How can you detect anomalies in power system data using machine learning algorithms?

In its simplest form, power usage data is simply a time stamp and a meter reading, in order to use this data you most likely need to transform this data into a matrix with times and dates as columns and rows, storing the power used during that time slot. You'll then need to develop a stragergy for filling in any missing data. Once this is done you will be able to visualise your power usage and to start to profile use, by time, day, week and month which is the first step to modelling use.

There are several machine learning algorithms that have been found to be effective in detecting anomalies in power system data. Some of the best algorithms include: 1. Isolation Forest: This algorithm is effective in isolating anomalies by creating random forests and isolating the anomalies in the data. 2. Autoencoders: Autoencoders are a type of neural network that can be used for anomaly detection by reconstructing input data and identifying anomalies based on the reconstruction error. The output levels that can help find anomalies in energy optimization include identifying abnormal patterns in power consumption, voltage levels, frequency deviations, and equipment performance.

At the most basic level you can use simple timeseries analysis algorithms to establish whether there are underlying trends, is your usage increasing over time, which might indicate a power problem. You could also apply simple linear regressions to indicate what normal usage might look like, which could then be used to identify outliers, for you to investigate. Similarly you could use clustering or classification models to identify normal versus abnormal use. The main problem with any form of machine learning on energy use / power systems would be that your not likely to have a lot of features within your dataset to analyse.

In my experience, model evaluation can be difficult, not least because in most situations there are lots of factors that affect energy use / power systems. Its often hard to establish what's noise and what's model performance.

Generally i found data visualisation to be a very effective way to alayse anomolies in power systems. In particular comparing usage on any given day to usage the same day last week, last month and last year worked well, obviously looking at lots of datapoints becomes very time consuming if for example you need to look at daily use for each day over a year or for 100 sensors. I found it really useful to write a short script to iterate over each day looking at each time point to identify if usage was within 10% of the rolling average for that specific day/time point , flag up any day where more that 5 time points were 10% above or below the average. The script then visualize where these anomoloies occured.