Intelligent Power Demand Forecast to Minimize “Order-Consumption” Gap
Our Client is a significant consumer of electricity for its
operations and is required to submit precise energy usage
forecasts to its suppliers on a daily basis. It is crucial that
these projections accurately reflect the anticipated
consumption as penalties may apply if there’s substantial
variance between the forecast and actual consumption. Our
Client was looking to invest into advanced machine learning
models as the monitoring solutions to do more accurate
forecasting, reduce any manual intervention and most
importantly avoid an increase in operational costs incurred
due to penalties or higher purchasing costs. Furthermore, the
introduction of advanced forecasting tools will allow our client
to mitigate any future increase in excess operating cost as the
electric locomotive’s footprint increases gradually. Our Client
is promoting more electric equipment’s adoption and thus
advanced technology investments are very much aligned with
overall objectives and goals.
Challenges
The current energy forecasting solution deployed at our
client’s place is based on a statistical method that leverages
historical energy consumption static data and requires too
frequent manual intervention to adjust & override the
system-generated forecasts in order to minimize the absolute
deviation and minimize operations costs that are incurred
through penalties or higher purchase costs. The higher
deviation in the forecast on a daily basis is limiting our client to
provide more accurate energy demand to its suppliers. It is
expected the demand for energy due to the increased
adoption of electric equipment’s in future will further aggravate
this problem and hence looking into advanced machine
learning-based methods are needed for more accurate
forecasting.
The current solution leverages a statistical approach
using the weighted average method to determine the
energy consumption forecast. The limitations around
the accurate forecast method is resulting in substantial
main absolute deviation and thus leading to higher
operating cost aka penalties from energy supplier
sources.
Key observations:
Subjectivity: The weights assigned to each data
point can sometimes be subjective.
Increased deviation: They can be less stable
over time and more sensitive to changes in
individual data points.
Needs manual intervention: If there is a lot of
variance, the new demand is manually adjusted
based on energy consumption of the latest slots.
Dynamic factors are not considered: In the
current weighted average solution there is no
dynamic variables considered that would influence
the energy consumption in near real-time manner.
ThoughtsWin Systems recommends developing machine learning-based solutions that would factor in all the historic consumption data to train AI/ML models and incorporate key dynamic factors that have direct impacts towards energy consumption. The new solution will have the ability to update forecasts based on previous blocks’ data/metrics (Machine Learning will be triggered for each contiguous block and forecasts can be regenerated).
Some key dynamic factors for model development consideration in addition to historical energy consumption data:
Dynamic nature of equipment operations/schedules
Composition of equipment
Location and elevation of equipment
Local weather
Variation in power consumption of electric equipment
Increased Employee Satisfaction (Due to less manual intervention)
Serving Public & Private Clients
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Satisfied Clients
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Years in Business
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