Machine Learning Approach for Detecting Anomalies in Industrial Energy Consumption

Industrial energy usage is at the heart of
increasing efficiency, reducing costs, and promoting
proper usage of sustainable energy. When there is a
deviation in energy usage, it may indicate issues such as
machine failures, leaks, or inefficient usage. Detecting
these issues early can help reduce machine downtime
and maintenance costs. This project aims to create an
anomaly detection system using machine learning
algorithms based on a simulated dataset, since acquiring
live data from industries is difficult. We use algorithms
like Isolation Forest, One-Class SVM, and Gradient
Boosting to detect anomalies in energy usage. We
analyze time-series features like power usage, voltage,
and other signals to detect anomalies. A dashboard is
also provided for easy visualization of energy usage and
anomalies. The results show that this method is helpful
for predictive maintenance and increasing efficiency in
industries. To sum up, machine learning can be very
helpful in anomaly detection in this domain.