Browsing by Keyword "Machine learning"
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Item AI-based medical e-diagnosis for fast and automatic ventricular volume measurement in patients with normal pressure hydrocephalus(2022-02-24) Zhou, Xi; Ye, Qinghao; Yang, Xiaolin; Chen, Jiakun; Ma, Haiqin; Xia, Jun; Del Ser, Javier; Yang, Guang; IABased on CT and MRI images acquired from normal pressure hydrocephalus (NPH) patients, using machine learning methods, we aim to establish a multimodal and high-performance automatic ventricle segmentation method to achieve an efficient and accurate automatic measurement of the ventricular volume. First, we extract the brain CT and MRI images of 143 definite NPH patients. Second, we manually label the ventricular volume (VV) and intracranial volume (ICV). Then, we use the machine learning method to extract features and establish automatic ventricle segmentation model. Finally, we verify the reliability of the model and achieved automatic measurement of VV and ICV. In CT images, the Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), Pearson correlation, and Bland–Altman analysis of the automatic and manual segmentation result of the VV were 0.95, 0.99, 0.99, and 4.2 ± 2.6, respectively. The results of ICV were 0.96, 0.99, 0.99, and 6.0 ± 3.8, respectively. The whole process takes 3.4 ± 0.3 s. In MRI images, the DSC, ICC, Pearson correlation, and Bland–Altman analysis of the automatic and manual segmentation result of the VV were 0.94, 0.99, 0.99, and 2.0 ± 0.6, respectively. The results of ICV were 0.93, 0.99, 0.99, and 7.9 ± 3.8, respectively. The whole process took 1.9 ± 0.1 s. We have established a multimodal and high-performance automatic ventricle segmentation method to achieve efficient and accurate automatic measurement of the ventricular volume of NPH patients. This can help clinicians quickly and accurately understand the situation of NPH patient’s ventricles.Item Award Price Estimator for Public Procurement Auctions Using Machine Learning Algorithms: Case Study with Tenders from Spain(2021) Rodriguez, Manuel J.Garcia; Montequin, Vicente Rodriguez; Aranguren Ubierna, Andoni; Hermida, Roberto Santana; Araujo, Basilio Sierra; Jauregi, Ana Zelaia; IAThe public procurement process plays an important role in the efficient use of public resources. In this context, the evaluation of machine learning techniques that are able to predict the award price is a relevant research topic. In this paper, the suitability of a representative set of machine learning algorithms is evaluated for this problem. The traditional regression methods, such as linear regression and random forest, are compared with the less investigated paradigms, such as isotonic regression and popular artificial neural network models. Extensive experiments are conducted based on the Spanish public procurement announcements (tenders) dataset and employ diverse error metrics and implementations in WEKA and Tensorflow 2.Item Big data in road transport and mobility research(Elsevier, 2017-01-01) Campos-Cordobés, Sergio; del Ser, Javier; Laña, Ibai; Olabarrieta, Ignacio Iñaki; Sánchez-Cubillo, Javier; Sánchez-Medina, Javier J.; Torre-Bastida, Ana I.; LABORATORIO DE TRANSFORMACIÓN URBANA; SMART_TRANSPORT; IA; Tecnalia Research & Innovation; HPAUbiquitous computing has changed the acquisition of mobility data, with two aspects contributing: the high penetration rate and the ability to capture and share information on a continuous basis. This applies to geolocation information, operational mobile phone data, and also, social network crowdsourced information. Additionally, under the umbrella of the Internet of Things trend, the deployment of the Connected Vehicle (Car-as-a-sensor) concept, supported by advanced V2X communications, provides massive data volume. For all these cases, data are open to never before seen opportunities to analyze and predict individual and aggregated mobility patterns. Big Data refers to the processsing capabilities of such an explosion in the amount, quality, and heterogeneity of available data. This chapter will review the most relevant data sources, introduce the underlying techniques supporting the BigData paradigm and, finally, provide a list of some relevant applications in the transport and mobility domain.Item A cognitive robotic ecology approach to self-configuring and evolving AAL systems(2015-10-01) Dragone, Mauro; Amato, Giuseppe; Bacciu, Davide; Chessa, Stefano; Coleman, Sonya; Di Rocco, Maurizio; Gallicchio, Claudio; Gennaro, Claudio; Lozano, Hector; Maguire, Liam; McGinnity, Martin; Micheli, Alessio; O׳Hare, Gregory M.P.; Renteria, Arantxa; Saffiotti, Alessandro; Vairo, Claudio; Vance, P.; O'Hare, Gregory M.P.; Medical Technologies; Robótica MédicaRobotic ecologies are systems made out of several robotic devices, including mobile robots, wireless sensors and effectors embedded in everyday environments, where they cooperate to achieve complex tasks. This paper demonstrates how endowing robotic ecologies with information processing algorithms such as perception, learning, planning, and novelty detection can make these systems able to deliver modular, flexible, manageable and dependable Ambient Assisted Living (AAL) solutions. Specifically, we show how the integrated and self-organising cognitive solutions implemented within the EU project RUBICON (Robotic UBIquitous Cognitive Network) can reduce the need of costly pre-programming and maintenance of robotic ecologies. We illustrate how these solutions can be harnessed to (i) deliver a range of assistive services by coordinating the sensing & acting capabilities of heterogeneous devices, (ii) adapt and tune the overall behaviour of the ecology to the preferences and behaviour of its inhabitants, and also (iii) deal with novel events, due to the occurrence of new user's activities and changing user's habits.Item Combining physics-based and data-driven methods in metal stamping(2024) Abanda, Amaia; Arroyo, Amaia; Boto, Fernando; Esteras, Miguel; IA; PROMETAL; FACTORYThis work presents a methodology for combining physical modeling strategies (FEM), machine learning techniques, and evolutionary algorithms for a metal stamping process to ensure process quality during production. Firstly, a surrogate model or metamodel is proposed to approximate the behavior of the simulation model for different outputs in a fraction of time. Secondly, based on the surrogate model, multiple soft sensors that estimate different quality measures of the stamped part departing from the draw-ins are proposed, which enables their integration into the process. Lastly, evolutionary algorithms are used to estimate the latent blank characteristics and for the prescriptions of process parameters that maximize the quality of the stamped part. The obtained numerical results are promising, with relative errors around 2 2% in most cases and outperforming a naive method. This methodology aims to be a decision support system that moves towards zero defects in the stamping process from the process conception phase.Item Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0(2019-10) Diez-Olivan, Alberto; Del Ser, Javier; Galar, Diego; Sierra, Basilio; Tecnalia Research & Innovation; IAThe so-called “smartization” of manufacturing industries has been conceived as the fourth industrial revolution or Industry 4.0, a paradigm shift propelled by the upsurge and progressive maturity of new Information and Communication Technologies (ICT) applied to industrial processes and products. From a data science perspective, this paradigm shift allows extracting relevant knowledge from monitored assets through the adoption of intelligent monitoring and data fusion strategies, as well as by the application of machine learning and optimization methods. One of the main goals of data science in this context is to effectively predict abnormal behaviors in industrial machinery, tools and processes so as to anticipate critical events and damage, eventually causing important economical losses and safety issues. In this context, data-driven prognosis is gradually gaining attention in different industrial sectors. This paper provides a comprehensive survey of the recent developments in data fusion and machine learning for industrial prognosis, placing an emphasis on the identification of research trends, niches of opportunity and unexplored challenges. To this end, a principled categorization of the utilized feature extraction techniques and machine learning methods will be provided on the basis of its intended purpose: analyze what caused the failure (descriptive), determine when the monitored asset will fail (predictive) or decide what to do so as to minimize its impact on the industry at hand (prescriptive). This threefold analysis, along with a discussion on its hardware and software implications, intends to serve as a stepping stone for future researchers and practitioners to join the community investigating on this vibrant field.Item Data-driven prognostics using a combination of constrained K-means clustering, fuzzy modeling and LOF-based score(2017-06-07) Diez-Olivan, Alberto; Pagan, Jose A.; Sanz, Ricardo; Sierra, Basilio; Tecnalia Research & InnovationToday, failure modes characterization and early detection is a key issue in complex assets. This is due to the negative impact of corrective operations and the conservative strategies usually put in practice, focused on preventive maintenance. In this paper anomaly detection issue is addressed in new monitoring sensor data by characterizing and modeling operational behaviors. The learning framework is performed on the basis of a machine learning approach that combines constrained K-means clustering for outlier detection and fuzzy modeling of distances to normality. A final score is also calculated over time, considering the membership degree to resulting fuzzy sets and a local outlier factor. Proposed solution is deployed in a CBM+ platform for online monitoring of the assets. In order to show the validity of the approach, experiments have been conducted on real operational faults in an auxiliary marine diesel engine. Experimental results show a fully comprehensive yet accurate prognostics approach, improving detection capabilities and knowledge management. The performance achieved is quite high (precision, sensitivity and specificity above 93% and κ=0.93), even more so given that a very small percentage of real faults are present in data.Item Deep evolutionary modeling of condition monitoring data in marine propulsion systems(2019-10-01) Diez-Olivan, Alberto; Pagan, Jose A.; Sanz, Ricardo; Sierra, Basilio; Tecnalia Research & InnovationIn many complex industrial scenarios where condition monitoring data are involved, data-driven models can highly support maintenance tasks and improve assets’ performance. To infer physical meaningful models that accurately characterize assets’ behaviors across a wide range of operating conditions is a difficult issue. Usually, data-driven models are in black-box format, accurate but too complex to intelligibly explain the inherent physics of the process and lacking in conciseness. This study presents a deep evolutionary-based approach to optimally model and predict physical behaviors in industrial assets from operational data. The evolutionary modeling process is combined with long short-term memory networks, which are trained on estimations made by the evolutionary physical model and then used to predict sequences of data over a number of time steps. The likelihood of behaviors of interest is assessed by means of the resulting sequences of residuals, and a resulting score is computed over time. The proposed approach is applied to model and predict a set of temperatures related to a marine propulsion system, anticipating anomalies and changes in operating conditions. It is demonstrated that deep evolutionary modeling results are quite satisfactory for prognostics and obtained physical models are practical and easy to understand.Item Deep learning for brain age estimation: A systematic review(2023-08) Tanveer, M.; Ganaie, M. A.; Beheshti, Iman; Goel, Tripti; Ahmad, Nehal; Lai, Kuan Ting; Huang, Kaizhu; Zhang, Yu Dong; Del Ser, Javier; Lin, Chin Teng; IAOver the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated with the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required to elicit accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models.Item Deep Learning for Road Traffic Forecasting: Does it Make a Difference?(2022-07-01) Manibardo, Eric L.; Lana, Ibai; Ser, Javier Del; IADeep Learning methods have been proven to be flexible to model complex phenomena. This has also been the case of Intelligent Transportation Systems, in which several areas such as vehicular perception and traffic analysis have widely embraced Deep Learning as a core modeling technology. Particularly in short-term traffic forecasting, the capability of Deep Learning to deliver good results has generated a prevalent inertia towards using Deep Learning models, without examining in depth their benefits and downsides. This paper focuses on critically analyzing the state of the art in what refers to the use of Deep Learning for this particular Intelligent Transportation Systems research area. To this end, we elaborate on the findings distilled from a review of publications from recent years, based on two taxonomic criteria. A posterior critical analysis is held to formulate questions and trigger a necessary debate about the issues of Deep Learning for traffic forecasting. The study is completed with a benchmark of diverse short-term traffic forecasting methods over traffic datasets of different nature, aimed to cover a wide spectrum of possible scenarios. Our experimentation reveals that Deep Learning could not be the best modeling technique for every case, which unveils some caveats unconsidered to date that should be addressed by the community in prospective studies. These insights reveal new challenges and research opportunities in road traffic forecasting, which are enumerated and discussed thoroughly, with the intention of inspiring and guiding future research efforts in this field.Item Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles(2019-02) Dendaluce Jahnke, Martin; Cosco, Francesco; Novickis, Rihards; Pérez Rastelli, Joshué; Gomez-Garay, Vicente; Tecnalia Research & Innovation; CCAMThe combination of machine learning and heterogeneous embedded platforms enables new potential for developing sophisticated control concepts which are applicable to the field of vehicle dynamics and ADAS. This interdisciplinary work provides enabler solutions -ultimately implementing fast predictions using neural networks (NNs) on field programmable gate arrays (FPGAs) and graphical processing units (GPUs)- while applying them to a challenging application: Torque Vectoring on a multi-electric-motor vehicle for enhanced vehicle dynamics. The foundation motivating this work is provided by discussing multiple domains of the technological context as well as the constraints related to the automotive field, which contrast with the attractiveness of exploiting the capabilities of new embedded platforms to apply advanced control algorithms for complex control problems. In this particular case we target enhanced vehicle dynamics on a multi-motor electric vehicle benefiting from the greater degrees of freedom and controllability offered by such powertrains. Considering the constraints of the application and the implications of the selected multivariable optimization challenge, we propose a NN to provide batch predictions for real-time optimization. This leads to the major contribution of this work: efficient NN implementations on two intrinsically parallel embedded platforms, a GPU and a FPGA, following an analysis of theoretical and practical implications of their different operating paradigms, in order to efficiently harness their computing potential while gaining insight into their peculiarities. The achieved results exceed the expectations and additionally provide a representative illustration of the strengths and weaknesses of each kind of platform. Consequently, having shown the applicability of the proposed solutions, this work contributes valuable enablers also for further developments following similar fundamental principles.Item Ensemble forecaster based on the combination of time-frequency analysis and machine learning strategies for very short-term wind speed prediction(2023-01-01) Rodríguez, Fermín; Alonso-Pérez, Sandra; Sánchez-Guardamino, Ignacio; Galarza, Ainhoa; Tecnalia Research & Innovation; DIGITAL ENERGYBased on the predictions of fossil fuels depletion in the following years, as well as their negative impact due to generated exhaust fumes, eco-friendly generators, and more specifically wind generators, have arisen as a solution for the electric demand challenge. Wind energy consists in extracting energy from wind speed, and because of the uncertain and intermittent behaviour of this meteorological parameter, wind turbines output power cannot be optimally exploited. Although the vast majority of the research in wind speed forecasting field has consisted in the purpose of novel algorithms, these studies have not made a pre-processing step of the data in order to try to extract the maximum information from databases. Therefore, the goal of this paper consists in analysing whether the combination of time-frequency decomposition of wind speed data with different machine learning algorithms can increase the accuracy of current wind speed predictions for 10 min ahead. Obtained error metrics demonstrated that the deviation of developed wind speed forecaster was lower than 0.1% in 62% of the validation database. In addition, the root mean square error of the final forecaster was 0.34 m/s. This means an accuracy increase of 51.5% if the result is compared with benchmark model's results.Item From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability: A prescription of functional requirements for model actionability(2021-02-05) Laña, Ibai; Sanchez-Medina, Javier J.; Vlahogianni, Eleni I.; Ser, Javier Del; IAAdvances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a “story” intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infras-tructure, vehicles or the travelers’ personal devices act as sources of data flows that are eventually fed into software running on automatic devices, actuators or control systems producing, in turn, complex information flows among users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded in this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrin-sic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the ever-changing phenomenon scenario underly-ing the majority of ITS applications. Finally, we provide a prospect of current research lines within Data Science that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.Item Handbook of re-engineering software intensive systems into software product lines(Springer International Publishing, 2022-11-22) Lopez-Herrejon, Roberto E.; Martinez, Jabier; Assunção, Wesley Klewerton Guez; Ziadi, Tewfik; Acher, Mathieu; Vergilio, Silvia; SWTThis handbook distils the wealth of expertise and knowledge from a large community of researchers and industrial practitioners in Software Product Lines (SPLs) gained through extensive and rigorous theoretical, empirical, and applied research. It is a timely compilation of well-established and cutting-edge approaches that can be leveraged by those facing the prevailing and daunting challenge of re-engineering their systems into SPLs. The selection of chapters provides readers with a wide and diverse perspective that reflects the complementary and varied expertise of the chapter authors. This perspective covers the re-engineering processes, from planning to execution. SPLs are families of systems that share common assets, allowing a disciplined software reuse. The adoption of SPL practices has shown to enable significant technical and economic benefits for the companies that employ them. However, successful SPLs rarely start from scratch, but instead, they usually start from a set of existing systems that must undergo well-defined re-engineering processes to unleash new levels of productivity and competitiveness. Practitioners will benefit from the lessons learned by the community, captured in the array of methodological and technological alternatives presented in the chapters of the handbook, and will gain the confidence for undertaking their own re-engineering challenges. Researchers and educators will find a valuable single-entry point to quickly become familiar with the state-of-the-art on the topic and the open research opportunities; including undergraduate, graduate students, and R&D engineers who want to have a comprehensive understanding of techniques in reverse engineering and re-engineering of variability-rich software systems.Item Influence of statistical feature normalisation methods on K-Nearest Neighbours and K-Means in the context of industry 4.0(2022-05) Niño-Adan, Iratxe; Landa-Torres, Itziar; Portillo, Eva; Manjarres, Diana; Tecnalia Research & Innovation; IANormalisation is a preprocessing technique widely employed in Machine Learning (ML)-based solutions for industry to equalise the features’ contribution. However, few researchers have analysed the normalisation effect and its implications on the ML algorithm performance, especially on Euclidean distance-based algorithms, such as the well-known K-Nearest Neighbours and K-means. In this sense, this paper formally analyses the effect of normalisation yielding results significantly far from the state-of-the-art traditional claims. In particular, this paper shows that normalisation does not equalise the contribution of the features, with the consequent impact on the performance of the learning process for a particular problem. More concretely, this demonstration is made on K-Nearest Neighbours and K-means Euclidean distance-based ML algorithms. This paper concludes that normalisation can be viewed as an unsupervised Feature Weighting method. In this context, a new metric (Normalisation weight) for measuring the impact of normalisation on the features is presented. Likewise, an analysis of the normalisation effect on the Euclidean distance is conducted and a new metric referred to as Proportional influence that measures the features influence on the Euclidean distance is proposed. Both metrics enable the automatic selection of the most appropriate normalisation method for a particular engineering problem, which can significantly improve both the computational cost and classification performance of K-Nearest Neighbours and K-means algorithms. The analytical conclusions are validated on well-known datasets from the UCI repository and a real-life application from the refinery industry.Item An IoT−based system that aids learning from human behavior: A potential application for the care of the elderly: A potential application for the care of the elderly(2017-10-04) Saralegui, Unai; Antón, Miguel Ángel; Ordieres-Meré, Joaquín; Tecnalia Research & Innovation; DIGITALIZACIÓN Y AUTOMATIZACIÓN DE LA CONSTRUCCIÓNThe goal of this paper is to describe the way of taking advantage of the non-intrusive indoor air quality monitoring system by using data oriented modeling technologies to determine specific human behaviors. The specific goal is to determine when a human presence occurs in a specific room, while the objective is to extend the use of the existing indoor air quality monitoring system to provide a higher level aspect of the house usage. Different models have been trained by means of machine learning algorithms using the available temperature, relative humidity and CO2 levels to determine binary occupation. The paper will discuss the overall acceptable quality provided by those classifiers when operating over new data not previously seen. Therefore, a recommendation on how to proceed is provided, as well as the confidence level regarding the new created knowledge. Such knowledge could bring additional opportunities in the care of the elderly for specific diseases that are usually accompanied by changes in patterns of behavior.Item Kernel density-based pattern classification in blind fasteners installation(Springer Verlag, 2017) Diez-Olivan, Alberto; Penalva, Mariluz; Veiga, Fernando; Deitert, Lutz; Sanz, Ricardo; Sierra, Basilio; Quintian, Hector; Corchado, Emilio; [surname]Martinez de Pison, Francisco Javier; Urraca, Ruben; Tecnalia Research & Innovation; FABRIC_INTELIn this work we introduce a kernel density-based pattern classification approach for the automatic identification of behavioral patterns from monitoring data related to blind fasteners installation. High density regions are estimated from feature space to establish behavioral patterns, automatically removing outliers and noisy instances in an iterative process. First the kernel density estimator is applied on the fastener features representing the quality of the installation. Then the behavioral patterns are identified from resulting high density regions, also considering the proximity between instances. Patterns are computed as the average of related monitoring torque-rotation diagrams. New fastening installations can be thus automatically classified in an online fashion. In order to show the validity of the approach, experiments have been conducted on real fastening data. Experimental results show an accurate pattern identification and classification approach, obtaining a global accuracy over 78% and improving current detection capabilities and existing evaluation systems.Item Kernel-based support vector machines for automated health status assessment in monitoring sensor data(2018-03-01) Diez-Olivan, Alberto; Pagan, Jose A.; Khoa, Nguyen Lu Dang; Sanz, Ricardo; Sierra, Basilio; Tecnalia Research & InnovationThis paper presents a novel algorithm to assess the health status in monitoring sensor data using a kernel-based support vector machine (SVM) approach. Today, accurate fault prediction is a key issue raised by maintenance. In particular, automatically modelling the normal behaviour from condition monitoring data is probably one of the most challenging problems, specially when there is limited information of real faults. To overcome this difficulty, a data-driven learning framework based on nonparametric density estimation for outlier detection and ν-SVM for normality modelling, with optimal bandwidth selection, is proposed. A health score based on the log-normalisation of the distance to the separating hyperplane is also provided. Experimental results obtained when analysing the propagation of a critical fault over time in a marine diesel engine demonstrate the validity of the algorithm. The predictions of normality models learned were compared to those of the k-nearest neighbours (kNN) method. Low false positive rates on healthy data and improved prediction capacities are achieved.Item Measuring the Confidence of Single-Point Traffic Forecasting Models: Techniques, Experimental Comparison, and Guidelines Toward Their Actionability(2024) Lana, Ibai; Olabarrieta, Ignacio; Ser, Javier Del; IAThe estimation of the amount of uncertainty featured by predictive machine learning models has acquired a great momentum in recent years. Uncertainty estimation provides the user with augmented information about the model’s confidence in its predicted outcome. Despite the inherent utility of this information for the trustworthiness of the user, there is a thin consensus around the different types of uncertainty that one can gauge in machine learning models and the suitability of different techniques that can be used to quantify the uncertainty of a specific model. This subject is mostly non existent within the traffic modeling domain, even though the measurement of the confidence associated to traffic forecasts can favor significantly their actionability in practical traffic management systems. This work aims to cover this lack of research by reviewing different techniques and metrics of uncertainty available in the literature, and by critically discussing how confidence levels computed for traffic forecasting models can be helpful for researchers and practitioners working in this research area. To shed light with empirical evidence, this critical discussion is further informed by experimental results produced by different uncertainty estimation techniques over real traffic data collected in Madrid (Spain), rendering a general overview of the benefits and caveats of every technique, how they can be compared to each other, and how the measured uncertainty decreases depending on the amount, quality and diversity of data used to produce the forecasts.Item Modelling Electricity Consumption During the COVID19 Pandemic: Datasets, Models, Results and a Research Agenda(2023-09-01) Khan, Zulfiqar Ahmad; Hussain, Tanveer; Ullah, Amin; Ullah, Waseem; Del Ser, Javier; Muhammad, Khan; Sajjad, Muhammad; Baik, Sung Wook; IAThe COVID19 pandemic has impacted the global economy, social activities, and Electricity Consumption (EC), affecting the performance of historical data-based Electricity Load Forecasting (ELF) algorithms. This study thoroughly analyses the pandemic's impact on these models and develop a hybrid model with better prediction accuracy using COVID19 data. Existing datasets are reviewed, and their limited generalization potential for the COVID19 period is highlighted. A dataset of 96 residential customers, comprising 36 and six months before and after the pandemic, is collected, posing significant challenges for current models. The proposed model employs convolutional layers for feature extraction, gated recurrent nets for temporal feature learning, and a self-attention module for feature selection, leading to better generalization for predicting EC patterns. Our proposed model outperforms existing models, as demonstrated by a detailed ablation study using our dataset. For instance, it achieves an average reduction of 0.56% & 3.46% in MSE, 1.5% & 5.07% in RMSE, and 11.81% & 13.19% in MAPE over the pre- and post-pandemic data, respectively. However, further research is required to address the varied nature of the data. These findings have significant implications for improving ELF algorithms during pandemics and other significant events that disrupt historical data patterns.