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ABSTRACT
Energy consumption prediction is an activity that helps energy supply firms to adjust
to different behavior. Knowing the behavior of their customers to adapt their rates to
consumption or knowing the intervals in which it will cause a greater demand for
energy and having planned the adaptation of supply chains are just a few of the
actions that organizations can do.
Forecasting is a technique for predicting future values of a time series based on
present and previous values. Forecasting future consumptions based on various data
and information available about customer behavior is known as load forecasting.
Short-term load forecasting is useful for estimating load flows and avoiding overloads.
Timely implementations of such decisions lead to improvement of network reliability
and to reduced occurrences of equipment failures or blackouts. The review made
allowed us to observe that the data set using the Linear Regression and SVR has the
best results provided.
Both schemes used similar parameters for training and testing simulations. After 10-
time cross training validation and five averaged repeated runs with random
permutation per data splitting, the proposed classifier shows better computation speed
and higher classification accuracy than the conventional method.
However, when the number of its desired levels increases, its prediction accuracy
seems to decrease and approaches the accuracy of the conventional method. The
developed energy level prediction, which is computationally inexpensive and has a
good classification performance, can serve as an alternative forecasting scheme.
