"Electric load time series forecasting and relative predictions on simulation model ",
UNIVERSITA’ POLITECNICA DELLE MARCHE, ANCONA, IT, 2021.
Time series forecasting is an important area of machine learning because there
are so many prediction problems that involve a time component. A normal
machine learning dataset is a collection of observations, while a time series
dataset adds an explicit order dependence between observations, represented by
time dimension. This additional dimension is both a constraint and a structure that
provides a source of additional information.
In this topic, electric load time series forecasting represent a crucial task in the
next future. The progressive replacement of fossil fuels in a wide energy demand
case with the using of electricity, like cooling or heating of buildings and transport
with the spreading of electric vehicles, make electric power demand predicitions
fundamental in order to develop a smart grid structure at any level, from electric
industry to facilities and private houses. Electricity load forecasting allows to make
this exponentially growing of using of electricity energy sustainable, with smart
management of the energy resources to cover the electric demand and savings
The objective of the research is to investigate different time series forecasting
algorithms applied to electric consumption of a facility, including the estimation
of some confidence bounds, such as minimal and maximal load values as well as
the expected load standard deviation.
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