Energy demand forecasting is part
of process optimization in terms of daily capacity reservation and programming
daily purchase of energy resource, stabilizing the stress on the lines, diversifying
resources and lowering their cost with regard to spot and future markets, and
planning future investments. In that sense, forecasting becomes crucial part of
managing the company to survive in the sector through peak times or times when
resources are constrained.
Especially, during winter times,
when the need for heating is high, residential heating peaks. Energy management
and projection of daily demand, even hourly, would put much strain on financial
and operational sides of any company ranging from wholesaler and distribution
ones to power producer plants; such as the risk of falling into spot market,
factored network operations charges due to wrong planning of supply, losing
money on take-or-pay agreements in the long run and so on. Therefore, a
methodology has been built up by the forecasters and statisticians regarding
different type of segments including mainly households, industrial users and
power producers.
Since this topic is related to space
heating during winter times, residential consumers are of focus in this
analysis. In residential heating, energy consumption depends on the factors
mainly as outside temperature, the isolation of buildings, energy efficiency of
boilers/ACs, price, income, etc. Among all, outside temperature will mostly
determine the energy consumed. Whenever the temperature is out of comfort
range, meaning 18 0 C – 22 0 C, energy is consumed to heat
up or cool down the room space. As the actual temperature diverges from one of
these threshold temperatures, energy need for households increases. That being
said, "Heating degree days", or "HDD", are a measure of how
much (in degrees), and for how long (in days), outside air temperature was
lower than a specific "base temperature" (or "balance
point"). For example, for one day, if the outside temperature was 14 0
C, then the HDD would be 18 – 14 = 4. In that sense, as HDD increases, the
heating need for space, eventually energy consumption, would increase, as well.
Specifically, when we talk about natural gas consumption by boilers, in
general, energy need per subscriber would look like as in the following graph:
* Numbers for
energy consumption / subscriber are arbitrary
Yet, when we analyze energy
consumption data by using Eviews, we would treat data in time series format in
seasons; winter period, spring period, summer period. Each period has its own
consumer behavior model with regard to energy consumption pattern. In that
sense, daily Natural Gas consumption data analysis is done as such; retrieve
the temperature data for each day, then convert to HDD, and then match and
compare the total energy consumed per subscriber with the relevant HDD data by
running regression tool.
On Eviews, energy consumption
regression graph would look like the following one:
This graph shows us how total
energy consumption per subscriber in one day changes with regard to HDD of that
day. Using the formula derived from this regression of energy and temperature
data, companies can estimate the energy need by residents with regard to HDD
vaue of coming days.
Regarding the data needed to create
time series and then run a regression, Energy companies can track energy
consumption and subscriber data, yet tracking temperature data is difficult,
and even some meteorological observatory institutions would ask for a fee to
provide such a service. On the other hand, jotting down numbers from websites
would take time, as well. At this point, a software called as Python would step
in to bring digital data to your computer from the web. Python, as a
programming language, needs codes to get the orders and serve you as an agent
to capture the data. Since I am not a programmer, I would not talk about the
coding procedure, yet I will give you the overall picture. First, one should
have installed the relevant programs:
-
Install Python 2.7.3
-
Then install Spyder 2.1.11
-
Then install xlwt-0.7.3
*Warning: If
you run on 64 bit computer, you may need different versions for these programs.
Here, “xlwt” is a program to create
excel files; as Python captures data with regard to the codes you entered, they
will be formatted as excel files. Besides, the codes you entered to python file
can be edited through IDLE. Regarding the temperature data, a format has been
already placed on web so that Python will recognize the data format and create
excel sheets as the process of capturing data starts. At this point, a website
called as www.weatherunderground.com
steps in. This website is highly popular one among those who are keeping an eye
on weather conditions and compatible with Python users. The compatibility issue
is a matter of formatting the data on websites as “json” files. On this
website, max and min temperature data, wind direction and magnitude data, along
with comments and other data are formatted as json files so that Python reads
the data and brings them to your computer to format in an excel file. In that
sense, for any province or city, one can find meteorological data in json
format. Then, it is part of your programming skill to write the codes to
capture historical temperature data or 10 days of forecast. Below is part of
the code that is written to capture historical data for Muğla, a western city
of Turkey.
Likewise, the website also has
daily json files for 10 days of forecast so that energy companies can forecast
the related energy need change with regard to temperature forecast in any city.
On excel sheet, the forecast data will look like as such:
As you create your model on Excel,
you can easily link your forecasting formula with the name of excel files you
create each day by running the software. In conclusion, one leg (temperature
data) of tri-pod (energy consumed, subscribers, temperature) on which
residential energy consumption pattern analysis is mounted would also be
digitally available in an easier format.
