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Recently big data analytics are gaining popularity in
the energy management systems (EMS). The EMS are responsible
for controlling, optimization and managing the energy market
operations. Energy consumption forecasting plays a key role
in EMS and helps in generation planning, management and
energy conversation. A large amount of data is being collected
by the smart meters on daily basis. Big data analytics can
help in achieving insights for smart energy management. Several
prediction methods are proposed for energy consumption
forecasting. This study explores the state-of-the-art forecasting
methods. The studied forecasting methods are classified into
two major categories: (i) univariate (time series) forecasting
models and (ii) multivariate forecasting models. The strengths
and limitations of studied methods are discussed. Comparative
anlysis of these methods is also done in this survey. Furthermore,
the forecasting techniques are reviewed from the aspects of big
data and conventional data. Based on this survey, the gaps in
the existing research are identified and future directions are
described.
International Conference on Cyber Security and Computer Science
ICONCS
Sana Mujeeb
Nadeem Javaid
Sakeena Javaid
Asma Rafique
Manzoor Ilahi