In thelast
special issue on STLF-2019 by MDPI, five of the papers published werein the top 10% of the
most cited articles in the journal
"Energies" (papers published
in the special issues STLF 2019 and 2020).
This is explained by the high quality of
papers and a detailed
review of each submission, to guarantee
the publication of top level STLF papers.
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NOW CLOSED !!!!!
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************************************Paper:
September 30, 2024
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Acknowledgments: Thanks to MDPI editorial
staff and editors, and MDPI Journals for their
continuous support in the management of this new
Topic issue (and previous special issues STLF-2019
and STLF-2021)
And also thanks to the Spanish Government and the
European Union, FEDER-EURDF funds, for their support
in previous research projects of the team
(2007-2024).
STLF "Topic" Information:
It is well known
that short-term load forecasting (STLF) plays a key role
in the formulation of economic, reliable, and secure
operating strategies for power systems (planning,
scheduling, maintenance, and control processes, among
others), and this topic has been an important issue for
several decades. However, there is
still much progress to be made in this field. The
deployment of enabling technologies (e.g., smart meters
and sub-metering) has made high granular data
available for many customer segments and many tasks—for
instance, it has made load forecasting tasks feasible at
several demand aggregation levels. The first challenge in
this area is the improvement of STLF models and their
performance at new demand aggregation levels. Moreover,
the increasing inclusion of renewable energies (wind and
solar power) in the power system, and the necessity of including more
flexibility through demand response initiatives, have
introduced greater uncertainties, creating new challenges
for a more
accurate STLF in future power systems, considering that new balance
responsibilities of demand actors involve new economic
impacts in the demand-side actors.Other relevant issues are net demand
forecasting in “prosumers” (i.e., the integrated or
disaggregated forecast of demand and renewable
generation), or the consideration of forecasting of demand
by end-uses (i.e., both for flexible and non-flexible
demand) in large customers or in aggregated customers
catalyzed by sub-metering or non-intrusive load monitoring
.
Many
techniques have been proposed for STLF, including
traditional statistical models (such as SARIMA, ARMAX,
exponential smoothing, linear and non-linear models, etc.)
and artificial intelligence techniques (such as fuzzy
regression, artificial neural networks, support vector
regression, tree-based regression, ensemble methods,
stacked methods, etc.). Furthermore, distribution planning
needs, as well as grid modernization, have initiated the
development of hierarchical load forecasting. Analogously,
the need to face new uncertainty sources in the power system has
given more importance to probabilistic load forecasting in
recent years.
This Topic is concerned with
both fundamental research on STLF methodologies and its practical application to power systems, aiming at exploring the
challenges that will be faced by a more distributed power
system in the future.
All submitted contributions must
be based on the rigorous examination of the mentioned
approaches and the
demonstration of a theoretically sound
framework; submissions lacking such a scientific
approach are discouraged. It is recommended that
existing/presented approaches are validated using real
practical applications.
Topics editors:
Prof. Dr. María Carmen Ruiz-Abellón, Prof. Dr.
Luis Alfredo Fernández-Jiménez and Prof. Antonio
Gabaldón
Keywords:short-term
load forecasting and distributed energy resources, short-term load
forecasting and demand aggregation levels, short-term net
demand forecasting, statistical
forecasting models (SARIMA, ARMAX, exponential smoothing,
linear and non-linear regression...),
artificial neural networks (ANNs), fuzzy
regression models,
tree-based regression methods,
stacked and ensemble methods,
evolutionary algorithms, deep
learning architectures,
support vector regression (SVR),
robust load forecasting,
hierarchical and probabilistic forecasting,
hybrid and combined models,
renewable generation forecasting,
End-Use demand forecasting.