Electricity theft results in high ﬁnancial losses for several countries such as the United States ($6 billion/year) and India ($17 billion/year) . Other developing countries lose almost 50% of their electricity revenue due to theft . Modern smart grids rely on advanced metering infrastructure (AMI) networks for monitoring and billing purposes. However, such an approach suffers from electricity theft.
What it does
Here we present an approach to identify the suspect customers, using the customer power usage pattern. The Electricity Distribution companies through local region-wise substations to detect fraudulent customers. Consumers can also use their power usage patterns to keep a check on power wastage and hence reducing electricity bills.
How I built it
We Built it with the help of Machine Learning Algorithms like Neural Networks, K- Means Clustering in Python with implementation on Jupyter Notebook
Challenges I ran into
Dealing with bogus data that have different sequential patterns and dealing with processing it.
Accomplishments that I'm proud of
Successfully Implemented 300-400 Lines of Code with difficult pre-processing
What I learned
Vast Complexity of ML Algorithms and their specific uses.
What's next for MavericsDTU
We plan to become Data Scientist in upcoming years and will be looking for job opportunities in the area.