DSpace Colección :
https://hdl.handle.net/11000/497
2024-03-28T10:36:02ZReduction of Computational Burden and Accuracy Maximization in Short-Term Load Forecasting
https://hdl.handle.net/11000/31227
Título : Reduction of Computational Burden and Accuracy Maximization in Short-Term Load Forecasting
Autor : Candela Esclapez, Alfredo; López García, Miguel; VALERO, SERGIO; Senabre, Carolina
Resumen : Electrical energy is consumed at the same time as it is generated, since its storage is unfeasible. Therefore, short-term load forecasting is needed to manage energy operations. Due to better energy management, precise load forecasting indirectly saves money and CO2 emissions. In Europe, owing to directives and new technologies, prediction systems will be on a quarter-hour basis, which will reduce computation time and increase the computational burden. Therefore, a predictive system may not dispose of sufficient time to compute all future forecasts. Prediction systems perform calculations throughout the day, calculating the same forecasts repeatedly as the predicted time approaches. However, there are forecasts that are no more accurate than others that have already been made. If previous forecasts are used preferentially over these, then computational burden will be saved while accuracy increases. In this way, it will be possible to optimize the schedule of future quarter-hour systems and fulfill the execution time limits. This paper offers an algorithm to estimate which forecasts provide greater accuracy than previous ones, and then make a forecasting schedule. The algorithm has been applied to the forecasting system of the Spanish electricity operator, obtaining a calculation schedule that achieves better accuracy and involves less computational burden. This new algorithm could be applied to other forecasting systems in order to speed up computation times and to reduce forecasting errors.2024-02-07T13:36:27ZUse of Available Daylight to Improve Short-Term Load Forecasting Accuracy
https://hdl.handle.net/11000/31226
Título : Use of Available Daylight to Improve Short-Term Load Forecasting Accuracy
Autor : López García, Miguel; VALERO, SERGIO; Sans Treserras, Carlos; Senabre, Carolina
Resumen : This paper introduces a new methodology to include daylight information in short-term
load forecasting (STLF) models. The relation between daylight and power consumption is obvious
due to the use of electricity in lighting in general. Nevertheless, very few STLF systems include
this variable as an input. In addition, an analysis of one of the current STLF models at the Spanish
Transmission System Operator (TSO), shows two humps in its error profile, occurring at sunrise
and sunset times. The new methodology includes properly treated daylight information in STLF
models in order to reduce the forecasting error during sunrise and sunset, especially when daylight
savings time (DST) one-hour time shifts occur. This paper describes the raw information and the
linearization method needed. The forecasting model used as the benchmark is currently used at
the TSO’s headquarters and it uses both autoregressive (AR) and neural network (NN) components.
The method has been designed with data from the Spanish electric system from 2011 to 2017 and
tested over 2018 data. The results include a justification to use the proposed linearization over other
techniques as well as a thorough analysis of the forecast results yielding an error reduction in sunset
hours from 1.56% to 1.38% for the AR model and from 1.37% to 1.30% for the combined forecast.
In addition, during the weeks in which DST shifts are implemented, sunset error drops from 2.53%
to 2.09%.2024-02-07T13:36:10ZClassification of Special Days in Short-Term Load Forecasting: The Spanish Case Study
https://hdl.handle.net/11000/31218
Título : Classification of Special Days in Short-Term Load Forecasting: The Spanish Case Study
Autor : López García, Miguel; Sans Tresserras, Carlos; VALERO, SERGIO; Senabre, Carolina
Resumen : Short-Term Load Forecasting is a very relevant aspect in managing, operating or
participating an electric system. From system operators to energy producers and retailers knowing
the electric demand in advance with high accuracy is a key feature for their business. The load series
of a given system presents highly repetitive daily, weekly and yearly patterns. However, other
factors like temperature or social events cause abnormalities in this otherwise periodic behavior. In
order to develop an effective load forecasting system, it is necessary to understand and model these
abnormalities because, in many cases, the higher forecasting error typical of these special days is
linked to the larger part of the losses related to load forecasting. This paper focuses on the effect that
several types of special days have on the load curve and how important it is to model these
behaviors in detail. The paper analyzes the Spanish national system and it uses linear regression to
model the effect that social events like holidays or festive periods have on the load curve. The results
presented in this paper show that a large classification of events is needed in order to accurately
model all the events that may occur in a 7-year period.2024-02-07T13:32:09ZEmpirical Comparison of Neural Network and Auto-Regressive Models in Short-Term Load Forecasting
https://hdl.handle.net/11000/31194
Título : Empirical Comparison of Neural Network and Auto-Regressive Models in Short-Term Load Forecasting
Autor : López, Miguel; Sans, Carlos; VALERO, SERGIO; Senabre, Carolina
Resumen : Artificial Intelligence (AI) has been widely used in Short-Term Load Forecasting (STLF)
in the last 20 years and it has partly displaced older time-series and statistical methods to a second
row. However, the STLF problem is very particular and specific to each case and, while there are
many papers about AI applications, there is little research determining which features of an STLF
system is better suited for a specific data set. In many occasions both classical and modern methods
coexist, providing combined forecasts that outperform the individual ones. This paper presents a
thorough empirical comparison between Neural Networks (NN) and Autoregressive (AR) models as
forecasting engines. The objective of this paper is to determine the circumstances under which each
model shows a better performance. It analyzes one of the models currently in use at the National
Transport System Operator in Spain, Red Eléctrica de España (REE), which combines both techniques.
The parameters that are tested are the availability of historical data, the treatment of exogenous
variables, the training frequency and the configuration of the model. The performance of each model
is measured as RMSE over a one-year period and analyzed under several factors like special days
or extreme temperatures. The AR model has 0.13% lower error than the NN under ideal conditions.
However, the NN model performs more accurately under certain stress situations.2024-02-07T08:03:32ZNumerical sound prediction model to study tyre impact noise
https://hdl.handle.net/11000/31164
Título : Numerical sound prediction model to study tyre impact noise
Autor : Campello Vicente, Héctor; Campillo Davo, Nuria; Peral Orts, Ramón; Fabra Rodríguez, Miguel; Abellán López, David
Resumen : Impact noise is one of the mechanisms of vibratory origin that constitutes tyre/road interaction noise.
When assessing a vehicle as a noise source, the impact sound mechanism is especially significant when
obstacles are present on the driving surface. This document aims to enhance understanding of the impact
noise phenomenon by presenting a two-step numerical model for studying the sound propagation of an
accelerated tyre impacting a flat, rigid, and reflective surface: Firstly, a dynamic analysis of the contact is
performed using the Finite Element Method. Then, the Boundary Element Method is used to perform an
acoustic analysis with the vibration of the tyre surface as the sound source. The model has been successfully
validated through a drop-test, where a tyre/rim assembly is dropped onto a ground surface. The validation
was determined by comparing the predicted Sound Pressure Level measurements to those
obtained from a circular microphone structure at various points during the drop-test.2024-02-06T16:53:19ZInverse transfer path analysis, a different approach to shorten time in NVH assessments
https://hdl.handle.net/11000/31163
Título : Inverse transfer path analysis, a different approach to shorten time in NVH assessments
Autor : Campello Vicente, Héctor; Campillo Davo, Nuria; Peral Orts, Ramón; Cervantes Madrid, Ginés
Resumen : This paper presents the design and implementation of a simplified method, based on the transmissibility
concept, for a noise path assessment, which allows a rapid and accurate analysis. The Inverse Transfer
Path Analysis aims to assess, and determine, the contribution of the critical paths, which are transmitting
structure-borne noises and vibrations, from the vehicle’s vibration sources to the driver’s ear.
The cabin noise transfer function, from the involved attachment points and directions, can be simultaneously
obtained by applying an impulsive noise source inside the cabin. This approach avoids the use of
other time consuming classic procedures.
The proposed methodology includes two types of tests, static condition tests in a semi-anechoic chamber
and operational tests on a roller bench. The results assessment comprises the analysis of the noise
contribution of each path, depending on the frequency and the vehicle speed range.
This publication introduces a novel NVH method proposed to study and identify noise transfer paths in
a car structure. The theoretical approach of the methodology, practical implementation, and obtained
results, are described in this work, as well as a methodology validation, to evidence the suitability of
the proposed method.2024-02-06T16:52:15ZGear sound model for an approach of a Mechanical Acoustic Vehicle Alerting System (MAVAS) to increase EV’s detectability
https://hdl.handle.net/11000/31158
Título : Gear sound model for an approach of a Mechanical Acoustic Vehicle Alerting System (MAVAS) to increase EV’s detectability
Autor : Campello Vicente, Héctor; Campillo Davo, Nuria; Peral Orts, Ramón; Fabra Rodríguez, Miguel
Resumen : Hybrid-electric and electric vehicles significantly reduce noise road emissions. This noise mitigation also
causes a reduction in the sound detectability and therefore it increases the potential of causing accidents.
A suitable solution arises with the Acoustic Vehicle Alerting Systems (AVAS) emitting a warning sound to
alert pedestrians about the presence of a silent vehicle. This paper details an acoustic prediction model
capable of simulating the sound produced by a pair of spur dry gears used as a Mechanical Acoustic
Vehicle Alerting System (MAVAS). This proposal that tries to reproduce a sound closer to the mechanical
sound of a conventional vehicle would be used as an alternative to existing systems. The prediction
model developed is validated and consists in two consecutive parts: first, a dynamic model studies the
rattle of the gears, then, an analytical model reproduces the sound of each impact of the gear teeth.
This sound model makes it possible to characterize a proposed gear combination of the MAVAS, verifying
its compliance with the European legislation.2024-02-06T12:24:41ZAssessing the Impact of Attendance Modality on the Learning Performance of a Course on Machines and Mechanisms Theory
https://hdl.handle.net/11000/31157
Título : Assessing the Impact of Attendance Modality on the Learning Performance of a Course on Machines and Mechanisms Theory
Autor : Campello Vicente, Héctor; Campillo Davo, Nuria; Valiente, David; Velasco Sánchez, Emilio; Rodríguez Mas, Fernando
Resumen : University education approaches related to the field of science, technology, engineering
and mathematics (STEM), have generally particularized on teaching activity and learning programs
which are commonly understood as reoriented lessons that fuse theoretic concepts interweaved with
practical activities. In this context, team work has been widely acknowledged as a means to conduct
practical and hands-on lessons, and has been revealed to be successful in the achievement of exercise
resolution and design tasks. Besides this, methodologies sustained by ICT resources such as online
or blended approaches, have also reported numerous benefits for students’ active learning. However,
such benefits have to be fully validated within the particular teaching context, which may facilitate
student achievement to a greater or lesser extent. In this work, we analyze the impact of attendance
modalities on the learning performance of a STEM-related course on “Machines and Mechanisms
Theory”, in which practical lessons are tackled through a team work approach. The validity of the
results is reinforced by group testing and statistical tests with a sample of 128 participants. Students
were arranged in a test group (online attendance) and in a control group (face-to-face attendance)
to proceed with team work during the practical lessons. Thus, the efficacy of distance and in situ
methodologies is compared. Moreover, additional variables have also been compared according to
the historical record of the course, in regards to previous academic years. Finally, students’ insights
about the collaborative side of this program, self-knowledge and satisfaction with the proposal have
also been reported by a custom questionnaire. The results demonstrate greater performance and
satisfaction amongst participants in the face-to-face modality. Such a modality is prooven to be
statistically significant for the final achievement of students in detriment to online attendance.2024-02-06T12:24:07ZAn alternative Drum test method to UNECE Regulation 117 for measuring tyre/road noise under laboratory controlled conditions
https://hdl.handle.net/11000/31156
Título : An alternative Drum test method to UNECE Regulation 117 for measuring tyre/road noise under laboratory controlled conditions
Autor : Campello Vicente, Héctor; Clar García, David; Sánchez Lozano, Miguel; Velasco Sánchez, Emilio
Resumen : Tyre/road sound emissions have been proved to be the main source of noise caused by road traffic when
traveling at medium and high speeds (Sandberg and Ejsmont, 2002). Tyre/road noise has been widely
studied among the last decades. However, an important part of this research has been focused, mainly,
on track tests. Different track or road methods have been developed for measurement of tyre/road sound
emissions. The most important ones are the Coast-By, the Close-Proximity, the Statistical Pass-By or the
Controlled Pass-By methods. Among all of them, the Coast-By method has been raised in Europe as standard
method concerning the approval of tyres with regard to tyre/road sound emissions as preconized in
UNECE Regulation 117 (2007)[2]. However, all the above mentioned methods have several disadvantages
such as the influence of environmental factors, the different results that can be obtained depending on
the test track or the vehicle upon which the tests are carried out, the lack of repeatability or, the most
important aspect, which is the limitation of the measured magnitude, the sound pressure level.
A new methodology (Clar-Garcia et al., 2016) based on drum tests and the ISO 3744 (1994), which was
developed in order to avoid these limitations, has been proved to be comparable to the Coast-By (CB)
method. This paper describes how different tyres have been tested according to both the CB and the
new Alternative Drum test method (A-DR) while their results have been compared. In order to be able
to carry out this comparison, as the measured magnitudes and test conditions differ widely from one test
to another, the standardised ISO 9613 sound propagation method (ISO 9613-2, 1996) has been applied to
obtain the sound pressure value at 7.5 m from the sound power level of a tyre measured under
laboratory-controlled conditions when rolling against a drum. Results have shown that both methods
are not only comparable but also have remarkably similar sound spectra and, for that reason, the new
methodology based on drum tests can be used in order to obtain tyre/road noise emission approved
values.2024-02-06T12:23:26ZAn alternative close-proximity test to evaluate sound power level emitted by a rolling tyre
https://hdl.handle.net/11000/31155
Título : An alternative close-proximity test to evaluate sound power level emitted by a rolling tyre
Autor : Campello Vicente, Héctor; Campillo Davo, Nuria; Peral Orts, Ramón; Velasco Sánchez, Emilio
Resumen : The noise emission of a rolling tyre is produced by different physical mechanisms generated during the
tyre-road interaction, being the main noise source of a vehicle when driving at high speeds. Diverse measurement
methods can be found in the literature to assess the rolling noise emission. In that sense, the
close-proximity (CPX) method allows to evaluate tyre/road sound level with at least two microphones
operating in the close field of the test tyre. This paper presents a new methodology, based on the CPX
method, which allows assessing the sound power level of the rolling tyre by introducing some changes
in the traditional close-proximity test. The methodology (named A-CPX) has been analytically and experimentally
validated, and is finally used to obtain the total tyre/road sound power level emitted by the
whole set of tyres of a vehicle.2024-02-06T12:22:26Z