Abstract:
Forecasting energy consumption for short-term to make better decisions is becoming one
of the basic needs for all industries. For this purpose many Machine Learning techniques
are being used. More prominent and widely used techniques are Artificial Neural
Networks and Grey Prediction Models. This research targets to benchmark both
techniques for same system in order to provide the guidelines for selecting a suitable
Machine Learning technique for short-term energy consumption forecasting. In this
regard the energy consumption dataset from Remote Monitoring System installed at a
facility is acquired and popular computation models from both techniques are
implemented. An experiment with different variations is designed in order to explore
multiple aspects of capabilities for both techniques. From experimentation results it is
observed that the Artificial Neural Network is more suited technique if sufficient data for
training is available as the case in this research. It is also observed that the performance
of data is comparable while using small amount of data, which suggests that in absence
of sufficient amount of data for training Grey Prediction Models should be preferred.
