| 1. |
- Abghari, Shahrooz, et al.
(author)
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Trend analysis to automatically identify heat program changes
- 2017
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In: Energy Procedia. - : Elsevier. ; , s. 407-415
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Conference paper (peer-reviewed)abstract
- The aim of this study is to improve the monitoring and controlling of heating systems located at customer buildings through the use of a decision support system. To achieve this, the proposed system applies a two-step classifier to detect manual changes of the temperature of the heating system. We apply data from the Swedish company NODA, active in energy optimization and services for energy efficiency, to train and test the suggested system. The decision support system is evaluated through an experiment and the results are validated by experts at NODA. The results show that the decision support system can detect changes within three days after their occurrence and only by considering daily average measurements.
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| 2. |
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| 3. |
- Frasheri, Mirgita, et al.
(author)
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Adaptive Autonomy in Wireless Sensor Networks
- 2020
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In: Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS. - 9781450375184 ; , s. 375-383
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Conference paper (peer-reviewed)abstract
- Moving nodes in a Mobile Wireless Sensor Network (MWSN) typically have two maintenance objectives: (i) extend the coverage of the network as long as possible to a target area, and (ii) extend the longevity of the network as much as possible. As nodes move and also route traffic in the network, their battery levels deplete differently for each node. Dead nodes lead to loss of connectivity and even to disengaging full parts of the network. Several reactive and rule-based approaches have been proposed to solve this issue by adapting redeployment to depleted nodes. However, in large networks a cooperative approach may increase performance by taking the evolution of node battery and traffic into account. In this paper, we present a hybrid agent-based architecture that addresses the problem of depleting nodes during the maintenance phase of a MWSN. Agents, each assigned to a node, collaborate and adapt their behaviour to their battery levels. The collaborative behavior is modeled through the willingness to interact abstraction, which defines when agents ask and give help to one another. Thus, depleting nodes may ask to be replaced by healthier counterparts and move to areas with less traffic or to a collection point. At the lower level, negotiations trigger a reactive navigation behaviour based on Social Potential Fields (SPF). It is shown that the proposed method improves coverage and extends network longevity in an environment without obstacles as compared to SPF alone.
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| 4. |
- García Martín, Eva, et al.
(author)
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Energy Efficiency in Data Stream Mining
- 2015
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In: ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. - New York, NY, USA : ACM Digital Library. - 9781450338547 ; , s. 1125-1132
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Conference paper (peer-reviewed)abstract
- Data mining algorithms are usually designed to optimize a trade-off between predictive accuracy and computational efficiency. This paper introduces energy consumption and energy efficiency as important factors to consider during data mining algorithm analysis and evaluation. We extended the CRISP (Cross Industry Standard Process for Data Mining) framework to include energy consumption analysis. Based on this framework, we conducted an experiment to illustrate how energy consumption and accuracy are affected when varying the parameters of the Very Fast Decision Tree (VFDT) algorithm. The results indicate that energy consumption can be reduced by up to 92.5% (557 J) while maintaining accuracy.
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| 5. |
- García Martín, Eva
(author)
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Energy Efficiency in Machine Learning : A position paper
- 2017
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In: 30th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2017, May 15–16, 2017, Karlskrona, Sweden. - Linköping : Linköping University Electronic Press. - 9789176854969 ; , s. 68-72
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Conference paper (peer-reviewed)abstract
- Machine learning algorithms are usually evaluated and developed in terms of predictive performance. Since these types of algorithms often run on large-scale data centers, they account for a significant share of the energy consumed in many countries. This position paper argues for the reasons why developing energy efficient machine learning algorithms is of great importance.
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| 6. |
- García Martín, Eva, et al.
(author)
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Hoeffding Trees with nmin adaptation
- 2018
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In: The 5th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2018). - : IEEE. - 9781538650905
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Conference paper (peer-reviewed)abstract
- Machine learning software accounts for a significant amount of energy consumed in data centers. These algorithms are usually optimized towards predictive performance, i.e. accuracy, and scalability. This is the case of data stream mining algorithms. Although these algorithms are adaptive to the incoming data, they have fixed parameters from the beginning of the execution. We have observed that having fixed parameters lead to unnecessary computations, thus making the algorithm energy inefficient.In this paper we present the nmin adaptation method for Hoeffding trees. This method adapts the value of the nmin pa- rameter, which significantly affects the energy consumption of the algorithm. The method reduces unnecessary computations and memory accesses, thus reducing the energy, while the accuracy is only marginally affected. We experimentally compared VFDT (Very Fast Decision Tree, the first Hoeffding tree algorithm) and CVFDT (Concept-adapting VFDT) with the VFDT-nmin (VFDT with nmin adaptation). The results show that VFDT-nmin consumes up to 27% less energy than the standard VFDT, and up to 92% less energy than CVFDT, trading off a few percent of accuracy in a few datasets.
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| 7. |
- García Martín, Eva, et al.
(author)
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How to Measure Energy Consumption in Machine Learning Algorithms
- 2019
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In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer. - 9783030134525 ; , s. 243-255
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Conference paper (peer-reviewed)abstract
- Machine learning algorithms are responsible for a significant amount of computations. These computations are increasing with the advancements in different machine learning fields. For example, fields such as deep learning require algorithms to run during weeks consuming vast amounts of energy. While there is a trend in optimizing machine learning algorithms for performance and energy consumption, still there is little knowledge on how to estimate an algorithm’s energy consumption. Currently, a straightforward cross-platform approach to estimate energy consumption for different types of algorithms does not exist. For that reason, well-known researchers in computer architecture have published extensive works on approaches to estimate the energy consumption. This study presents a survey of methods to estimate energy consumption, and maps them to specific machine learning scenarios. Finally, we illustrate our mapping suggestions with a case study, where we measure energy consumption in a big data stream mining scenario. Our ultimate goal is to bridge the current gap that exists to estimate energy consumption in machine learning scenarios.
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| 8. |
- García Martín, Eva, et al.
(author)
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How to Measure Energy Consumption in Machine Learning Algorithms
- 2018
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In: Green Data Mining, International Workshop on Energy Efficient Data Mining and Knowledge Discovery.
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Conference paper (peer-reviewed)abstract
- Machine learning algorithms are responsible for a significant amount of computations. These computations are increasing with the advancements in different machine learning fields. For example, fields such as deep learning require algorithms to run during weeks consuming vast amounts of energy. While there is a trend in optimizing machine learning algorithms for performance and energy consumption, still there is little knowledge on how to estimate an algorithm’s energy consumption. Currently, a straightforward cross-platform approach to estimate energy consumption for different types of algorithms does not exist. For that reason, well-known researchers in computer architecture have published extensive works on approaches to estimate the energy consumption. This study presents a survey of methods to estimate energy consumption, and maps them to specific machine learning scenarios. Finally, we illustrate our mapping suggestions with a case study, where we measure energy consumption in a big data stream mining scenario. Our ultimate goal is to bridge the current gap that exists to estimate energy consumption in machine learning scenarios.
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| 9. |
- García Martín, Eva, et al.
(author)
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Identification of Energy Hotspots : A Case Study of the Very Fast Decision Tree
- 2017
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In: GPC 2017: Green, Pervasive, and Cloud Computing. - Cham, Switzerland : Springer. - 9783319571850 - 9783319571867 ; , s. 267-281
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Conference paper (peer-reviewed)abstract
- Large-scale data centers account for a significant share of the energy consumption in many countries. Machine learning technology requires intensive workloads and thus drives requirements for lots of power and cooling capacity in data centers. It is time to explore green machine learning. The aim of this paper is to profile a machine learning algorithm with respect to its energy consumption and to determine the causes behind this consumption. The first scalable machine learning algorithm able to handle large volumes of streaming data is the Very Fast Decision Tree (VFDT), which outputs competitive results in comparison to algorithms that analyze data from static datasets. Our objectives are to: (i) establish a methodology that profiles the energy consumption of decision trees at the function level, (ii) apply this methodology in an experiment to obtain the energy consumption of the VFDT, (iii) conduct a fine-grained analysis of the functions that consume most of the energy, providing an understanding of that consumption, (iv) analyze how different parameter settings can significantly reduce the energy consumption. The results show that by addressing the most energy intensive part of the VFDT, the energy consumption can be reduced up to a 74.3%.
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| 10. |
- García Martín, Eva, et al.
(author)
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Is it ethical to avoid error analysis?
- 2017
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Conference paper (peer-reviewed)abstract
- Machine learning algorithms tend to create more accurate models with the availability of large datasets. In some cases, highly accurate models can hide the presence of bias in the data. There are several studies published that tackle the development of discriminatory-aware machine learning algorithms. We center on the further evaluation of machine learning models by doing error analysis, to understand under what conditions the model is not working as expected. We focus on the ethical implications of avoiding error analysis, from a falsification of results and discrimination perspective. Finally, we show different ways to approach error analysis in non-interpretable machine learning algorithms such as deep learning.
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