@mastersthesis {pub4632,
	title = {Electric load time series forecasting and relative predictions on simulation model },
	author = {Francesco Romagnoli},
	year = {2021},
	abstract = {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 
costs. 
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. },
	publisher = {UNIVERSITA{\textquoteright} POLITECNICA DELLE MARCHE, ANCONA, IT},
	booktitle = {UNIVERSITA{\textquoteright} POLITECNICA DELLE MARCHE, ANCONA, IT}
}