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This paper addresses the challenges of load forecasting
that occur due to the complex nature of load in different
predicting horizons and as well as the total consumption within
these horizons. It is not often easy to accurately fit the several
complex factors that are faced with demand for electricity into
the predicting models. More so, due to the dynamic nature of
these complex factors (i.e., temperature, humidity and other
factors that influence consumption), it is difficult to derive an
accurate demand forecast based on these parameters. As a
consequence, a model that uses hourly electricity loads and
temperature data to forecast the next hourly loads is proposed.
The model is based on modified entropy mutual information
based feature selection to remove irrelevancy and redundancy
from the dataset. Conditional restricted Boltzmann machine
(CRBM) is investigated to perform load forecasting; accuracy
and convergence are improved to reduce the CRBM’s forecast
error via a Jaya based meta-heuristic optimization algorithm.
The proposed model is implemented on the publicly available
dataset of GEFCom2012 of the US utility. Comparative analysis
is carried out on an existing accurate, fast converging shortterm
load forecasting (AFC-STLF) model since it has a similar
architecture to the proposed model. Simulation results confirm
that the proposed model improves the accuracy up to 56.32% as
compared to 43.67% of AFC-STLF. Besides, the proposed model
reduces the average execution time up to 53.87% as compared
to 46.12% of AFC-STLF.
International Conference on Cyber Security and Computer Science
ICONCS
Omaji Samuel
Nadeem Javaid
Asma Rafique