Abstract:
The objective is to generate household electricity load-profiles by predicting device-wise
electricity usage patterns using the time-series data, to trace the disutility of electricity. Further,
the emphasis lies on how the socio-economic parameters of a household can combine with the
time-series data to aid in better prediction. This is a comparative study representing a bottom-up
model, with input granularity set to 10-min cycle power of 5 everyday household devices along
with their associated timestamps as the building blocks and predicts, whether a device would be
switched on or off at a given point in time.
The model is trained and validated on REFIT dataset, comprising of 20 houses along with the
socio-economic features of each house. For comparison purpose, two datasets are created, with
and without the socio-economic parameters. Results point towards the impact of socio-economic
features and how they improved the prediction accuracy by a fine margin for each device, leading
towards promising high-resolution electricity load profiles. Using the socio-economic features, we
were able to predict the state of a device up to an accuracy of 97%, whereas without these features
the accuracy was 76%.
