| Study programme competencies |
| Code | Study programme competences |
| A11 | Capacidad para aplicar métodos de análisis de datos para la creación de sistemas energéticos eficientes. |
| B3 | Poseer y comprender conocimientos que aporten una base u oportunidad de ser originales en el desarrollo y/o aplicación de ideas, a menudo en un contexto de investigación. |
| B6 | Buscar y seleccionar alternativas considerando las mejores soluciones posibles. |
| B14 | Aplicar conocimientos de ciencias y tecnologías avanzadas a la práctica profesional o investigadora de la eficiencia |
| C3 | Aplicar una metodología que fomente el aprendizaje y el trabajo autónomo. |
| Learning aims | |||
| Learning outcomes | Study programme competences | ||
| Demonstrate detailed understanding of the main methods of data mining. | BC3 |
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| Recognize problems that are amenable to energy optimization by using data mining techniques. | BC6 |
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| Propose solutions for improving energy efficiency in systems that have operating data provided by different data acquisition systems. | AJ11 |
CC3 |
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| Knowing tools for dimension reduction | BC14 |
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| Application of classification and regression techniques to data obtained by monitoring critical variables on energy efficiency | AJ11 |
BC14 |
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| Contents |
| Topic | Sub-topic |
| 1. Introduction to machine learning and data mining | 1.1. Preliminary concepts 1.2. Exploratory data analysis 1.3. Types of problems: classification, regression, clustering, anomaly detection, etc. 1.4. Types of learning: supervised, unsupervised, reinforcement, etc. |
| 2. Models for supervised and unsupervised classification of data | 2.1. Preliminary concepts 2.2. Main models: k-nearest neighbors, SVMs, clustering, etc. |
| 3. Regression/system identification models for estimation and prediction | 3.1. Preliminary concepts 3.2. Main models |
| 4. Data processing techniques | 4.1. Data preparation and standardization 4.2. Dimension reduction |
| 5. Experimental methodology and analysis of results | 5.1. Metrics for evaluating the models and techniques for unbiased estimate of the error 5.2. Model selection and analysis of results |
| 6. Statistical Quality Control | 6.1. Control graphs 6.2. Process capacity analysis |
| 7. Applications in Energy Efficiency | 7.1. Examples in forecasting 7.2. Examples for anomaly detection |
| Planning | ||||
| Methodologies / tests | Competencies | Ordinary class hours | Student’s personal work hours | Total hours |
| Guest lecture / keynote speech | B3 | 9 | 18 | 27 |
| Laboratory practice | A11 B14 | 12 | 10 | 22 |
| Supervised projects | B6 C3 | 0 | 22 | 22 |
| Objective test | B3 | 3 | 0 | 3 |
| Personalized attention | 1 | 0 | 1 | |
| (*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. | ||||
| Methodologies |
| Methodologies | Description |
| Guest lecture / keynote speech | Classroom activity used to establish the fundamental concepts of matter. It consists of the oral presentation complemented by the use of audiovisual/multimedia media and performing some questions to students in order to transmit knowledge and facilitate learning. |
| Laboratory practice | Development of practices in the computer lab. This will consist of case studies and examples. Besides the students will solve exercises posed by teachers. |
| Supervised projects | Performing work related to any of the topics on the agenda of the subject. Students will deliver them in electronic format, including a memory and a presentation that will have to expose the teacher. These works require the assistance of at least one personal tutoring for each group. |
| Objective test | Evaluation test to be held at the end of course in the corresponding official announcements. It will consist of a written test that will be necessary to respond to different theoretical and practical issues. |
| Personalized attention |
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| Assessment | |||
| Methodologies | Competencies | Description | Qualification |
| Supervised projects | B6 C3 | Autonomous individual or small group work. It will be necessary to deliver the materials (memory and presentation) in a timely manner as described in the statement. In addition, it will require oral presentation by all members of the working group, using for that presentation delivered. It is taken into account for the evaluation of this activity the memory, the presentation and also the answers to the teacher's questions during compulsory presentation. Omission of the presentation will be a grade of zero in this activity. | 30 |
| Objective test | B3 | Final test of matter consisting of conducting individual examination. This test will have questions and related theoretical concepts studied in lectures, laboratory practices or content of such practices tutored projects. | 60 |
| Laboratory practice | A11 B14 | It will consist of collecting all the exercises in the labs during the course. These exercises should be done in the time allotted to practical classes and will be delivered at the end of them. While performing these exercises, students can raise questions to the teacher or consult the materials it deems appropriate. Therefore, this activity will evaluate the daily work of the student in practical classes. | 10 |
| Assessment comments | |||
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In order to pass the course the student must meet the following requirements (score between 0 and 10 in all activities):
-Achieving a grade greater or equal than 3.5 in the objective test conducted at the end of the semester. Noteson activities: -All activities will have a unique opportunity for delivery during the academic year, except the final objective test that will have two official exam opportunities. |
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| Sources of information |
| Basic |
T. Agami Reddy (2011). Applied Data Analysis and Modeling for Energy Engineers and Scientists. Springer
Basilio Sierra Araujo (2006). Aprendizaje Automático: conceptos básicos y avanzados. Pearson Prentice Hall
Douglas Montgomery (2005). Introduction to Statistical Quality Control. John Wiley & Sons |
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| Complementary | |
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| Recommendations | |
| Subjects that it is recommended to have taken before |
| Subjects that are recommended to be taken simultaneously |
| Subjects that continue the syllabus |
| Other comments | |